# Cheers - llms-full.txt ## Version 1.4 (2026-10-26) > Cheers is Y Combinator-backed, done-for-you local visibility for established service companies, especially multi-location brands and PE-backed home-service portfolios. Cheers tracks where each market appears across Google and AI, traces missed recommendations to reviews, profiles, sources, and pages, then helps fix what is holding each market back. This file contains the full text of selected educational articles from the Cheers GEO Academy. For a shorter summary, see /llms.txt. ## Company - **Website:** https://www.cheers.tech - **Contact:** info@cheers.tech | +1 (415) 340-1289 - **Founded:** 2024 | **Backed by:** Y Combinator (S24) - **Founders:** Dylan Allen-Arnegård (CEO), Amadeus Peterson (CTO) ## Solutions - [HVAC](https://www.cheers.tech/solutions/hvac): Seasonal demand, emergency searches, installs, maintenance visits, technicians, comfort advisors, and branch-level reviews. - [Plumbing](https://www.cheers.tech/solutions/plumbing): Emergency repairs, water heaters, drain clearing, leak detection, local trust, service-area reviews, and technician attribution. - [Roofing](https://www.cheers.tech/solutions/roofing): High-ticket projects, final walkthroughs, storm markets, estimator attribution, branch reviews, and AI-cited credibility. - [Franchise Brands](https://www.cheers.tech/solutions/franchise-brands): Brand-wide consistency for salons, med spas, wellness groups, hospitality, and local-service franchises where every location has to earn trust. - [Energy & Smart Home](https://www.cheers.tech/solutions/energy-smart-home): For solar, energy, smart home, and security brands with field sales, installers, technicians, local teams, and high-consideration trust moments. - [Home Service Roll-Ups](https://www.cheers.tech/solutions/home-services): Portfolio-wide reporting for HVAC, plumbing, electrical, pest, garage door, restoration, and franchise service brands. - [Review Generation](https://www.cheers.tech/solutions/review-generation): Always-on review capture with NFC badges, QR flows, employee attribution, tap-to-review reporting, and location scorecards. - [Local Content Generation](https://www.cheers.tech/solutions/local-content-generation): Draft local articles, service pages, and FAQs from real reviews, services, location details, and customer questions. - [Website Optimization](https://www.cheers.tech/solutions/website-optimization): Audit schema, crawler files, page structure, content depth, performance, and service-location coverage so AI and search systems can parse the site. - [AI Visibility Platform](https://www.cheers.tech/solutions/ai-visibility-platform): Prompt tracking, recommendation share, cited-source analysis, competitor mentions, and the local work needed to improve. - [Multi-Location Local SEO](https://www.cheers.tech/solutions/multi-location-local-seo): Location-level visibility across reviews, Google profiles, citations, competitors, service areas, local pages, and operator actions. - [Generative Engine Optimization](https://www.cheers.tech/solutions/generative-engine-optimization-agency): The done-for-you alternative to hiring a generative engine optimization agency: prompts tracked by market, evidence gaps worked every week. - [Multi-Location SEO](https://www.cheers.tech/solutions/multi-location-seo): Multi location SEO run as a managed program: every market measured, worked, and reported on its own, not rolled into one domain number. - [Franchise SEO](https://www.cheers.tech/solutions/franchise-seo): Franchise SEO services that keep every unit findable: profiles, local pages, listings, reviews, and AI answers handled without stepping on the franchisor's brand rules. - [Review Management Services](https://www.cheers.tech/solutions/review-management-services): Done-for-you review management services: capture at the service moment, employee attribution, replies handled, and velocity reported by location. ## Comparisons - [Cheers vs Birdeye](https://www.cheers.tech/compare/cheers-vs-birdeye): An honest Cheers and Birdeye comparison for multi-location service brands: suite breadth versus done-for-you local visibility work, with sources. Reviewed 2026-09-23. - [Cheers vs Podium](https://www.cheers.tech/compare/cheers-vs-podium): Podium converts inbound leads. Cheers wins the local and AI recommendation before the lead exists. An honest comparison with sources and wrong-fit cases. Reviewed 2026-09-23. - [Cheers vs SOCi](https://www.cheers.tech/compare/cheers-vs-soci): SOCi covers localized social, ads, and reputation for enterprise brands. Cheers runs a narrower done-for-you local visibility program. Honest comparison. Reviewed 2026-09-02. - [Cheers vs an SEO agency](https://www.cheers.tech/compare/cheers-vs-seo-agency): Compare an SEO agency with Cheers for local visibility, website work, and ongoing execution. See what each scope should cover before choosing. Reviewed 2026-09-02. - [Cheers vs LocalClarity](https://www.cheers.tech/compare/cheers-vs-localclarity): LocalClarity publishes per-location pricing for listings and review software you operate. Cheers does the work instead. Honest comparison with sources. Reviewed 2026-09-23. ## Case Studies - [Elite Rooter Case Study](https://www.cheers.tech/proof/elite-rooter): Cheers rebuilt Elite Rooter’s local pages, upgraded its plumbing guides, and brought proof and live customer reviews into the website. Technician badges keep that reputation growing in the field. - [True Plumbing Case Study](https://www.cheers.tech/proof/true-plumbing): Searchable plumbing guides, clearer service and city pages, and live customer proof now work together on True Plumbing’s rebuilt website. - [Action Furnace Case Study](https://www.cheers.tech/proof/action-furnace): Action Furnace uses Cheers to recognize the technicians behind its customer reviews, with a review program across three branches and local service content for the next homeowner. - [Hello Sugar Case Study](https://www.cheers.tech/proof/hello-sugar): Hello Sugar brings reviews and employee recognition into one program across its salon network. Its latest Google profile snapshot holds more than 70,000 customer reviews. - [Sierra Air Conditioning & Plumbing Case Study](https://www.cheers.tech/proof/sierra-cooling): Sierra Air Conditioning & Plumbing builds its Las Vegas reputation one service visit at a time. Cheers connects thousands of customer reviews to the people doing the work. ## Key Articles (full text below) - [AI Search for Local Businesses: What Google AI Mode Shows](https://www.cheers.tech/geo-academy/how-local-businesses-can-show-up-in-google-ai-search) - [What Sources Do AI Search Engines Cite?](https://www.cheers.tech/geo-academy/ai-search-engine-source-differences) - [AI Visibility Score: What's Good? 2026 Benchmarks](https://www.cheers.tech/geo-academy/what-is-a-good-ai-visibility-score) - [Best AI Visibility Tools for Local Businesses](https://www.cheers.tech/geo-academy/best-ai-visibility-tools-local-businesses) - [How Much Does AI Visibility Software Cost? 2026 Prices](https://www.cheers.tech/geo-academy/how-much-does-ai-visibility-software-cost) - [Best Local SEO Agency Alternatives for Home Services](https://www.cheers.tech/geo-academy/local-seo-agency-alternative-multi-location-home-services) - [Why Does AI Recommend My Competitor?](https://www.cheers.tech/geo-academy/ai-recommends-competitors) - [Where Does ChatGPT Get Its Information? Local Data 2026](https://www.cheers.tech/geo-academy/what-sources-does-chatgpt-use) - [What Is Query Fan-Out in Google AI Mode?](https://www.cheers.tech/geo-academy/what-is-query-fan-out-google-ai-mode) - [What Should Location Pages Include for AI Search? 2026 Data](https://www.cheers.tech/geo-academy/what-should-location-pages-include-for-ai-search) - [Do Google Reviews Affect AI Recommendations?](https://www.cheers.tech/geo-academy/reviews-and-geo) - [What Is GEO? Generative Engine Optimization Explained](https://www.cheers.tech/geo-academy/what-is-geo) --- ## Elite Rooter Case Study URL: https://www.cheers.tech/proof/elite-rooter Industry: Plumbing & Home Services Published: 2026-06-03 Modified: 2026-09-07 Proof window: Since each location joined Cheers, beginning September 2025, through September 7, 2026. Summary: Explore Elite Rooter’s upgraded local website: 249 location routes standardized, a searchable 90-guide blog, and live customer proof, backed by 1,236 team-attributed Google reviews. Elite Rooter’s website now brings local plumbing services, useful guides, and customer proof into the same experience. Cheers standardized 249 location routes and upgraded how visitors find, read, and act on the company’s content. The work connects with the field team, where 1,236 Google reviews have earned employee credit since each location joined Cheers. That reputation now has a place throughout the website. Proof metrics: - 1,236 Team-attributed Google reviews: Cheers credits 1,236 distinct Google reviews to Elite Rooter employees after their respective locations joined the program. Definition: Cheers credits 1,236 distinct Google reviews to Elite Rooter employees after their respective locations joined the program. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: Location-specific start dates, September 2025 onward, through September 7, 2026. Meaning: Employee attribution includes direct interactions, name matching, manual credits, and existing card assignments. This is the retained credit record in Cheers, rather than a current public-profile total. - 2,313 Google reviews received: Elite Rooter received 2,313 Google reviews across its connected locations after each joined Cheers. Definition: Elite Rooter received 2,313 Google reviews across its connected locations after each joined Cheers. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: Location-specific start dates through September 7, 2026. Meaning: Reviews received during the relationship across 13 locations; team-attributed reviews form part of this total. - 13 Google profiles: Cheers connects the local review program across 13 Elite Rooter Google profiles. Definition: Cheers connects the local review program across 13 Elite Rooter Google profiles. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: Account scope on September 7, 2026. Meaning: Cheers connects the local review program across 13 Elite Rooter Google profiles. Website work: - Homepage: https://www.eliterooter.com/. June 2026 source replay and September 7 live website, at the same 1440 × 1200 viewport. People and proof up front: The current opening brings the technician, service choices, and customer trust signals into the first part of the page. A clear next step: Calling and scheduling stay visible as a customer looks through the site, including on a phone. - Local page: https://www.eliterooter.com/location/tampa/. Matched opening views of the Tampa page, at 1440 × 720. Earlier view reconstructs preserved source HTML; current view captures production. The right market: A customer looking for a plumber in Tampa lands on a page for Tampa, with a local contact path and relevant service information. A consistent experience: The local page carries the website’s service and contact structure into the market where the work happens. - On a phone: https://www.eliterooter.com/. Same 390 × 1000 phone viewport. The earlier site is a source replay; the current site is a live capture. Built around the small screen: The technician image, service message, and primary actions fit the phone layout without relying on the desktop composition. Help stays within reach: The mobile dock keeps call and scheduling actions within reach as a customer explores services. Website upgrades: - A blog customers can actually search.: The upgraded blog brings 90 guides into a searchable library with topic filters and article-specific images. Visitors can find the plumbing problem they’re dealing with instead of paging through an undifferentiated archive. See https://www.eliterooter.com/blog/ - Useful answers, easier to read.: Updated guides use clear sections, decision tables, and relevant next steps. The water-heater guide helps homeowners compare repair and replacement, with a contact path alongside the article. See https://www.eliterooter.com/water-heater-replacement-cost/ - Proof before the first call.: A visible proof bar brings review totals, third-party trust signals, and licensing information together beneath the homepage opening. Customers can check the company while deciding which service they need. See https://www.eliterooter.com/ - Customer reviews that stay current.: The website displays Google reviews with branch names, dates, and links back to the source. Cheers supplies the review feed and shows when the profile totals last synced. See https://www.eliterooter.com/ - The right service, in the right market.: The local rebuild standardized 249 routes, including 152 city-service pages. A Tampa drain-cleaning customer gets a page about that service in Tampa, with local contact options and relevant customer proof. See https://www.eliterooter.com/location/tampa/drain-cleaning/ Ongoing work: - Website rebuild: Give every market a useful place to land. The current website includes 250 local pages. Market hubs and city-service pages connect customers with relevant plumbing information and the right contact path. - Customer experience: Put technicians and customer trust up front. A technician-led homepage, mobile layouts, and a sitewide trust strip bring the people doing the work into the website. Calling and scheduling stay close at hand. - Ongoing local work: Keep building after launch. Searchable location and blog directories help visitors find the right market or topic. New plumbing guides answer specific questions, including Phoenix maintenance and sewer-belly issues. Results: Elite’s local website and field review program now work together: useful pages explain the service, live proof shows the customer experience, and the team gets credit for the work. Make local expertise visible.: Searchable guides and market-specific service pages give visitors something useful to read. The site carries that context into service choices and contact paths. Keep the reputation growing.: Technician badges, employee attribution, and Elite’s review competitions support the field program. Cheers has credited 1,236 Google reviews to the team since the locations joined. Website exhibits and program totals checked September 7, 2026. Sources and counting methods are below. Badge attribution: Cheers has credited 1,236 Google reviews to Elite Rooter team members since their locations joined. Of those, 1,189 awarded five stars. Reviews give the company a way to recognize great work and give technicians something worth competing for. --- ## True Plumbing Case Study URL: https://www.cheers.tech/proof/true-plumbing Industry: Plumbing · Vancouver, Washington Published: 2026-09-07 Modified: 2026-09-07 Proof window: June 15–September 6, 2026, compared with March 23–June 14, 2026. Lifetime attribution read September 7. Summary: See True Plumbing’s website rebuild: searchable guides, local and service pages, a proof bar, live Google reviews, and a service-request flow, alongside 2.5× the prior Google review pace. Cheers rebuilt True Plumbing’s website around the questions homeowners bring to it: what service they need, whether the team covers their area, and why they should trust the company. The work reaches the guides, location pages, and service-request experience. The badge program strengthens the reputation behind those pages. True Plumbing received 112 Google reviews during June 15–September 6, compared with 45 in the preceding 84 days, while Cheers brought current reviews into the website. Proof metrics: - 2.5× Google review pace: 112 new Google reviews in the 84-day period beginning June 15, compared with 45 in the preceding 84 days; 112 ÷ 45 = 2.49. Definition: 112 new Google reviews in the 84-day period beginning June 15, compared with 45 in the preceding 84 days; 112 ÷ 45 = 2.49. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: June 15–September 6 vs. March 23–June 14, 2026 (Pacific time). Meaning: Equal-length before-and-after periods for the same connected location. The ratio describes observed review pace during the program. - 112 New Google reviews: True Plumbing received 112 Google reviews during June 15–September 6, the 84-day period beginning on its connected location’s setup date. Definition: True Plumbing received 112 Google reviews during June 15–September 6, the 84-day period beginning on its connected location’s setup date. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: June 15–September 6, 2026 (Pacific time). Meaning: True Plumbing received 112 Google reviews during June 15–September 6, the 84-day period beginning on its connected location’s setup date. - 94 Team-attributed Google reviews: Cheers credits 94 Google reviews to True Plumbing team members. All are dated after the connected location joined. Definition: Cheers credits 94 Google reviews to True Plumbing team members. All are dated after the connected location joined. Source: Cheers review and employee-attribution records. Observed: 2026-09-07. Window: June 29–September 6, 2026; read September 7. Meaning: 60 direct-interaction credits, 25 employee-name matches, and 9 manual credits. Each review counts once. Website work: - Homepage: https://www.true-plumbing.com/. Earlier homepage captured September 7 and current live homepage captured October 6, 2026, both at 1728 × 941. The current view includes the customer rating and trust badges below the hero. A clearer service path: Prominent service navigation and a primary request button give visitors a direct next step. The company’s familiar headline and van remain. Trust before the first call: Customer reviews, service-area information, and licensing details help visitors check the company before they get in touch. - Repiping service: https://www.true-plumbing.com/vancouver-plumbing-repipe/. The same repiping page before and after the rebuild. Earlier website captured September 7 at 1728 × 941; current live website captured October 6, 2026 at 1280 × 697. Explain the work first: The current page starts with the repiping service and explains that the team inspects the piping before pricing the scope. Connect information to a request: The service explanation sits with visible contact actions and local trust information. Customers can read about the work and ask for help on the same page. Website upgrades: - A plumbing library you can search.: The upgraded blog organizes 21 guides by the problem a homeowner is trying to solve. Search and topic filters make advice on water heaters, leaks, repiping, and water quality easier to find. See https://www.true-plumbing.com/blog/ - Service pages that explain the work.: The repiping page explains how the team maps the plumbing, finds the source of the problem, and sets the scope. Homeowners can understand the process before requesting service. See https://www.true-plumbing.com/vancouver-plumbing-repipe/ - Give customers reasons to feel confident.: The homepage proof bar puts Google reviews, BBB accreditation, Angi, and licensing information in one place. Those trust signals now sit directly alongside the route into services and requests. See https://www.true-plumbing.com/#social-proof - Bring fresh Google reviews onto the site.: Cheers brings recent customer reviews into the website, alongside the profile rating, review count, and source link. A visible sync timestamp shows when the totals last refreshed. See https://www.true-plumbing.com/#reviews - Give each community a relevant page.: The site now has 16 local pages. Camas gets its own plumbing and water-treatment information, with review proof and direct call and service-request options. See https://www.true-plumbing.com/best-plumbers-near-me-camas-wa-98607/ Ongoing work: - July 2026: Rebuild the service pages. The website gained a clearer service structure and specific local pages. The repiping comparison shows the change at one of the same customer-facing URLs. - August 2026: Bring customer proof into the site. Homepage review and mobile social-proof work made recent customer feedback part of the website experience. Licensing information gained its own clear destination. - September 2026: Connect information with the next step. Plumbing guides gained navigation links, and service pages received further contact-path work. The appointment flow describes a service request that the office confirms. Results: Useful content helps homeowners understand the work. Current customer proof helps them choose a company. The rebuilt website brings both into the service-request experience. A clearer path to the right service.: Visitors can search plumbing guides, explore their local page, read how a service works, and request help. Review proof and credentials stay part of that experience. More customer voices to put to work.: True Plumbing received 112 Google reviews during June 15–September 6, compared with 45 in the preceding 84 days. That is about two and a half times the pace, with 94 Google reviews carrying team credit in Cheers. Website inventory checked September 7, 2026. Review comparison uses equal 84-day periods; sources and dates are below. Badge attribution: 94 Google reviews now connect to True Plumbing team members in Cheers. Together with the website rebuild, the badge program gives the company a way to earn customer feedback and put it in front of the next homeowner. --- ## Action Furnace Case Study URL: https://www.cheers.tech/proof/action-furnace Industry: HVAC Services Published: 2026-06-03 Modified: 2026-09-07 Proof window: November 19, 2025–September 7, 2026. Employee-credit count begins with the first card on December 8, 2025. Summary: Action Furnace has credited 1,223 Google reviews to its employee program and received 1,606 Google reviews since joining Cheers. A customer’s review can tell Action Furnace which technician made the visit a good one. Cheers connects that feedback to the employee program, so managers can recognize the person behind the praise. Since the card program began, 1,223 Google reviews have a recorded employee-card, tap, or review-link attribution. Across its three connected profiles, Action Furnace has received 1,606 Google reviews since joining Cheers. Give a great service visit a name. Action Furnace’s technicians already give homeowners something worth reviewing. Cheers connects those reviews to the employee program, giving managers a way to recognize the work behind the customer’s words. Since December 2025, 1,223 Google reviews have recorded employee attribution. Each review counts once, even when it connects through more than one interaction. Three branches. One view of the team. Action Furnace has 259 active employee cards in its review program. Managers can read what customers wrote and connect praise to technicians across the three branches. During Action Furnace’s time with Cheers, the three connected profiles received 1,606 Google reviews. Their September 7 profile snapshots total 13,650. Help the next homeowner before the visit. The work also reaches customers researching furnace care. Local service content answers practical questions and gives homeowners a route to the relevant service. A published furnace-maintenance guide brings together maintenance advice, customer feedback, and a tune-up contact path on Action Furnace’s website. Proof metrics: - 1,223 Card-attributed reviews: 1,223 distinct Google-ID-backed reviews have a card, tap, or review-link attribution and date to Action Furnace’s card program. Definition: 1,223 distinct Google-ID-backed reviews have a card, tap, or review-link attribution and date to Action Furnace’s card program. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: December 8, 2025–September 7, 2026. Meaning: Each distinct review counts once across overlapping attribution links. Employee credit includes more than direct badge taps. - 13,650 Active Google reviews: The September 7 Google snapshots for Action Furnace’s three connected profiles sum to 13,650 reviews. Definition: The September 7 Google snapshots for Action Furnace’s three connected profiles sum to 13,650 reviews. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: September 7, 2026. Meaning: The September 7 Google snapshots for Action Furnace’s three connected profiles sum to 13,650 reviews. - +1,606 Google reviews added: 1,606 Google-ID-backed reviews on the same three profiles date to Action Furnace’s time with Cheers. Definition: 1,606 Google-ID-backed reviews on the same three profiles date to Action Furnace’s time with Cheers. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: November 19, 2025–September 7, 2026. Meaning: Reviews received during the relationship, counted once on the fixed three-profile cohort. --- ## Hello Sugar Case Study URL: https://www.cheers.tech/proof/hello-sugar Industry: Beauty & Wellness Franchise Published: 2026-06-03 Modified: 2026-09-07 Proof window: February 3, 2025–September 7, 2026. September 7 profile snapshot covers 276 Google profiles. Summary: Hello Sugar’s September snapshot spans 276 Google profiles and 70,581 Google reviews, with 34,860 reviews dated since joining Cheers. A guest chooses a salon in her neighborhood. Hello Sugar gives each location a place in the same review program, while keeping the customer feedback connected to the salon and team who earned it. The September snapshot covers 276 Google profiles and 70,581 Google reviews. Of the Google-ID-backed reviews on those same profiles, 34,860 date to Hello Sugar’s time with Cheers. One program. Hundreds of local reputations. A first-time guest wants to know what people say about the salon she can actually book. Hello Sugar’s program keeps reviews connected to each location, while giving the brand a shared view across its network. Cheers brings employee cards and review tracking into that daily work. A guest can open the right salon’s review page with a tap after her appointment. 34,860 more customer voices. The profiles in this snapshot received 34,860 Google reviews during Hello Sugar’s time with Cheers. Each one adds another customer’s experience to the reputation a future guest sees. That work happens appointment by appointment. Cheers gives the team a consistent way to ask for feedback and connect recognition to the people doing the service. 6,910 reviews with employee credit. Cheers connects 6,910 Google reviews to Hello Sugar’s employee program since the company joined. With 891 active employee cards, managers can recognize the people behind the reviews across the salon network. The brand can keep a shared review workflow as the network grows, while each salon builds its own local reputation. Proof metrics: - 70,581 Active Google reviews: The September 7 Google profile snapshot contains 70,581 reviews across 276 Hello Sugar profiles. Definition: The September 7 Google profile snapshot contains 70,581 reviews across 276 Hello Sugar profiles. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: September 7, 2026 snapshot. Meaning: Public Google profile counts summed across the 276 profiles with a September 7 observation. - 34,860 Google reviews added: 34,860 Google-ID-backed reviews on the same 276 profiles date to Hello Sugar’s time with Cheers. Definition: 34,860 Google-ID-backed reviews on the same 276 profiles date to Hello Sugar’s time with Cheers. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: February 3, 2025–September 7, 2026. Meaning: Each review counts once on the fixed 276-profile cohort. This measures reviews received during the program. - 276 Google profiles in this snapshot: 276 Hello Sugar Google profiles have a September 7 snapshot in the connected account. Definition: 276 Hello Sugar Google profiles have a September 7 snapshot in the connected account. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: September 7, 2026. Meaning: 276 Hello Sugar Google profiles have a September 7 snapshot in the connected account. --- ## Sierra Air Conditioning & Plumbing Case Study URL: https://www.cheers.tech/proof/sierra-cooling Industry: HVAC & Plumbing Published: 2026-06-03 Modified: 2026-09-07 Proof window: September 18, 2024–September 7, 2026. Employee attribution begins with the card program on October 3, 2024. Summary: Sierra has added 5,301 Google reviews since joining Cheers. Its September 7 Google profile snapshot shows 7,782 reviews. When Sierra finishes an air-conditioning or plumbing job, the customer has something specific to say about the team. Cheers gives that feedback a place in the company’s review program and connects credit to employees. Sierra’s connected Google record includes 5,301 reviews dated since joining Cheers. By September 7, its public-profile snapshot had reached 7,782 reviews. Thousands of service visits, visible on Google. Sierra’s Google review record now includes 5,301 reviews since joining Cheers. Those customer experiences give the next Las Vegas homeowner more to read before choosing a company. The September 7 public-profile snapshot shows 7,782 reviews. The review program helps Sierra keep building a reputation around the work its team does every day. 2,203 reviews with a person behind them. Cheers connects 2,203 Google reviews to Sierra’s employee review program, which has 139 active cards. Customer praise becomes useful inside the company, where managers can recognize the people earning it. Employee cards give customers a direct route to leave feedback. Attribution keeps the review connected to the team, giving managers a clearer view of individual contributions. Make recognition part of the routine. A good review can give a technician credit long after the service visit ends. Cheers brings the review and its employee attribution together so managers can use real customer feedback in team recognition. Sierra’s reputation keeps building through the same work: complete the job, make the review request easy, and recognize the team behind the customer’s experience. Proof metrics: - 7,782 Active Google reviews: Sierra’s September 7 Google profile snapshot shows 7,782 reviews. Definition: Sierra’s September 7 Google profile snapshot shows 7,782 reviews. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: September 7, 2026. Meaning: Sierra’s September 7 Google profile snapshot shows 7,782 reviews. - +5,301 Google review growth: 5,301 Google-ID-backed reviews on Sierra’s connected profile date to the period since it joined Cheers. Definition: 5,301 Google-ID-backed reviews on Sierra’s connected profile date to the period since it joined Cheers. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: September 18, 2024–September 7, 2026. Meaning: Each review counts once on the same connected Google profile. This is the surviving review record for the program period. - 2,203 Team-attributed Google reviews: 2,203 distinct Google-ID-backed reviews have a card, tap, or review-link attribution and date to Sierra’s card program. Definition: 2,203 distinct Google-ID-backed reviews have a card, tap, or review-link attribution and date to Sierra’s card program. Source: Connected Google profile snapshots and Cheers records. Observed: 2026-09-07. Window: October 3, 2024–September 7, 2026. Meaning: Overlapping attribution links count as one review. Employee credit includes more than direct badge taps. --- ## AI Search for Local Businesses: What Google AI Mode Shows Slug: how-local-businesses-can-show-up-in-google-ai-search Category: Playbooks Published: 2026-05-19 Last updated: 2026-09-28 URL: https://www.cheers.tech/geo-academy/how-local-businesses-can-show-up-in-google-ai-search To show up in Google AI search, a local business has to look like the businesses Google AI Mode already names: rated 4.8 stars or higher, backed by a real review history, and linked from a Business Profile to a working website and booking page. In 9,435 Google AI Mode answers to 32 home-services questions in 100 US and Canadian metros, collected by Cheers between August 31 and September 20, 2026, only 3.7% of the answers that showed rated business cards named any business under 4.0 stars, while 23.5% named at least one business with fewer than 20 reviews. For a multi-location brand, that pattern puts a branch sitting at 4.2 stars further from the businesses Google AI Mode names than a new branch with 15 good reviews. Last verified: September 23, 2026. Important: Every number here describes the businesses Google AI Mode named, not a rule Google applies. Google publishes no local recommendation formula, so read each finding as an association in our sample. In this article, Google AI search means Google AI Mode, the conversational search mode, and every number comes from it. AI Overviews, the summaries at the top of regular results, are a separate feature that Google says "may use different models and techniques", so we keep our data claims to AI Mode. If you want a one-location starting point on the other engines, the free AI Visibility Grader (https://www.cheers.tech/visibility) checks a business across ChatGPT, Gemini and Perplexity. ## What Google says, and what it leaves out Google's guide to its AI features in Search is short on local detail and firm on one point: Our generative AI features on Google Search are rooted in our core Search ranking and quality systems. The same guide, last updated July 10, 2026, says a page must be indexed and eligible to show a snippet to appear in those features, that "you don't need to create new machine readable files, AI text files, markup, or Markdown", and that "using products like Merchant Center and Google Business Profiles can help your products and services be visible in both AI responses and other Google Search results." For a local business, that boils down to normal Search eligibility plus an accurate Business Profile. The guide sets no standard for ratings, review counts or booking links, and the answers themselves say a lot about all three. ## What Google AI Mode put in front of customers A Google AI Mode answer to a home-services question is, nine times in ten, a short list of business cards. It showed rated business cards in 90% of the answers we collected, and the share barely moved with the kind of question: 89.7% for "best company for this job" questions, 91.6% for emergencies, 90.0% for same-day requests, 90.9% for questions about reviews and fair pricing, and 93.3% for deals and quotes. A typical answer carried about four cards, and Google AI Mode showed a star rating and review count for 90% of the businesses it named. The businesses on those cards were strongly rated. Across 34,017 rated business appearances in 8,523 answers, the median rating was 4.9 stars, 83% were rated 4.8 or higher, and 1.0% were under 4.0. Review counts spread much wider: the median business showed 226 Google reviews, 8% had fewer than 20 and 21% had 1,000 or more. ## The rating floor is firmer than the review floor The chart at the top of this page puts those two distributions side by side at the answer level. Among the 8,523 answers that showed rated cards, 23.5% named at least one business with fewer than 20 reviews, 14.6% named one rated under 4.5 stars, and just 3.7% named one under 4.0. A thin review count did not keep a business off the list nearly as often as a weak rating did. That is useful for a new or acquired location: a branch with a short, strong review history was still being named. It is a warning for an established branch that has slid into the low 4s, because Google AI Mode rarely named businesses there. We can't tell whether Google screens them out or whether those businesses simply lose on other grounds, since we only see who was named. The review floor also moves by trade and country. In electrical answers, 40.8% named at least one business with fewer than 20 reviews, against 11.6% of HVAC answers, where the median named business showed 449 reviews. Canadian businesses showed fewer reviews than US ones: a median of 134 against 332. ## The first business named usually has the most reviews Order carried a signal the rating didn't. In Google AI Mode answers that named three or more rated businesses, the first business named had a median of 342 reviews, against 237 for the second and 195 for the third. The most-reviewed business came first 36% of the time, where chance would put it first 25% of the time. Average ratings barely moved from first place to third. That is a correlation across 7,873 answers, and review count also tracks how big and how established a business is. It still gives an operator a clear read: once the rating clears the high 4s, review depth is the clearest difference between the first name and the third. ## Almost a third of answers linked to a booking page The booking-link finding is the easiest to act on. In all, 31.2% of Google AI Mode answers (2,947 of 9,435) linked directly to a business's online booking or scheduling page hosted on field-service software. Housecall Pro pages appeared in 20.5% of answers, ServiceTitan in 10.6% and Jobber in 3.1%. ChatGPT and Gemini never linked one, and Perplexity did in 0.2% of answers. The share depends on the trade: 39.4% of plumbing answers, 38.9% of HVAC answers and 33.3% of electrical answers carried a booking link, against 1.8% of roofing answers, which fits a trade where the first step is usually an inspection or estimate rather than a booked service call. US answers carried one more often than Canadian answers (33.4% against 27.9%). Where do those links come from? Our best guess is the booking link on each business's Google Business Profile, but that is an inference we have not verified. What Google does document is how that link works. A Business Profile can carry up to 10 links for booking an appointment, and Google's business links policy sets the conditions: Local business links must lead to a dedicated landing page for your business. For businesses with multiple locations, action links must lead to a website for a specific location. The same policy says the business must be verified before it can add a link, that the link must let a customer complete the action it promises, and that Google "may remove any non-compliant links." For a franchise or roll-up, that rules out pointing every location at one corporate scheduler page. Each profile needs its own branch's booking page, and it needs to work. ## Emergency answers lead with 24/7 For emergency questions ("right now", "stopped working", "available today"), 92.8% of Google AI Mode answers mentioned 24/7 or after-hours availability, against 48.0% for "best company for this job" questions. That count includes any mention, generic advice included, so it shows what the answer talks about rather than why a business was picked. It still tells you where a wrong hours listing hurts most. If a location takes calls at night, its Business Profile hours, its website and the person answering the phone should all say so. If it doesn't, don't claim it. The emergency hours guide (https://www.cheers.tech/geo-academy/where-should-home-service-brands-publish-emergency-hours-ai-search) covers where to publish that promise. ## Google AI Mode reads Google and your website Google AI Mode's citations looked nothing like ChatGPT's. It cited Google's own properties in 95.5% of its answers and businesses' own websites in 92.5%, and those two groups took about 43% and 34% of all its citations. Only 38.3% of its answers cited any directory, review platform or "best of" list, against 84.2% for ChatGPT. When Google AI Mode linked to a contractor's website, 66.9% of those links went to the homepage and 13.5% to a location or city page. We think the homepage links come from the website button on the Business Profile, again an inference. If that is right, a multi-location brand whose profiles all point at the corporate homepage is sending Google AI Mode to a page that can't describe any one branch. Linking each Business Profile to its location page (https://www.cheers.tech/geo-academy/google-business-profile-link-to-location-page) fixes that. Service pages drew only 5.0% of Google AI Mode's business-website citations, against 26.5% on Perplexity, so build them for the job and the other engines, and don't expect them to carry Google AI Mode alone. The profile carries more weight on Google AI Mode than anywhere else we measured. Whether it is enough for AI visibility on its own (https://www.cheers.tech/geo-academy/is-google-business-profile-enough-for-ai-visibility) is a separate question, and the answer is no once ChatGPT is in the picture: ChatGPT cited Google's own properties in 0.1% of its answers. ## Ask for reviews the way Google allows A high rating on a real review history comes from asking every customer, not from filtering who gets asked. Google's contribution policy prohibits businesses that "selectively solicit positive reviews from customers", bans incentives for reviews, and lists "merchants requesting that staff solicit a certain number of reviews" as prohibited. Ask every customer the same neutral way, and let the rating be what it is. ## What to do this week Pull every location's Google star rating and review count into one sheet, then work down this list: - Flag every location under 4.5 stars, read its last 20 reviews, and fix the operational problem they describe, because more reviews won't lift a rating the service keeps earning. - Check the booking link on every Business Profile. It should open that location's own booking page on your field-service software, and a test booking should go through. - Compare each profile's hours with what the phone line actually does after 6 p.m., and remove any 24/7 claim the branch can't answer. - Point each profile's website link at the location page, and make sure that page names the services and areas the branch covers. - Train every technician and front-desk person to ask every customer for a Google review the same way. For the ChatGPT side of the same job, the sources, the Yelp cards and the directories, read how to get ChatGPT to recommend your business (https://www.cheers.tech/geo-academy/how-to-get-ai-to-recommend-my-business). The businesses in these answers also change from week to week, which is why AI recommends different businesses every time (https://www.cheers.tech/geo-academy/why-ai-recommends-different-businesses) you check. The 2026 AI local ranking factors (https://www.cheers.tech/geo-academy/ai-local-recommendation-factors-2026) put all four engines on one page. ## Methodology Every week, Cheers asks ChatGPT, Gemini, Perplexity and Google AI Mode the same 32 home-services questions in 100 metro areas across the United States and Canada, and records every business each engine names, with its star rating, review count and map position where the engine shows one, and every source it cites. This analysis covers the three weeks from August 31 to September 20, 2026: 34,742 usable answers (9,434 from ChatGPT, 9,435 from Google AI Mode, 9,364 from Gemini and 6,509 from Perplexity) across 60 US and 40 Canadian metros. We excluded empty or cut-off answers, most of them from a Perplexity collection outage. Each question was asked once per city per week, so week-to-week changes include normal answer-to-answer variation. Business names were extracted automatically; a hand check of 40 answers found the extraction caught about three in four of the businesses a reader would count, so counts are lower bounds. Star ratings and review counts are the figures each engine displayed; for ChatGPT and Gemini we captured them from September 18 and September 14 onward. Findings describe associations in this sample, not how any engine ranks businesses. For this article: ratings and review counts are what Google AI Mode displayed on its place cards, and it shows counts above 999 rounded (for example 2.0K). Its place cards hid business names in about 5% of answers. Booking links are citations to booking pages hosted on field-service platforms (Housecall Pro, ServiceTitan, Jobber and others). Directory, platform and page-type groups come from hand-built domain lists and URL rules; "business website" counts only domains we could match to a named business, so it is a lower bound. Trade figures quoted here come from trades with two or more questions. ## Sources Checked September 23, 2026. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 10, 2026. Source of the core-systems quote, the snippet eligibility rule, the quoted line on machine-readable files and the quoted line on Business Profiles. - AI features and your website (https://developers.google.com/search/docs/appearance/ai-features). Google Search Central, last updated December 10, 2025. Source of the quoted line that AI Mode and AI Overviews may use different models and techniques. - Manage your local business links (https://support.google.com/business/answer/6218037?hl=en). Google Business Profile Help, no date shown, checked September 23, 2026. Source of the up-to-10 booking links per category. - Business links policies and guidelines (https://support.google.com/business/answer/13769188?hl=en). Google Business Profile Help, no date shown, checked September 23, 2026. Source of the quoted location-specific landing page rule, the verification requirement, the complete-the-action rule and link removal. - Set up bookings through a provider (https://support.google.com/business/answer/7475773?hl=en). Google Business Profile Help, checked September 23, 2026. Background on Reserve with Google providers and adding your own booking link. - Prohibited and restricted content (https://support.google.com/contributionpolicy/answer/7400114?hl=en). Google Maps User Generated Content Policy Help, checked September 23, 2026. Source of the quoted selective-solicitation and staff-target rules and the incentive ban. - Cheers Market Baseline, August 31 to September 20, 2026: 9,435 Google AI Mode answers within 34,742 answers from four engines to 32 home-services questions in 100 US and Canadian metros, aggregate only. Research methodology (https://www.cheers.tech/research/methodology). Source of every card, rating, review, order, booking, 24/7, citation and page-type figure. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.* --- ## What Sources Do AI Search Engines Cite? Slug: ai-search-engine-source-differences Category: Research Published: 2026-03-20 Last updated: 2026-09-02 URL: https://www.cheers.tech/geo-academy/ai-search-engine-source-differences Four engines, four different source diets. In the 28 days ending September 2, 2026, the Cheers market baseline ran 32 home-services prompts across 100 metro areas and collected 356,019 citations. Gemini pointed at the contractor's own website in 80.7% of its citations. ChatGPT did that in 48.8%, and sent 44.7% to directories and review platforms instead. Same question, same metros, same week. That is the gap this article is about: the documentation tells you what each engine can do, and the citation counts tell you what it actually did. Last verified: September 2, 2026. That is the comparison local operators can defend from the engines' own documentation as of September 2, 2026. ## ChatGPT Search OpenAI's help article on searching the web, updated in late August 2026, says ChatGPT search rewrites your question into targeted queries and sometimes hands those to other search providers, sharing an approximate location derived from your IP address so that "restaurants near me" becomes something like "top restaurants San Francisco". It is explicit that nothing about the result is owed to you: ChatGPT ranks search results using multiple factors intended to help users find relevant, reliable information. Placement is not guaranteed. The same page names actual partners in one narrow case: for restaurant reservations, availability may come from OpenTable, Resy, or Yelp. Outside that, OpenAI does not promise that a Google Business Profile, a Yelp page, or any other directory feeds a local answer. It also tells site owners the one concrete eligibility step, which is allowing OAI-SearchBot to crawl the site. ## Gemini and Google AI Mode Gemini Apps and AI Mode are separate surfaces. Google's Gemini help page is direct about how thin the source trail can be: Not all responses include related links or sources. When links do appear, Google says they may include public websites, files you uploaded, or Workspace documents and email if you connected Workspace. A missing Sources button is not evidence that nothing was retrieved. For AI Mode, Google's Search guide says the feature retrieves from the core Search index and can issue related searches through query fan-out. The response shows supporting web links. Google's local guidance tells site owners to keep Business Profile information current. That supports treating the profile and the branch website as source truth. It does not establish a fixed citation weight for either one. ## Perplexity Perplexity searches the live web and attaches source links to the answer. Its source-label article, last updated August 7, 2026, describes three domain labels, Government, Academic, and Trusted, and then limits what they mean: A label describes the website as a whole, not any single article or claim on it. Perplexity also says most domains carry no label at all, and that the absence of one is not a negative judgment. For a plumbing company, no label is the normal state. The useful local audit is the answer's actual source list, because Perplexity publishes no rule that one review site or directory controls local recommendations. ## Microsoft Copilot Microsoft says Copilot can turn a prompt into a shorter Bing query, ground the response in Bing results, and show the exact query and sources. Bing says its index is built from crawled third-party pages and that business queries may return details such as store hours and location. For local visibility, make sure Bing can reach the correct branch page and that the public business facts do not conflict. Do not assume that a strong Google result automatically creates the same Copilot result. ## What the citation mix actually looks like Documentation says what an engine can do. Counting citations says what it did. Yext analyzed more than 6.8 million citations across 1.6 million responses in October 2025 and found the engines disagree about what a good source is. 52.15% of Gemini citations came from brand-owned websites. 48.73% of ChatGPT citations came from third-party sites such as Yelp, TripAdvisor, and MapQuest. On subjective queries, directory citations inside ChatGPT rose to 46.3%, and niche industry sources made up 24% of Perplexity's citations. The Cheers market baseline runs a narrower version of that test on home services only: 32 natural-language trade prompts, 100 metro service areas in the United States and Canada, four engines, 28 days ending September 2, 2026, 356,019 citations. Every cited domain is sorted into contractor sites, directories and review platforms, social and forums, media and vendors, or Google itself. Aggregate only, no customer named. The methodology (https://www.cheers.tech/research/methodology) and the wider cuts are in AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics). Gemini sent 80.7% of its citations to a contractor website and Perplexity 72.3%, so on those two surfaces a thin or unreachable location page costs the citation outright. ChatGPT split almost evenly, 48.8% contractor sites against 44.7% directories and review platforms, which means a stale Angi or BBB record is doing real work there. Google AI Mode is the only engine where google.com itself is a meaningful share, at 11.4%. The most cited platform domains across all 100 metros were google.com, expertise.com, angi.com, bbb.org, and reddit.com. That is a list worth checking your own records against before deciding a directory does not matter. For ChatGPT alone, where ChatGPT gets its information (https://www.cheers.tech/geo-academy/what-sources-does-chatgpt-use) shows the platform-by-platform split, and the Reddit analysis (https://www.cheers.tech/geo-academy/why-is-reddit-not-getting-cited-by-ai) shows which subreddits the engines actually cite. ## What I audit across all four I save the prompt, engine, location context, businesses named, pages cited, and time checked. Then I group the sources into the business website, platform-managed business records, review or directory pages, publishers, and other third-party evidence. The provider-tracking guide (https://www.cheers.tech/geo-academy/best-platforms-track-chatgpt-gemini-perplexity) covers the tools that preserve that record. The citation cleanup guide (https://www.cheers.tech/geo-academy/citations-stack-2025) covers conflicting branch facts once the source audit finds them. I do not tell a client to build a listing because a generic checklist says an engine likes it. I want to see that source appear for the actual service, market, and competitor set, or know that customers already depend on it. The Cheers AI Visibility Grader (https://www.cheers.tech/visibility) runs the first version of that check on one profile across ChatGPT, Gemini, and Perplexity. Crawler permissions and optional source maps are separate from citation measurement. What Is LLMs.txt? (https://www.cheers.tech/geo-academy/what-is-llms-txt) explains what that file can and cannot do. For the editorial decision that follows, see which content formats AI search engines cite (https://www.cheers.tech/geo-academy/which-content-formats-ai-search-engines-cite-most) and how to interpret the different study samples. ## Sources Every link below was opened and checked on September 2, 2026. - Searching the web with ChatGPT (https://help.openai.com/en/articles/9237897). OpenAI Help Center, updated August 2026. Search behavior, query rewriting, search partners, approximate location, reservation providers, ranking, and OAI-SearchBot eligibility. - View related sources in Gemini Apps (https://support.google.com/gemini/answer/14143489?hl=en). Google Gemini Apps Help. Which sources related links can include and when a response has none. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 2026. AI Mode retrieval, query fan-out, and business-detail guidance. - Understanding source labels (https://www.perplexity.ai/help-center/en/articles/20260806-understanding-source-labels). Perplexity Help Center, updated August 7, 2026. What the Government, Academic, and Trusted labels do and do not mean. - Practical tips for using Perplexity (https://www.perplexity.ai/help-center/en/articles/10352971-practical-tips-for-using-perplexity). Perplexity Help Center. Live-web search and source links. - How web search works in Microsoft 365 Copilot (https://support.microsoft.com/en-us/microsoft-365-copilot/how-web-search-works-in-microsoft-365-copilot-chat-and-agents). Microsoft Support. Bing query generation, grounding, and the Sources control. - How Bing delivers search results (https://support.microsoft.com/en-us/bing/how-bing-delivers-search-results). Microsoft Support. Bingbot crawling, index construction, and business results such as store hours and location. - AI Visibility in 2025: How Gemini, ChatGPT, and Perplexity Cite Brands (https://www.yext.com/blog/ai-visibility-in-2025-how-gemini-chatgpt-perplexity-cite-brands). Yext, October 2025. 6.8 million citations across 1.6 million responses. - Cheers market baseline, 28 days ending September 2, 2026 (https://www.cheers.tech/research/ai-visibility-statistics). 32 home-services prompts, 100 metro areas, four engines, 356,019 citations, aggregate only. Methodology (https://www.cheers.tech/research/methodology). *Dylan Allen-Arnegard (https://www.linkedin.com/in/dylan-allen/) is the CEO and Co-Founder of Cheers. I help multi-location service brands get recommended by AI search and Google, with results tracked by location, employee, and competitor.* --- ## AI Visibility Score: What's Good? 2026 Benchmarks Slug: what-is-a-good-ai-visibility-score Category: Research Published: 2026-05-29 Last updated: 2026-10-26 URL: https://www.cheers.tech/geo-academy/what-is-a-good-ai-visibility-score A good AI visibility score for a home-services company is an appearance rate of 34.9% or higher, the top-quarter line across the 13 home-services companies Cheers tracked in the 28 days ending September 2, 2026. Leaving out questions that name the company itself, the median company was named in 29.5% of the AI answers we checked for it, and the bottom quarter sat under 12.8%. On a set of 100 buying questions, that spread is the difference between being named in fewer than 13 answers and in 35 or more, and every answer you are missing names somebody else. Last verified: September 28, 2026. Important: Corrected September 28, 2026. The first version of this page put the good line at 37.3% across "119 organizations". The 119 were account records, not companies, and our aggregate table had counted many AI answers two or three times. The panel also included branded prompts, questions that name the company itself and return it almost every time, and a Cheers test account. Every number below counts each answer and each company once and leaves both out. The correction note on the index (https://www.cheers.tech/research/home-services-ai-visibility-index-2026#correction) lists what changed. ## The benchmark bands An AI visibility score, the way I use the term, is an appearance rate: the share of tracked AI answers that name your business. If you do not know your own number yet, the AI Visibility Grader (https://www.cheers.tech/visibility) will measure one location in a few minutes. The 13 companies fell into four bands: - Below 12.8%: bottom quarter. The business is missing from about seven tracked answers in eight. - 12.8% to 29.5%: below the median company. Named sometimes, rarely by default. - Around 29.5%: the median company, named in about three answers in ten. - 34.9% or higher: top quarter. This is the line I call good. The mean across companies was 25.2%, a little under the median because a few companies near the bottom pull it down. The median company was cited as a source in 20.2% of its answers, and every one of the 13 companies was named at least once, so total absence is rare; frequency is the problem. There is no top-10% band. With 13 companies a 90th percentile would sit between the two highest companies, so we do not publish one. Those rates leave out branded prompts, questions such as "would you hire [your company] for a sewer backup?" that put the business name in the question and get it back almost every time. Three of the 13 companies track them. Left in, they would lift the mean to 27.9% and the bottom-quarter line to 22.7%, which measures whether an engine repeats a name you typed, not whether it recommends you. If your own tracking includes questions with your name in them, take them out before you compare against these bands. Read the scope before you borrow the numbers. Each company is measured once across all of its tracked locations, prompts, and engines: 183 locations and 958,754 AI answers across 593 tracked prompts between them. The panel is home services only, so a dentist or a law firm should not read these bands as their own, and 13 companies is a small panel. Treat the bands as a working benchmark, not a law of nature. The full cuts are in the Home Services AI Visibility Index (https://www.cheers.tech/research/home-services-ai-visibility-index-2026). ## One company, four different scores The same 13 companies score very differently depending on which engine you ask. Median company appearance by engine, same window: Google AI Mode 36.0%, ChatGPT 24.1%, Gemini 21.3%, Perplexity 13.9%. That is 22.1 points between the best and worst engine for the same businesses. Citation rates split differently again. The company mean citation rate was 31.0% on Google AI Mode, 23.4% on Perplexity, 11.5% on ChatGPT, and 10.8% on Gemini, so a company can be named often on one engine and almost never used as a source on another. ChatGPT is also the one to watch on position. Across the 10 companies with ranked ChatGPT answers, the median company's average position when named was about 2.7. Third on a four-name list is a different commercial outcome from first, and an appearance rate on its own will not show you that. The wording of the questions moves the number too, which is why a score needs its question list attached. Pooled across every check, questions asking for the best or top companies without a place name returned the tracked company 28.4% of the time; our panel is still too small to publish most other question shapes, including near-me questions, which only nine companies track. If a vendor quotes you one score, ask which engines and which questions are behind it, and whether any of those questions contain your own name, before you compare it with anything. The source comparison (https://www.cheers.tech/geo-academy/ai-search-engine-source-differences) explains why one engine cannot stand in for the rest. ## What people mean by a local score "Local score" is an umbrella label, not a standard metric. One product may use it for Google Business Profile completeness, another for Maps rankings, and another for reviews, citations, website health, or AI recommendations. Those numbers cannot be compared until the underlying inputs match. Ask what the score measured, which locations and services it covered, when it ran, and whether the result exposes the evidence behind the number. A score that hides the prompts, providers, source URLs, competitors, and location details cannot explain what changed. The Cheers grader uses a separate 100-point model, with Google Presence and AI Visibility as distinct categories. Its overall score combines several signals, and its AI Visibility category weights recommendation position. Neither is directly comparable with the raw appearance-rate benchmarks above. A complete Google Business Profile does not prove that ChatGPT, Gemini, or Perplexity recommended the business in the tested answers. ## An AI visibility score is not an SEO visibility score Search for "visibility score" and most of what comes back describes an SEO metric. Semrush says its Visibility % "is based on the click-through rate (CTR) that shows a website's progress in Google's top 100 for keywords" in a tracking campaign. SISTRIX weights each ranking "according to the search volume of the keyword and the expected click-through-rate (CTR) on the measured, organic position." Both are built from where your pages rank in classic Google results. An AI visibility score starts somewhere else. It counts whether an answer from ChatGPT, Gemini, Perplexity, or Google AI Mode named the business at all, and it is read from saved answers you can audit line by line. The two can move in opposite directions. A service page can sit on page one for "water heater replacement" while the AI answer to "who should I call to replace my water heater" names three competitors and a directory. Keep both, on separate lines of the report. Never average an SEO visibility percentage into an AI appearance rate; they share a name and nothing else. ## Use appearance rate before a blended score AI visibility tools calculate their scores in different ways, and they say so in their own documentation. BrightLocal's Local AI Visibility report gives a score out of 100 that blends how often you are mentioned, where you appear, how positively you are described, and how strongly you are recommended. Local Falcon scopes its metric far more tightly: Share of AI Voice (SAIV) is a metric that shows how visible your brand is within AI-generated search results for a specific query and area. One is a blended index across platforms. The other is a per-query, per-area share. Both are useful inside their own tool, neither converts into the other, and a vendor telling you "your AI visibility score went up 12 points" has told you nothing you can audit. I start with appearance rate because it is plain enough to check from the saved responses. Citation rate answers a separate question: how often the answer used the business or its site as a source. ## A score without location detail misleads A parent brand can post a healthy company score because a few mature branches appear often. That number can hide a new acquisition whose name, page, Business Profile, reviews, and citations still disagree with each other. The operating view breaks the score down by location, buyer prompt, engine, competitor, and cited source. A plumbing branch can do well for drain cleaning and disappear for water-heater replacement. An HVAC branch can show up in Gemini and miss in ChatGPT. The company average does not tell the branch owner which problem to fix. The location-level audit guide (https://www.cheers.tech/geo-academy/how-to-audit-ai-search-visibility-across-locations) shows how to store those dimensions. ## What to do this week Pick three branches and the five services that make each of them the most money. Write the question a customer would actually type for each service, without your business name in it, then run all fifteen on ChatGPT, Gemini, Perplexity, and Google AI Mode and record whether the branch was named. Divide the named answers by the total for each engine and put the four numbers next to the bands above. If a branch is below 12.8% on an engine, check that its hours, services, and service area are right on the Business Profile and the directories before anyone writes content. If it sits near the 29.5% median, compare the answers it lost with the competitors and sources that appeared instead. If it clears 34.9%, protect what is working and look for the weak service line rather than chasing the company average. Keep the same prompts, engines, and markets every time you rerun, because changing any of them changes the denominator. If you would rather start from a measured baseline than a spreadsheet, run the grader on the weakest of the three branches first. ## Methodology The bands come from the corrected Cheers production panel pull of September 28, 2026: read-only queries over daily prompt aggregates dated August 6 through September 2, 2026. The panel is every company in the Cheers account tree with at least one organization categorized as home services, excluding Cheers-owned and internal research organizations and a Cheers test account. Each AI answer is counted once, using only the prompt-level row for each answer. Branded prompts, those whose text contains the company's own name or an approved alias, are left out. Each company's appearance rate is its named answers divided by all of its answers across every tracked location, non-branded prompt, and engine, and percentiles are taken across the 13 companies. Engines are ChatGPT, Gemini, Perplexity, and Google AI Mode. Published cells need at least ten companies behind them; no company is named. The rules are in the methodology (https://www.cheers.tech/research/methodology). ## Sources The Cheers pages were checked on September 28, 2026, and the Semrush, SISTRIX, Local Falcon, and Google links on September 23, 2026. BrightLocal's help center blocked automated access on September 23; its definition was last checked on September 2, 2026. - Home Services AI Visibility Index 2026 (https://www.cheers.tech/research/home-services-ai-visibility-index-2026). Cheers Research, edition September 2, 2026, corrected September 28, 2026. 13 home-services companies, 958,754 AI answers to non-branded prompts, aggregate only. Supports the percentile bands, the branded-prompt comparison, the engine medians and citation means, the ChatGPT position, and the prompt-shape rate. - AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics). Cheers Research. The same corrected panel figures, one dated statistic at a time. - What is Visibility %? (https://www.semrush.com/kb/697-what-is-visibility). Semrush Knowledge Base. The ranking-and-CTR definition quoted above. - Calculation of the SISTRIX Visibility Index (https://www.sistrix.com/visibility-index/calculation). SISTRIX, updated March 7, 2023. The search-volume and CTR weighting quoted above. - What does each Local AI Visibility metric mean? (https://help.brightlocal.com/hc/en-us/articles/37990220496018-What-does-each-Local-AI-Visibility-metric-mean). BrightLocal Help Center. Definitions of visibility score, mention rate, share of voice, and average position. - How to use Share of AI Voice (https://www.localfalcon.com/knowledge-base/kb92-how-to-use-share-of-ai-voice-saiv). Local Falcon Knowledge Base. The per-query, per-area definition quoted above. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 10, 2026. A page must be indexed and eligible to show with a snippet before it can appear in Google's generative AI features. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO and Co-Founder of Cheers. I help multi-location service brands get recommended by AI search and Google, with results tracked by location, employee, and competitor.* --- ## Best AI Visibility Tools for Local Businesses Slug: best-ai-visibility-tools-local-businesses Category: Comparisons Published: 2026-06-13 Last updated: 2026-10-26 URL: https://www.cheers.tech/geo-academy/best-ai-visibility-tools-local-businesses The best AI visibility tools for a local or multi-location operator are Cheers, Local Falcon, BrightLocal, Otterly, Semrush, Ahrefs Brand Radar, Peec AI, and Profound. The right choice depends on whether you need a software report or a team that fixes the local inputs behind it. Last verified: September 2, 2026. My criteria are simple: the tool must track real buyer prompts, preserve the cited sources, separate locations, show the local competitors, and make the next owner obvious. A local score without those details is hard to use. The prompt set decides what you see. In the Cheers production panel of 13 home-services companies covering the 28 days ending September 2, 2026, leaving out questions that name the company itself, "best or top" list prompts without a place name produced a 28.4% pooled appearance rate. Cheers does not publish a rate for "best near me" or named-place prompts because too few companies track that shape, so ask any vendor which prompt shapes sit behind its number before you compare two tools. Aggregate only, no company named; see the panel methodology (https://www.cheers.tech/research/methodology) and AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics). An agency buying reports for clients has a separate access and handoff problem. The agency AI visibility tools guide (https://www.cheers.tech/geo-academy/best-ai-visibility-tools-agencies-managing-local-clients) compares that workflow. ## The local operator shortlist - Cheers: done-for-you work for multi-location service brands, with tracking by location, prompt, engine, competitor, source, and employee-linked review operations. - Local Falcon: self-serve geo-grid scanning for supported AI and map surfaces, built around a business location. - BrightLocal: local SEO software with Local AI Visibility included on active locations in its Track, Manage, and Grow plans. - Otterly: lower-cost self-serve prompt and citation monitoring. Its plan page lists "Tracking of 4 AI Search Engines: ChatGPT, Google AI Overviews, Perplexity, MS Copilot" in the base, with "Claude, Google AI-Mode, Gemini as extra Add-ons." - Semrush: AI visibility inside a broader SEO workflow, sold per domain with custom prompts. - Ahrefs Brand Radar: broad brand, web, and AI monitoring with custom prompts that retain location context. - Peec AI: model-level visibility, citation, sentiment, and competitor analytics for marketing teams. - Profound: brand and content monitoring for enterprise teams. As of September 23, 2026, brands get a free 7-day trial or custom Enterprise pricing, which covers up to nine answer engines. ## Start with the location, not the brand average A home-services company does not win one national answer. It wins or loses an emergency plumbing question in one market, a heat-pump question in another, and a roofing comparison somewhere else. That makes the reporting unit important. Local Falcon uses geo-grid scans, and BrightLocal organizes Local AI Visibility inside an active location. Semrush can generate prompts from a domain and location, Ahrefs carries location context into custom prompts, and Peec supports projects and countries. Profound publishes region limits by plan; Otterly supports multiple countries and workspaces. Cheers goes further for the narrow market we serve. We connect each missed recommendation to the location page, Business Profile, review pattern, citation, or local source that needs work, then we own that work with the client. That is a service model, not a lighter software plan. ## Where broad platforms fit Broad AI visibility platforms are useful when the business wants to monitor brand mentions, corporate narratives, PR sources, content gaps, and market perception. The right question is fit, not which logo has the longest feature list. Use this buyer shortlist by job. Cheers appears in that comparison because I want the boundary to be clear. We are not the right choice for corporate PR monitoring, an ecommerce catalog, or a single-location team that only wants to inspect raw answers. Ahrefs, Semrush, Profound, Peec, or Otterly will usually be cleaner for those jobs. If the core need is a local geo-grid, start with Local Falcon. If your team already lives in BrightLocal, its active-location view can reduce tool sprawl. If nobody on your side has time to turn findings into page, profile, review, listing, and content work, a self-serve monitor will document the problem without fixing it. ## How I would evaluate a trial Run the same service question in at least two markets. Confirm the exact engine, location setting, answer, competitors, cited pages, refresh cadence, export options, and plan limit. Then ask who owns the first corrective action. Do not accept a sales demo built only around the parent brand name. A buyer is more likely to ask who can solve a specific problem in a specific place. Your test set should match that decision. Whatever the tool reports, the fix usually lands on surfaces Google documents. Its AI features guidance, last updated December 2025, is short about the eligibility question: "There are no additional technical requirements." The work is the underlying page, profile, and review evidence. Use the AI Visibility Grader (https://www.cheers.tech/visibility) for a baseline. Then follow the location-level audit workflow (https://www.cheers.tech/geo-academy/how-to-audit-ai-search-visibility-across-locations) if you need a repeatable record across branches, and the price anchors (https://www.cheers.tech/geo-academy/how-much-does-ai-visibility-software-cost) when the shortlist is down to two. ## How this comparison was built We reviewed each vendor's official product, documentation, and pricing pages listed below. The comparison records publicly described capabilities; it does not award points for company size, funding, or category popularity. Pricing appears only where a vendor publishes it. Cheers wrote this guide and appears in the comparison. That makes it a vendor-authored buying aid, not an independent ranking. Use the criteria to pressure-test Cheers and every other platform against the same location set, prompts, providers, competitors, cited sources, and handoff process. ## Sources - Local Falcon AI scan reports (https://www.localfalcon.com/knowledge-base/kb96-how-to-read-an-ai-visibility-scan-report). Official location, geo-grid, competitor, and source details checked September 2, 2026. - BrightLocal Local AI Visibility (https://help.brightlocal.com/hc/en-us/articles/37989673412114-What-is-Local-AI-Visibility). Official active-location, platform, prompt, competitor, and source details checked September 2, 2026. - Otterly pricing (https://otterly.ai/pricing). Official engine, add-on, workspace, and prompt details checked September 2, 2026. - Semrush AI visibility features (https://www.semrush.com/kb/1626-ai-visibility-features). Official domain, location, engine, prompt, and update details checked September 2, 2026. - Ahrefs Brand Radar (https://help.ahrefs.com/en/articles/11064852-what-is-brand-radar-and-how-to-use-it). Official custom-prompt, location, engine, and price details checked September 2, 2026. - Peec AI pricing (https://peec.ai/pricing). Official model, project, country, and plan details checked September 2, 2026. - Profound pricing (https://www.tryprofound.com/pricing). Official plan details checked September 23, 2026. Brands see a free 7-day trial (50 prompts on ChatGPT, Gemini, and Google AI Overviews) and custom Enterprise pricing covering up to nine answer engines; the earlier $99 and $399 brand plans are no longer listed. - AI features and your website (https://developers.google.com/search/docs/appearance/ai-features). Google Search Central, last updated December 2025. Checked September 2, 2026. - AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics) and methodology (https://www.cheers.tech/research/methodology). Cheers Research, September 2, 2026. Appearance rate for "best or top" list prompts across 13 home-services companies, non-branded prompts, 28 days ending September 2, 2026 (corrected September 28, 2026). *Dylan Allen-Arnegard (https://www.linkedin.com/in/dylan-allen/) is the CEO and Co-Founder of Cheers. I help multi-location service brands get recommended by AI search and Google, with results tracked by location, employee, and competitor.* --- ## How Much Does AI Visibility Software Cost? 2026 Prices Slug: how-much-does-ai-visibility-software-cost Category: Comparisons Published: 2026-06-14 Last updated: 2026-09-28 URL: https://www.cheers.tech/geo-academy/how-much-does-ai-visibility-software-cost AI visibility software costs $24.99 to $295 a month for the cheapest paid plan, based on the vendor pricing pages we read on September 23, 2026, from Local Falcon's Starter credits to AthenaHQ's Starter plan. A 25-location service brand pays $156 to $499 a month at list price for a plan that covers its whole footprint, and four of the 20 vendors we checked (Profound, Yext, SOCi and Birdeye) publish no price for brands at all. Last verified: September 23, 2026. Prices in this category move quickly. Between our September 2 check and this one, Profound removed its $99 and $399 brand plans, BrightLocal's cheapest annual rate went from $29 to $31 a month, and LocalClarity's Professional tier dropped from $20 to $16 per location. Every price below carries the date we read it, and each vendor's own pricing page is in the sources. Cheers wrote this and sells a managed AI visibility program (https://www.cheers.tech/solutions/ai-visibility-platform). Cheers isn't in the table because it published its plans after this check: Visibility at $750 a month and Managed starting at $3,000 a month, priced to your scope. ## What AI answer trackers charge Otterly.AI's Lite plan is $29 a month for 15 prompts, or $25 billed annually, and it tracks ChatGPT, Google AI Overviews, Perplexity and Copilot; Gemini and AI Mode cost $9 a month each on top. HubSpot's AEO tool is $50 a month for 25 prompts per brand on ChatGPT, Gemini and Perplexity, and its one-time AI Search Grader is free. Peec AI's Starter is $95 a month, or $80 billed annually, for 50 prompts in 1 project on three models you choose. GinzaMetrics pricing now lives under a new name. The company renamed itself DemandSphere, and its Starter plan is $79 a month billed annually, or $95 monthly, for 100 prompts or 1,000 keywords across ChatGPT, Gemini, Perplexity, AI Mode and AI Overviews. Semrush's AI Visibility Toolkit is $99 a month for 1 domain and 25 prompts on ChatGPT, Google AI, Gemini and Perplexity. Higher up, Ahrefs sells Brand Radar at $199 a month per AI platform, or $699 for all of them with 2,500 custom prompt checks a month included. Scrunch's Core plan is $250 a month for 125 prompts and 5 users on ChatGPT, Perplexity, AI Overviews and Copilot, though the page doesn't say whether that rate assumes annual billing. AthenaHQ's Starter is $295 a month, or $245 billed annually, for 3,600 credits, where one credit is one AI response across 11 models; it also has a free Essential tier. Profound changed the most in three weeks. Its brand page now shows a free 7-day trial (50 prompts on ChatGPT, Gemini and AI Overviews) and a custom Enterprise tier, with nothing in between, while its agency plan still lists $99 a month plus add-ons. ## What local search suites charge for AI tracking Local Falcon's Starter is $24.99 a month for 7,500 credits. One credit is one map pin, so a 5x5 grid costs 25 credits, and its AI scans of AI Overviews, AI Mode, Gemini, ChatGPT and Grok draw on the same credits. BrightLocal's Track plan is $31 a month billed annually for one location, or $41 monthly, and its Local AI Visibility tracker comes included for active locations: ChatGPT, AI Overviews and AI Mode, up to 20 prompts per location. Synup's Solo plan is $49 a month for one location with AEO scoring on ChatGPT, Gemini and Perplexity, and its Pro plan is $499 a month for up to 25 locations. Uberall sells GEO Studio apart from its core platform. GEO Starter is $109 for 1,000 credits monthly (50 prompts, 3 engines), GEO Scale is $449 for 150 prompts, 5 engines and location-level tracking, and the core listings and reviews plans say "request pricing." Yext's Scout and SOCi's Genius Local Search Agent both track AI visibility by location, and neither publishes a price. LocalClarity's price is the most common search that lands on this page, so here it is plainly: Professional is $16 per location per month, or $12 billed yearly, for listings sync and reviews from 50+ sources. We didn't find AI answer tracking on its product pages, so it's in the table as a price anchor for listings and reviews, not for AI tracking. The Cheers vs LocalClarity comparison (https://www.cheers.tech/compare/cheers-vs-localclarity) covers when that software is the better buy. Birdeye's AI Search covers ChatGPT, Claude, Perplexity, Gemini, AI Overviews, AI Mode and Grok. Its pricing page asks for a location count and then a form, so there is no number to print. ## What managed services charge Three vendors publish a rate for doing the work rather than selling the dashboard. BrightLocal's Managed SEO Service is $1,299 a month. WebFX's AI SEO pricing starts at $3,000 a month on a 6-month commitment, and its local SEO starts at $575. Hook Agency, which works with home-service contractors, lists AEO at $4,000 a month, or $2,000 when added to its SEO package, which starts at $2,800. Cheers belongs in this group and published its plans after this check: Managed starts at $3,000 a month, priced to your scope, with our team doing the work, and Visibility is $750 a month for up to 3 locations. Bigger brands get a contract price after a scoping call, because the work behind a missed answer (location pages, profiles, reviews, listings, structured data) varies more by footprint than the tracking does. ## What drives the price Four meters decide the bill, and every vendor uses at least one of them. Prompts are the most common. Otterly's plans run $29 for 15 prompts, $189 for 100 and $489 for 400, the same product at three volumes. Engines come second: Otterly charges $9 to $149 a month to add Gemini or AI Mode, depending on the plan, and Peec charges $35 a month for a fourth model on Starter. Locations matter most to a multi-location brand, since BrightLocal and Synup price by location band while LocalClarity prices per location. Credits are the fourth meter, used by Local Falcon (one per map pin), AthenaHQ (one per AI response) and Uberall's GEO Studio. Seats rarely drive the price here: Peec and Otterly include unlimited users, and Scrunch's Core plan includes 5. The practical difference is how the bill grows. A location-metered tool gets more expensive as you open branches, and a prompt-metered tool gets more expensive as you ask more questions per branch, so a brand that wants 10 service questions in every market hits prompt limits long before it hits location limits. ## What a 25-location brand should budget Take a 25-location service brand tracking 10 questions per location, about 250 prompts. At list price and monthly billing, the smallest published plans that cover it are Synup Pro at $499 a month for up to 25 locations, Peec AI Advanced at $495 for 350 prompts on 3 models you choose, and Otterly.AI Premium at $489 for 400 prompts on 4 engines. Adding Gemini and AI Mode to Otterly costs $149 each on Premium, which brings it to $787. BrightLocal Track for 21 to 30 locations is $156 a month, or $117 billed annually, and covers up to 20 AI prompts per location on ChatGPT and Google's AI surfaces. The cheapest plan in that chart covers only ChatGPT and Google's AI surfaces, and none of the four does the work. Tracking is the small line item. Someone still has to fix the location page an answer skipped, the profile with stale hours, and the review gap at the branch that never shows up, and that labor is why the managed rates above start at $1,299 a month. A realistic budget for 25 locations has two lines: $156 to $499 a month for tracking, plus either your team's hours or a managed service for the fixes. ## What to do this week Write down your unit before the first demo: the number of locations, the questions you want tracked per location, and the engines your customers use. Price three tools from the table against that exact footprint, and ask the four vendors without a public price (Profound, Yext, SOCi and Birdeye) for a written quote at your location count. Run a free baseline before you buy anything. HubSpot's AI Search Grader and our AI Visibility Grader (https://www.cheers.tech/visibility) both run without a contract. For which tool fits which job, read Best AI Visibility Tools for Local Businesses (https://www.cheers.tech/geo-academy/best-ai-visibility-tools-local-businesses), and for engine coverage side by side, Tools That Track ChatGPT, Gemini, and Perplexity (https://www.cheers.tech/geo-academy/best-platforms-track-chatgpt-gemini-perplexity). ## How we checked these prices We read each vendor's own public pricing page on September 23, 2026, and recorded the cheapest paid plan for a brand (not an agency) plus the unit it is priced on. Several pages only show prices after their scripts run, including Peec's plans and BrightLocal's location bands, so we confirmed those in a browser or in the page's own pricing data rather than the static page source. "Not published" means the page shows no price and routes to sales. We didn't sign up, request quotes, or use third-party price lists. Prices are USD list prices before tax, discounts, and promotions, monthly unless a row says otherwise, and budget figures use the published plan for that location band. The September 2 figures we compare against come from our own previous check of the same pages. ## Sources - Otterly.AI pricing (https://otterly.ai/pricing). Lite $29 a month (15 prompts), Standard $189, Premium $489, engine add-ons. Read September 23, 2026. - HubSpot AEO (https://www.hubspot.com/products/aeo) and AI Search Grader (https://www.hubspot.com/ai-search-grader). $50 a month for 25 prompts; free grader. Read September 23, 2026. - Peec AI pricing (https://peec.ai/pricing). Starter $95, Pro $245, Advanced $495 a month, model add-ons. Read September 23, 2026. - DemandSphere pricing (https://www.demandsphere.com/pricing/) and GinzaMetrics becomes DemandSphere (https://www.demandsphere.com/blog/ginzametrics-gets-upgrade-new-name-introducing-demandsphere/). Starter $79 a month billed annually. Read September 23, 2026. - Semrush AI Visibility Toolkit pricing (https://www.semrush.com/pricing/ai/). $99 a month per domain, 25 prompts. Read September 23, 2026. - Ahrefs: What is Brand Radar (https://help.ahrefs.com/en/articles/11064852-what-is-brand-radar-and-how-to-use-it). $199 per index, $699 for all platforms. Read September 23, 2026. - Scrunch pricing (https://www.scrunch.com/pricing). Core $250 a month, 125 prompts. Read September 23, 2026. - AthenaHQ plans (https://www.athenahq.ai/plans). Starter $295 a month, 3,600 credits. Read September 23, 2026. - Profound pricing (https://www.tryprofound.com/pricing). Free trial and custom Enterprise for brands; agency plan $99 a month plus add-ons. Read September 23, 2026. - Local Falcon pricing (https://www.localfalcon.com/pricing). Starter $24.99 a month, 7,500 credits. Read September 23, 2026. - BrightLocal pricing (https://www.brightlocal.com/pricing/) and Local AI Visibility (https://www.brightlocal.com/local-seo-tools/local-ai-visibility/). Track $31 a month billed annually, location bands, Managed SEO Service $1,299 a month. Read September 23, 2026. - Synup pricing (https://www.synup.com/pricing). Solo $49, Pro $499 for up to 25 locations. Read September 23, 2026. - Uberall GEO Studio pricing (https://uberall.com/en-us/pricing-geo-studio). GEO Starter $109, GEO Scale $449. Read September 23, 2026. - LocalClarity pricing (https://www.localclarity.com/pricing/). Professional $16 per location, $12 billed yearly. Read September 23, 2026. - Yext Scout (https://www.yext.com/platform/scout). Surfaces tracked; no published price. Read September 23, 2026. - SOCi Genius Local Search Agent (https://www.soci.ai/genius-search/). Location-level AI visibility; demo only. Read September 23, 2026. - Birdeye AI Search (https://birdeye.com/ai-search/) and Birdeye pricing (https://birdeye.com/pricing/). Engines covered; pricing form, no published price. Read September 23, 2026. - WebFX AI SEO pricing (https://www.webfx.com/seo/pricing/ai-seo/) and local SEO pricing (https://www.webfx.com/local-seo/pricing/). From $3,000 and $575 a month. Read September 23, 2026. - Hook Agency pricing (https://hookagency.com/pricing/). Local SEO from $2,800, AEO $4,000 a month. Read September 23, 2026. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.* --- ## Best Local SEO Agency Alternatives for Home Services Slug: local-seo-agency-alternative-multi-location-home-services Category: Comparisons Published: 2026-06-12 Last updated: 2026-09-02 URL: https://www.cheers.tech/geo-academy/local-seo-agency-alternative-multi-location-home-services Google's own guidance on hiring SEO help, last updated June 5, 2026, now lists "Optimizing for generative AI" among the services agencies provide. Every shortlist below sells that line. The question a multi-location operator has to answer is which of them will still be there on a Tuesday when one branch's profile drifts and nobody has noticed. Last verified: September 2, 2026. The best local SEO agency alternative depends on the work that is actually failing. Scorpion, 1SEO, CAMP Digital, and RYNO are broad home-service marketing partners built around websites, SEO, paid media, lead flow, and reporting. Cheers is narrower: it manages the reviews, local pages, listings, structured facts, AI citations, and branch-level work that help each location get found and recommended. That makes the comparison more useful than calling every option an SEO agency. A brand that needs advertising and lead generation should evaluate a full-service agency. A brand that already has demand but lacks review velocity, local proof, AI cited-source tracking, and an owner for every market should evaluate Cheers. Channel eligibility still needs independent review. Apple's current Maps advertising policy prohibits home-services ads, so a full-service proposal should not count that inventory as available without a documented policy change. The Apple Maps ads guide for home services (https://www.cheers.tech/geo-academy/apple-maps-ads-home-services) covers the current boundary. Google still matters to both models. Local ranking is shaped by relevance, distance, and prominence, and complete business information helps Google match a business to relevant searches. On the review half, Google's local ranking page is unambiguous: "More reviews and positive ratings can help your business's local ranking." Google's AI Search guidance also points back to normal Search foundations: useful content, crawlable pages, good user experience, accurate business details, and structured data that matches visible content. Important: The best alternative to a local SEO agency is not "software instead of services." For a multi-location service brand, the better model is a done-for-you operating loop that connects reviews, locations, sources, and frontline behavior. If you want to pressure-test that model against one market or acquisition cluster, use the demo action below with the actual locations, services, and prompts you care about. ## The short answer Hire an agency when the work is a defined SEO project: site migration, technical cleanup, local page architecture, content planning, analytics setup, or a campaign that needs a specialist. Use software when your team already has the operators to turn alerts into work. Software can show listings gaps, rank changes, review trends, or AI visibility misses. Software alone does not guarantee that field teams, content owners, and regional managers will complete the next fix. Use a done-for-you local visibility platform when the work has to happen every week across locations. For a PE-backed HVAC group, plumbing brand, restoration rollup, garage door company, or franchise service system, the real question is not "who can write a page?" It is "who will notice the market is weak, identify the source gap, assign the work, and verify the next run?" Scorpion announced the 1SEO acquisition on June 18, 2026: "Scorpion, the leading provider of digital marketing and technology solutions for local businesses, today announced it has acquired 1SEO Digital Agency, a Philadelphia-based digital marketing agency founded in 2009." The table keeps separate rows because buyers may still encounter both brands and service models during the transition, but a current proposal should clarify which Scorpion platform, contract, and team will own the account. This is not a claim that the agencies are bad at the jobs they sell. Their public offers cover websites, paid media, lead generation, and revenue reporting. The distinction is operating focus. Cheers belongs on the shortlist when the hard problem is creating policy-compliant review opportunities, attributing them to employees and branches, tracking cited sources, and assigning local fixes every week. A full-service agency belongs on the shortlist when the buyer needs a broader acquisition program that includes advertising. For buyers comparing that model with a broad reputation platform, Birdeye Alternatives & Competitors (https://www.cheers.tech/geo-academy/birdeye-alternatives-ai-visibility-local-reviews) lays out the tradeoff and each vendor's published price. That is where Cheers' multi-location local SEO platform (https://www.cheers.tech/solutions/multi-location-local-seo) should be evaluated: not as a generic SEO retainer, but as a system for local visibility work that depends on operations. ## The owned site is where the citations land Cheers runs a market baseline that is not a customer: 32 natural-language home-services prompts executed in 100 metro service areas across four engines, producing 356,019 citations in the 28 days ending September 2, 2026. Pooled across those engines, 68.1% of citations went to business websites and 23.0% to directory and review platforms. Google's own properties took 3.3%, social and forums 2.8%. That distribution is the strongest argument for the owned-asset work an agency and a done-for-you platform both sell. It is also why a report that only tracks rank positions misses most of what the engines are actually reading. Method and cell-size rules are in the Cheers Research methodology (https://www.cheers.tech/research/methodology), with the full cuts in AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics). ## Where agencies are still useful A good agency can be the right choice when the problem is outside the day-to-day operating system. If a home services group just acquired 30 branches and needs URL migration support, a technical SEO agency can help. If the site is not crawlable, analytics are broken, or location pages are thin, a specialist can fix real problems. If leadership needs a content strategy, structured data review, or a second opinion on site architecture, agency work can be useful. Google's own SEO hiring guidance frames the decision this way: an SEO can improve a site and save time, but a bad one can damage the site or reputation. That is a buyer diligence problem. Ask what the agency will change, how the team will measure it, and who owns the work after the recommendation is written. For home services, the handoff is the risk. A deck that says "increase reviews in underperforming markets" is not the same as a field workflow that gets technicians, dispatchers, managers, and customers moving in the same direction. ## How to compare home-service SEO agencies If the shortlist includes Scorpion, 1SEO, CAMP Digital, RYNO Strategic Solutions, or a local home-services shop, compare the operating model behind each case-study headline. The breadth is real and it is stated plainly. RYNO's services page says: "We provide full-suite digital marketing, including SEO, website design, PPC, Local Services advertising, social media, email marketing, video production, and more." CAMP Digital's HVAC page leads with "Stay visible as AI-driven search behavior rapidly evolves." Breadth is the offer. Depth per branch is the thing to test. Ask each agency to show how it would handle five things: review velocity by branch, Google Business Profile service accuracy, location-page proof, source tracking for AI answers, and the handoff between marketing recommendations and field execution. A strong agency can still be the right partner. The gap appears when the agency can diagnose the issue but cannot move the branch behavior, review flow, or source cleanup that would fix it. The best agency relationship is honest about ownership. If the agency owns technical cleanup and page strategy while Cheers owns review generation, AI visibility tracking, and recurring local proof, the buyer gets clearer accountability than a single vague retainer promising "local SEO." ## Where retainers often need help Some traditional local SEO retainers need help in the same places: - They can report on rankings, but not always on whether the right branch appeared in ChatGPT, Gemini, Perplexity, or Google AI for a priority service query. - They can recommend more reviews, but not always attribute review creation to employees, branches, and market adoption. - They can publish service pages, but not always confirm whether the local operation can actually perform the work the page describes. - They can clean citations, but not always detect which cited sources an AI answer used this month. - They can advise profile hygiene, but not always keep the source stack aligned after acquisitions, rebrands, emergency-hour changes, and service-line changes. That does not make agencies bad. It means the buyer should separate advice from execution. A multi-location service business often needs both, but the scarce part is usually execution. Sierra Air Conditioning & Plumbing is a useful public example of the operating layer. The Sierra case study (https://www.cheers.tech/proof/sierra-cooling) ties public Google review growth to dated proof and AI visibility checks. Elite Rooter shows the plumbing version: 12 active markets and AI visibility checks across market-prompt pairs (https://www.cheers.tech/proof/elite-rooter). Those are not generic ranking reports. They are operating systems around local trust. ## What the done-for-you model should own A serious alternative to an SEO agency should own the weekly local visibility loop: - Measure priority prompts and locations across AI and local search surfaces. - Capture which sources are cited and which competitors are mentioned. - Tie review generation to branches, employees, and service moments. - Turn customer language and field proof into better location and service pages. - Keep Business Profile, location pages, citations, reviews, and structured facts aligned. - Assign every market miss to a real owner and retest after the fix. This is where AI visibility tracking (https://www.cheers.tech/solutions/ai-visibility-platform) and review generation (https://www.cheers.tech/solutions/review-generation) belong in the same conversation. AI visibility without frontline review work turns into a monitoring dashboard. Review generation without source tracking turns into a bigger review count that may not answer the buyer's next question. ## How to decide what to buy Start with the failure mode. If the site is technically broken, hire technical help. If the location pages are thin, fix the page system. If profiles and citations are inconsistent, clean the source layer. If reviews are stale, build a field workflow. If AI answers mention competitors because they cite stronger third-party sources, track the source pattern before writing another blog post. Then ask who can own the work every week. Agencies, internal teams, and software can all be part of the answer, but they should not hide the real owner. A local visibility program needs a cadence: inspect prompts and markets, identify the cited source gap, make the page or profile change, improve review capture, and check again. If the decision is moving from retainer advice into tooling, use Best Local SEO Software for Multi-Location Service Businesses (https://www.cheers.tech/geo-academy/best-local-seo-software-multi-location-service-businesses) as the comparison layer. For most multi-location service brands, the agency alternative is not cheaper content. It is tighter ownership. The right platform should make the work visible enough that marketing, operations, and local managers can act without waiting for the next retainer meeting. ## Sources - Do you need an SEO? (https://developers.google.com/search/docs/fundamentals/do-i-need-seo). Google Search Central, last updated June 5, 2026. The service list, now including optimizing for generative AI, and the warning about aggressive or spam-policy-violating work. Checked September 2, 2026. - Optimizing your website for generative AI features (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 2026. Checked September 2, 2026. - Tips to improve your local ranking on Google (https://support.google.com/business/answer/7091). Google Business Profile Help, undated help page. Relevance, distance, prominence, and the review sentence. Checked September 2, 2026. - Tips to get more reviews (https://support.google.com/business/answer/3474122). Google Business Profile Help, undated help page. Checked September 2, 2026. - Scorpion acquires 1SEO Digital Agency (https://www.scorpion.co/about-us/scorpion-news/scorpion-news/2026/scorpion-acquires-1seo-digital-agency-bringing-a/). Scorpion, June 18, 2026. Checked September 2, 2026. - Scorpion home services (https://www.scorpion.co/home-services/) and Scorpion RevenueMAX for home services (https://www.scorpion.co/revenuemax/why-revenuemax-home-services/). Scorpion, 2026. Checked September 2, 2026. - 1SEO home services digital marketing (https://1seo.com/digital-marketing-services/home-services-digital-marketing/). 1SEO, 2026. Checked September 2, 2026. - CAMP Digital HVAC marketing (https://campdigital.com/hvac-digital-marketing/). CAMP Digital, undated. AI and search optimization, paid search, websites, capacity marketing, and attribution. Checked September 2, 2026. - RYNO digital marketing services (https://rynoss.com/services/) and RYNO restoration marketing (https://rynoss.com/industries/restoration/). RYNO Strategic Solutions, undated. Full-suite service mix. Checked September 2, 2026. - AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics) and methodology (https://www.cheers.tech/research/methodology). Cheers Research, September 2, 2026. Market Baseline citation pull: 32 prompts, 100 metros, four engines, 356,019 citations in the 28 days ending September 2, 2026. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the done-for-you platform that manages the website, reviews, listings, structured data, and local content that get service businesses recommended across Google, Maps, ChatGPT, and Perplexity.* --- ## Why Does AI Recommend My Competitor? Slug: ai-recommends-competitors Category: Playbooks Published: 2026-06-27 Last updated: 2026-09-02 URL: https://www.cheers.tech/geo-academy/ai-recommends-competitors AI recommends a competitor when the retrieved evidence makes that business a clearer fit for the location and service in the prompt. I diagnose the miss in this order: location match, service proof, business facts, review evidence, cited sources, and engine access. Last verified: September 2, 2026. The comparison has to use the exact answer. A generic brand search cannot explain why one branch lost an emergency plumbing recommendation in one city. ## Save the evidence before changing anything Record the prompt, engine, location context, businesses named, wording of the recommendation, and every cited page. The citation trail is the only part of the mechanism the vendors actually document. OpenAI's web search guide puts it plainly: Web search allows models to access up-to-date information from the internet and provide answers with sourced citations. The same document explains how geography enters the request: "you can specify an approximate user location using country, city, region, and/or timezone." Google describes query fan-out in AI Mode, where the model issues concurrent related queries and shows supporting links. Perplexity's own developer docs describe real-time, ranked web results with citations on the answer side. None of them publish a fixed local ranking formula, and no third-party tool has one either. Google's guidance is direct about that: "No third-party tool has access to our internal ranking or AI systems." So work from the citation trail, not from a score. ## Compare the six likely gaps - Location match: does the winning source prove the competitor serves the requested market while your branch page stays vague? - Service proof: does the competitor have a clear page and recent customer language for the exact job? - Business facts: do your website, Business Profile, phone, hours, service area, and public listings agree? - Review evidence: do recent reviews describe the service and outcome in customers' own words? - Cited sources: is the engine relying on a directory, publisher, or business page where the competitor has stronger or more current evidence? - Engine access: can the engine's search crawler reach the page you expect it to use? Do not manufacture reviews, copy the competitor, or publish thin pages for every wording variation. Google says creating many pages mainly to target query variations or fan-out searches can violate its scaled content abuse policy. ## Use the panel as context The mean company appearance rate is 25.2 percent in the Cheers panel, 28 days ending September 2, 2026, 13 home-services companies, leaving out questions that name the company itself, aggregate only. The median is 29.5 percent. Missing from one answer is common; missing across the high-intent prompt set is the problem to fix. The shortlist also moves on its own: in the Cheers Market Baseline, Google AI Mode named only 41% of the same businesses again when the same question for the same city was asked a week later, which is why AI recommends different businesses every time (https://www.cheers.tech/geo-academy/why-ai-recommends-different-businesses) you check. The spread is what makes the average useless on its own. The 25th percentile company appeared in 12.8 percent of its checks and the 75th in 34.9 percent. All 13 companies were named at least once, so "AI never mentions us" is rarely literally true. The home-services AI visibility index (https://www.cheers.tech/research/home-services-ai-visibility-index-2026) publishes the distribution; the methodology (https://www.cheers.tech/research/methodology) defines a check and an appearance. That panel covers 593 distinct tracked prompts across the 13 companies, and I only report the aggregate. Underneath it I keep the branch, prompt, engine, competitor, and source attached to every result so an average does not hide a local miss. ## Turn one miss into one owned fix If the cited page has the wrong hours, correct the hours. If the competitor wins on a specific service page, improve the real customer information and proof on the corresponding branch page. If the review set never mentions the service, fix the neutral post-service request process instead of scripting customer language. The location audit workflow (https://www.cheers.tech/geo-academy/how-to-audit-ai-search-visibility-across-locations) shows how to assign the work. For trade-specific versions of the same diagnosis, see HVAC (https://www.cheers.tech/geo-academy/how-hvac-companies-get-recommended-by-chatgpt) and plumbing (https://www.cheers.tech/geo-academy/how-plumbing-companies-get-recommended-by-chatgpt). The engine source comparison (https://www.cheers.tech/geo-academy/ai-search-engine-source-differences) explains why the winning source can change by product. Use the AI Visibility Grader (https://www.cheers.tech/visibility) for a current baseline. Keep the answer and source trail so the next run can be compared with the same scope. If the next question is whether to hand this to an agency or run it in-house, Cheers compared with an SEO agency (https://www.cheers.tech/compare/cheers-vs-seo-agency) sets out who owns which part of the work. ## Sources Every link below was opened and checked on September 2, 2026. - Web search. OpenAI Platform documentation. Checked September 2, 2026. developers.openai.com/api/docs/guides/tools-web-search (https://developers.openai.com/api/docs/guides/tools-web-search). Source of the quoted lines on sourced citations and approximate user location. - Google's guide to optimizing for generative AI features on Google Search. Google Search Central. Last updated July 10, 2026, checked September 2, 2026. developers.google.com/search/docs/fundamentals/ai-optimization-guide (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Source of the quoted line on third-party tools, plus the query fan-out and scaled-content guidance. - Search API quickstart. Perplexity developer documentation. Checked September 2, 2026. docs.perplexity.ai/docs/search/quickstart (https://docs.perplexity.ai/docs/search/quickstart). Supports the real-time ranked web results and citation behavior described here. - Spam policies for Google web search. Google Search Central. Checked September 2, 2026. developers.google.com/search/docs/essentials/spam-policies (https://developers.google.com/search/docs/essentials/spam-policies). Supports the scaled content abuse warning against publishing a page per query variation. - Home Services AI Visibility Index 2026. Cheers Research. Cheers panel, 28 days ending September 2, 2026, 13 home-services companies, 958,754 checks, 593 distinct non-branded prompts, aggregate only (corrected September 28, 2026). Figures first pulled September 3, 2026. cheers.tech/research/home-services-ai-visibility-index-2026 (https://www.cheers.tech/research/home-services-ai-visibility-index-2026). Source of the mean, median, percentile, and zero-appearance figures. *Last verified: September 2, 2026.* *Dylan Allen-Arnegard (https://www.linkedin.com/in/dylan-allen/) is the CEO and Co-Founder of Cheers. I help multi-location service brands get recommended by AI search and Google, with results tracked by location, employee, and competitor.* --- ## Where Does ChatGPT Get Its Information? Local Data 2026 Slug: what-sources-does-chatgpt-use Category: Research Published: 2025-12-20 Last updated: 2026-09-28 URL: https://www.cheers.tech/geo-academy/what-sources-does-chatgpt-use ChatGPT gets its information from two places: what its models learned in training, from public web pages, partner data and people, and a live web search it runs through outside search providers when a question needs current facts. For a local business the live search is the part you can see and act on, and its sources are not the obvious ones: across 9,434 ChatGPT answers to home-services questions in 100 US and Canadian metros between August 31 and September 20, 2026, ChatGPT cited Expertise.com in 32.1% of answers and Angi or HomeAdvisor in 29.4%, but Yelp in 0.6%. That matters if you run 40 locations, because the pages deciding whether ChatGPT names a branch are mostly pages you did not write. Of the ChatGPT answers that cited anything, 84% cited at least one directory, review platform or "best of" list. Last verified: September 23, 2026. The numbers come from the Cheers Market Baseline, which asks four AI engines the same questions every week; the method is at the end of this page. To check one of your own locations against live AI answers, run the free Cheers AI Visibility Grader (https://www.cheers.tech/visibility). ## What OpenAI says about where ChatGPT's information comes from OpenAI documents both halves, in two help center articles updated in August 2026. The training half is described in "How ChatGPT and our foundation models are developed." OpenAI says its models learn from three primary sources: information publicly available on the internet, information it accesses through partnerships with third parties, and information that users, human trainers and researchers provide or generate. It adds that it increasingly uses synthetic data, and that site owners can keep their pages out of training by disallowing GPTBot in robots.txt. The live half is described in "Searching the web with ChatGPT." ChatGPT may search the web automatically when a question would benefit from current information. When it does, it rewrites your question into one or more targeted queries and sends them to search providers, and the page points readers to Microsoft's and Shopify's privacy policies for how those providers handle the queries. It also estimates a general location from your IP address, so OpenAI's own example turns "good restaurants near me" into "top restaurants San Francisco." For local questions, OpenAI says ChatGPT may share that location with trusted third-party providers that supply nearby business listings, and on iOS and Android the answer can include a map. OpenAI does not name the business-listing provider behind the map. When it launched ChatGPT search on October 31, 2024, it said only that it had partnered with news and data providers for categories including weather, news and maps. Outside testing fills in part of the picture: in May 2025, Search Engine Land's Damian Rollison found that ChatGPT pulled web results from Bing's index for local questions but ordered them with its own logic, and that it did not see the profile details businesses manage in Bing Places. On ranking, OpenAI's help article gives site owners one sentence: ChatGPT ranks search results using multiple factors intended to help users find relevant, reliable information. Placement is not guaranteed. The one thing OpenAI tells site owners to do is technical. Allow OAI-SearchBot in robots.txt and make sure your host or CDN is not blocking OpenAI's published search IP addresses. OpenAI's crawler documentation is blunt about the alternative: sites opted out of OAI-SearchBot will not be shown in ChatGPT search answers. GPTBot is the separate training crawler, so blocking it does not remove you from search. So for a question like "who are the best plumbers for water heater repair or replacement?" asked from a phone in Denver, the answer you can inspect is built from two visible layers: the pages ChatGPT cites and the business cards on its map. Those are the two layers we measured. ## What sources does ChatGPT use for local businesses? Directories and ranking lists, then contractor websites, then almost nothing else. In the three weeks we measured, ChatGPT's 9,434 answers carried 43,785 citation links. Named platforms led: Expertise.com in 32.1% of answers, Angi or HomeAdvisor in 29.4%, HomeStars in 16.0%, BBB in 14.5%, Thumbtack in 5.0% and Reddit in 4.9%. A long tail of other directories and "best of" list sites, a group that includes Three Best Rated, Best Pick Reports, HomeGuide, Yellow Pages and dozens of smaller directories, showed up in 59.9% of answers. Sites that rank local companies, the "best of" list format, appeared in 64.2% of the ChatGPT answers that cited anything. Contractor websites were the other big source. At least 44.2% of ChatGPT answers cited a website belonging to a business in our dataset, and those sites took 26.8% of all ChatGPT citation links, about the same share as the long tail of directories. The trade changes the split. In plumbing, 53.7% of ChatGPT answers cited a plumber's own site; in roofing, only 17.8% of 600 answers cited a roofer's site while 40.0% cited Expertise.com. Google and Yelp barely registered. ChatGPT cited a Google page in 0.1% of answers and Yelp in 0.6%, 52 answers out of 9,434. Yelp shows up somewhere else: in the five days from September 18 to 22, when we captured ChatGPT's map in full, 34% of the rated business cards on it linked to a Yelp business page, and 98% of the answers showing a business under 4.0 stars involved one of those Yelp cards. We cover that rating gap in why ChatGPT shows a lower star rating than Google (https://www.cheers.tech/geo-academy/why-chatgpt-shows-lower-star-rating-than-google). When ChatGPT did cite a contractor's site, it cited the homepage 37.4% of the time, a service page 24.1%, a page naming a service and a city 17.4%, and a location page 10.7%. Blog posts got 1.2%. We turned that into a page spec in what location pages should include for AI search (https://www.cheers.tech/geo-academy/what-should-location-pages-include-for-ai-search). ## Four engines, four source lists The same 32 questions, asked in the same 100 metros in the same weeks, produced four very different source lists. Each cell below is the share of that engine's answers citing at least one page from the source. ChatGPT is the directory engine, with the long-tail lists, Expertise.com and Angi or HomeAdvisor covered above, and it almost never cited Google (0.1%), Yelp (0.6%) or a booking page (0.0%). Google AI Mode is the opposite. It cited Google's own pages in 95.5% of answers and a business website in 92.5%, linked an online booking page in 31.2% and Yelp in 19.7%, while Reddit came in at 9.1%, Angi or HomeAdvisor 9.3%, long-tail directories 9.2%, BBB 1.7% and Expertise.com 0.2%. Only 38.3% of its answers with citations cited any directory, review platform or list, against 84.2% for ChatGPT. Perplexity cites everything at once: business websites 89.0%, long-tail directories 81.7%, BBB 31.0%, Expertise.com 30.9%, Angi or HomeAdvisor 30.8%, Yelp 13.9%, Reddit 10.8%, Google 1.1% and booking pages 0.2%. Across its answers with citations, 93.5% cited a directory, review platform or list. Gemini is the hardest to read, because we captured citations for only 4,141 of its answers. Within those, it cited business websites in 88.3%, long-tail directories 37.8%, Angi or HomeAdvisor 13.4%, BBB 8.4%, Expertise.com 6.0%, Reddit 1.2% and Google 0.2%, and we saw no Yelp or booking-page citations. Across its answers with citations, 55.8% cited a directory, review platform or list. For the engine-by-engine version of this page, read the source differences between ChatGPT, Gemini, Perplexity and Google AI Mode (https://www.cheers.tech/geo-academy/ai-search-engine-source-differences). The practical result is that a brand can be strong on one engine and missing on another. On occasions when all four engines answered the same question for the same city in the same week, 77% of the businesses named were named by only one of them. Our AI local ranking factors summary (https://www.cheers.tech/geo-academy/ai-local-recommendation-factors-2026) lays out those differences factor by factor, from ratings to booking links. ## The US and Canada are different source markets If you operate on both sides of the border, ChatGPT is reading two different directory sets. Angi and HomeAdvisor appeared in 49.1% of ChatGPT's 5,659 US answers and in 1 of its 3,775 Canadian answers. Since September 16, 2026, Angi has also been testing a paid, labeled chat that opens from ChatGPT ads in the US, which we explain in what ChatGPT Sponsored Agents are (https://www.cheers.tech/geo-academy/chatgpt-sponsored-agents-home-services). HomeStars fills that role in Canada, at 39.9% of Canadian answers and none in the US. Expertise.com appeared in 53.5% of US answers and none of the Canadian ones; we saw no Expertise.com page for a Canadian city. BBB was cited in 21.5% of Canadian answers against 9.8% in the US, and Reddit in 7.9% against 2.9%, a gap we cover in why Reddit is rarely cited by AI for local businesses (https://www.cheers.tech/geo-academy/why-is-reddit-not-getting-cited-by-ai). Canadian ChatGPT answers also leaned harder on contractor websites, 55.4% against 36.7% in the US, and on long-tail directories, 68.0% against 54.4%. ## What to do this week Pick five metros and one money question per trade, the kind a customer asks when they are ready to hire. Then spend the week on the sources that answer it. - Ask ChatGPT the question for each metro and save two things: the cited links and the business cards on the map. The free AI Visibility Grader (https://www.cheers.tech/visibility) gives you a one-location head start across ChatGPT, Gemini and Perplexity. - Open every directory page it cited: the Expertise.com list for your trade and city in US metros, HomeStars in Canadian ones, Angi, BBB and whatever "best of" sites came up. Check whether each branch is listed and whether the name, phone, service area and hours are right, and fix the profiles you control. - Open each location's Yelp page and read it the way a customer would, because in US metros it is often the rating ChatGPT puts on its map card. - Ask your web team to confirm that robots.txt allows OAI-SearchBot and that the CDN is not blocking OpenAI's published IP ranges. - Make sure each location has a service page with the city in it, so ChatGPT has something of yours to cite besides the homepage. If the same wrong phone number or closed branch keeps coming back, trace it with the business-information correction workflow (https://www.cheers.tech/geo-academy/fix-wrong-business-information-chatgpt). ## Methodology Every week, Cheers asks ChatGPT, Gemini, Perplexity and Google AI Mode the same 32 home-services questions in 100 metro areas across the United States and Canada, and records every business each engine names, with its star rating, review count and map position where the engine shows one, and every source it cites. This analysis covers the three weeks from August 31 to September 20, 2026: 34,742 usable answers (9,434 from ChatGPT, 9,435 from Google AI Mode, 9,364 from Gemini and 6,509 from Perplexity) across 60 US and 40 Canadian metros. We excluded empty or cut-off answers, most of them from a Perplexity collection outage. Each question was asked once per city per week, so week-to-week changes include normal answer-to-answer variation. Business names were extracted automatically; a hand check of 40 answers found the extraction caught about three in four of the businesses a reader would count, so counts are lower bounds. Star ratings and review counts are the figures each engine displayed; for ChatGPT and Gemini we captured them from September 18 and September 14 onward. Findings describe associations in this sample, not how any engine ranks businesses. Source groups come from hand-built domain lists. "Business website" means a domain that matches the website of a business named anywhere in the dataset, 12,667 domains, so it is a lower bound. Citation links were de-duplicated within each answer. Gemini citations were captured for only 4,141 of its answers, so Gemini shares use those answers as the denominator and a missing citation is unknown, not zero. The ChatGPT map-card figures come from September 18 to 22, the only days we captured those cards in full. Full definitions are on our research methodology (https://www.cheers.tech/research/methodology) page. ## Sources Every link below was opened and checked on September 23, 2026. - How ChatGPT and our foundation models are developed (https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed). OpenAI Help Center, updated August 2026. The three primary sources of training data, synthetic data, and the GPTBot robots.txt control. - Searching the web with ChatGPT (https://help.openai.com/en/articles/9237897-chatgpt-search). OpenAI Help Center, updated August 2026. Automatic search, query rewriting, search providers, approximate location, third-party business listings, maps, ranking and OAI-SearchBot eligibility. - Overview of OpenAI crawlers (https://developers.openai.com/api/docs/bots). OpenAI developer documentation. The difference between OAI-SearchBot, GPTBot and ChatGPT-User, and what opting out of OAI-SearchBot does. - How does ChatGPT conduct local searches? (https://searchengineland.com/how-does-chatgpt-conduct-local-searches-454894). Search Engine Land, May 2, 2025, by Damian Rollison. Side-by-side tests showing ChatGPT drawing on Bing web results without Bing Places profile data. A dated outside observation, not OpenAI documentation. - Introducing ChatGPT search (https://openai.com/index/introducing-chatgpt-search/). OpenAI, October 31, 2024. Partnerships with news and data providers for weather, stocks, sports, news and maps. - Cheers Market Baseline, August 31 to September 20, 2026: 32 home-services questions, 100 US and Canadian metros, 34,742 usable answers, aggregate only. Research methodology (https://www.cheers.tech/research/methodology). *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.* --- ## What Is Query Fan-Out in Google AI Mode? Slug: what-is-query-fan-out-google-ai-mode Category: AI Search News Published: 2026-05-31 Last updated: 2026-09-02 URL: https://www.cheers.tech/geo-academy/what-is-query-fan-out-google-ai-mode Google defines query fan-out in its own optimization guide: A set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results to address the user's query. Last verified: September 2, 2026. Google's worked example starts with "how to fix a lawn that's full of weeds" and fans out into things like best herbicides for lawns and removing weeds without chemicals. A local service request behaves the same way: one question about a failed water heater turns into several searches about the job, the market, the proof, and who is available. Google does not publish the exact hidden set for any given answer. The panel data does not separate the shapes Cheers can publish. Across the Cheers panel of 13 home-services companies, 28 days ending September 2, 2026, leaving out questions that name the company itself, aggregate only, broad "best or top" prompts without a place name returned a 28.4% pooled appearance rate and a 22.4% pooled citation rate. Prompts outside the named shapes returned 29.2% and 25.8%. Two shapes that close together do not support a ranking. "Best near me" and named-place prompts, price and cost, and how-and-why questions each sat behind fewer than ten companies, so Cheers does not publish a rate for them. The case for writing pages that answer the whole buying decision, instead of chasing one phrase, rests on how Google describes fan-out, not on a gap between prompt shapes. The cuts are in AI visibility statistics (https://www.cheers.tech/research/ai-visibility-statistics) and the sampling rules in the methodology (https://www.cheers.tech/research/methodology). ## What Google confirms Google says AI Mode and AI Overviews use core Search ranking and quality systems. Retrieval-augmented generation pulls current pages from the Search index. Query fan-out generates related searches. The system then shows supporting links for the response. That means the technical foundation still matters. A useful page has to be crawlable, indexed, eligible for a Search snippet, and linked clearly from the rest of the site. Google says there is no special AI schema or extra technical requirement. ## What local operators should do Write for the buying decision, not an imagined list of subqueries. A strong location page should state the services that branch truly provides, the area it serves, current contact and booking details, hours or availability, qualifications, and local proof. The location-page checklist (https://www.cheers.tech/geo-academy/what-should-location-pages-include-for-ai-search) covers that structure. Service pages should answer the parts of the job that change the buyer's choice, such as what is included, who performs the work, how estimates work, and what evidence supports the claim. Do not produce a thin page for every city-service wording. Google names the tactic and the policy directly, and then explains why it fails on its own terms: a high quantity of pages doesn't make a website higher quality or more relevant to users Creating separate content for every search variation or fan-out query, primarily to manipulate rankings or generative AI responses, violates Google's scaled content abuse spam policy. The safer version of that instinct is building city pages without doorway risk (https://www.cheers.tech/geo-academy/build-city-pages-without-doorway-risk-ai-search). ## How I audit a result I save the original prompt, AI Mode response, supporting links, market, and time checked. Then I compare the pages Google cited with the branch page and public business facts. If a competitor page answers a real buyer concern more clearly, improve the useful content. If a source contains the wrong location or hours, correct the source. Do not claim to have reverse-engineered the hidden query tree from one response. The AI engine source comparison (https://www.cheers.tech/geo-academy/ai-search-engine-source-differences) explains why AI Mode should be tracked separately from ChatGPT, Perplexity, and Copilot. Does SEO Still Work? (https://www.cheers.tech/geo-academy/does-seo-still-work) covers why the core Search work still matters. Use the AI Visibility Grader (https://www.cheers.tech/visibility) when you need a current baseline for your own business. ## Sources Every link below was opened and checked on September 2, 2026. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 2026. Source of the query fan-out definition, the lawn-weeds example, the scaled content abuse warning, and the note that structured data is not required for generative AI search. - AI features and your website (https://developers.google.com/search/docs/appearance/ai-features). Google Search Central. Eligibility, indexing, internal-link, and Search Console guidance for AI surfaces. - Scaled content abuse (https://developers.google.com/search/docs/essentials/spam-policies). Google Search Essentials spam policies. The policy Google names when it warns against a page per query variation. - Cheers panel, 28 days ending September 2, 2026 (https://www.cheers.tech/research/ai-visibility-statistics). 13 home-services companies, non-branded prompts, pooled appearance and citation rates by prompt shape, aggregate only (corrected September 28, 2026). Methodology (https://www.cheers.tech/research/methodology). *Dylan Allen-Arnegard (https://www.linkedin.com/in/dylan-allen/) is the CEO and Co-Founder of Cheers. I help multi-location service brands get recommended by AI search and Google, with results tracked by location, employee, and competitor.* --- ## What Should Location Pages Include for AI Search? 2026 Data Slug: what-should-location-pages-include-for-ai-search Category: Playbooks Published: 2026-05-25 Last updated: 2026-09-28 URL: https://www.cheers.tech/geo-academy/what-should-location-pages-include-for-ai-search A location page for AI search should be a page about one service in one city, carrying that branch's facts, proof and a way to book, because that is the kind of page AI engines cite. Of 74,969 citations that ChatGPT, Gemini, Perplexity and Google AI Mode gave to contractor websites while answering 32 home-services questions in 100 US and Canadian metros between August 31 and September 20, 2026, 43% went to homepages and 48% to service, location or service-plus-city pages. Blog posts got 1.5% and review or testimonial pages 0.7%. For a brand with 40 branches, that makes the page set a practical question. A single locations page that lists addresses gives the engines nothing to cite when a customer asks for the best water heater repair in one of your cities, and a blog post about water heaters barely registers. Last verified: September 23, 2026. The numbers come from the Cheers Market Baseline, which asks four AI engines the same questions every week; the method is at the end of this page. ## Which pages AI engines cite on contractor websites We sorted every cited contractor URL by its path. The homepage took 43.1% of the citations. Pages that named a service and a city together, such as a water heater page inside a city folder, took 17.1%. Service pages with no city took 16.9%, and location or city pages with no service keyword took 14.0%. Everything a marketing team tends to produce for "content" came last: booking, offer and financing pages 2.3%, blog and resource pages 1.5%, about and contact pages 1.2%, and review or testimonial pages 0.7%, with 3.2% left unclassified. The trade shifts the balance but not the order. In plumbing, 35.7% of the 25,033 cited URLs were homepages, 19.6% service pages, 18.3% service-plus-city pages and 16.4% location pages. In HVAC it was 45.8%, 17.3%, 15.4% and 10.5%, and in electrical 48.8%, 24.7%, 13.4% and 6.6%. Roofing, with two questions and 2,631 cited URLs, leaned on homepages at 67.7%. ## Each engine reads a different page The blended numbers hide a split between Google AI Mode and everyone else. Perplexity cited service-plus-city pages more than any other type, 28.6% of its 23,011 website citations, then service pages at 26.5%, homepages at 19.6% and location pages at 16.5%. Gemini was close behind: homepages 29.6%, service-plus-city pages 26.0%, service pages 23.1% and location pages 13.5% of 9,341 citations. ChatGPT sat in the middle, with homepages at 37.4%, service pages 24.1%, service-plus-city pages 17.4% and location pages 10.7% of 11,752 citations. Google AI Mode sent 66.9% of its 30,865 website citations to homepages, 13.5% to location pages, 5.7% to service-plus-city pages and 5.0% to service pages. Our read, which is an inference and not something the data proves, is that most of those homepage links come from the website button on each business's Google Business Profile. If your profiles all point at the brand homepage, Google AI Mode has no branch page to send people to. Where each profile should link is covered in linking a Google Business Profile to its location page (https://www.cheers.tech/geo-academy/google-business-profile-link-to-location-page), and Google's business links policy already requires it for booking and ordering links: For businesses with multiple locations, action links must lead to a website for a specific location. ## The page spec for a multi-location brand This is the page we would build for each service a branch actually runs, in the city it serves, and the evidence behind each part. ### Name the service and the city The URL, title and H1 should say the job and the place, for example /locations/denver/water-heater-repair. That is the page type Perplexity cited most (28.6% of its website citations) and Gemini second most (26.0%). One page per service the branch really performs is enough; a page for every suburb is not the goal. ### Carry the branch facts, word for word Branch name, address or a plain description of the service area, the local phone number, hours and the towns the crew covers. These should match the branch's Google Business Profile exactly, because the profile and the page are two copies of the same record, and an engine that finds two versions has to pick one. ### Answer the questions the engines answer Say what this branch does for this service: the scope, the brands it services, whether it takes emergency calls and when, how pricing works and what the warranty covers. The answers themselves talk about these things. For emergency questions, 93% to 99% of answers on every engine mentioned 24/7 or after-hours availability, against 34% to 61% for "best company for this job" questions, and for deal and quote questions ChatGPT and Gemini put a dollar figure in 62% and 68% of answers. Those are counts of what the answers mention, not proof of why a business was picked, so publish the facts that are true for the branch and leave out the ones that aren't. ### Put the proof on the page Recent reviews that mention this service in this city, photos of real jobs, the technicians who do the work and current licenses belong on the service-plus-city page itself. A standalone testimonials page took 0.7% of website citations and blog posts 1.5%, so proof parked there is proof the engines rarely read. ### Give it one way to book Link the same online booking page the branch's profile uses, and show the local phone number. Google AI Mode linked a business's online booking page in 31.2% of its answers, most often on Housecall Pro (20.5% of answers), ServiceTitan (10.6%) or Jobber (3.1%). That these links come from the Book button on each Business Profile is our inference. ChatGPT and Gemini never linked a booking page in this sample, and Perplexity did in 0.2% of answers. The trade matters. Across plumbing's 9 questions, 39.4% of Google AI Mode answers carried a booking link, HVAC's 9 questions 38.9% and electrical's 6 questions 33.3%. Roofing, with 2 questions, was at 1.8%. If your trade books online, the booking page is part of the location page's job. ### Mark up what the page shows Add LocalBusiness structured data for the branch with the same name, address, phone and hours the page displays. Google's AI features guide is explicit that nothing extra is needed for its AI surfaces: "You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities), as Google Search itself doesn't use them." The markup describes the page; it doesn't replace it. For multi-location markup patterns, see LocalBusiness schema for multiple locations (https://www.cheers.tech/geo-academy/localbusiness-schema-multiple-locations). ## What not to build A page for every town, with the city name swapped and nothing else changed, is the pattern Google's spam policies call doorway abuse. The policy, last updated August 28, 2026, lists pages "targeted at specific regions or cities that funnel users to one page" and "substantially similar pages that are closer to search results than a clearly defined, browseable hierarchy." Our data shows engines citing pages that name a service and a city; it does not show them rewarding a hundred near-identical copies. Strip out the local facts and proof and you have the doorway version. Building city pages without doorway risk (https://www.cheers.tech/geo-academy/build-city-pages-without-doorway-risk-ai-search) walks through where that line sits. ## What to do this week Pick one market and one high-value service, and get that single page to spec before you touch the rest. - Export every Business Profile's website link and booking link, and mark which ones point at the brand homepage instead of the branch page. - List the services each branch actually performs and check that each has its own page with the city in the URL, title and H1. - For the chosen market, rewrite the page to the spec above: branch facts matching the profile, service specifics, reviews and photos from that city, one booking link, and matching LocalBusiness markup. - Ask the four engines your money question for that city before and after the change, and save which of your pages they cite. The free AI Visibility Grader (https://www.cheers.tech/visibility) runs a one-location check on ChatGPT, Gemini and Perplexity. If you run dozens of markets and would rather have one team keep every branch page and profile in line, that is what we do in multi-location local SEO (https://www.cheers.tech/solutions/multi-location-local-seo). ## Methodology Every week, Cheers asks ChatGPT, Gemini, Perplexity and Google AI Mode the same 32 home-services questions in 100 metro areas across the United States and Canada, and records every business each engine names, with its star rating, review count and map position where the engine shows one, and every source it cites. This analysis covers the three weeks from August 31 to September 20, 2026: 34,742 usable answers (9,434 from ChatGPT, 9,435 from Google AI Mode, 9,364 from Gemini and 6,509 from Perplexity) across 60 US and 40 Canadian metros. We excluded empty or cut-off answers, most of them from a Perplexity collection outage. Each question was asked once per city per week, so week-to-week changes include normal answer-to-answer variation. Business names were extracted automatically; a hand check of 40 answers found the extraction caught about three in four of the businesses a reader would count, so counts are lower bounds. Star ratings and review counts are the figures each engine displayed; for ChatGPT and Gemini we captured them from September 18 and September 14 onward. Findings describe associations in this sample, not how any engine ranks businesses. Page types come from URL paths, first match wins. A homepage has an empty path, /, /index or /home. Review pages have "review" or "testimonial" in the path, and blog pages sit under paths such as /blog, /news, /resources or a dated folder. A service-plus-city page has a location folder such as /locations/ or /service-areas/, or the answer's city in the path, plus a trade or service keyword; a location page has the folder or city without a service keyword; a service page has the keyword alone. Citations were counted once per answer and only on domains that match a business named somewhere in the dataset. Full definitions are on our research methodology (https://www.cheers.tech/research/methodology) page. Before assigning the next page, use our guide to choosing a content format for AI search (https://www.cheers.tech/geo-academy/which-content-formats-ai-search-engines-cite-most) to separate local hiring questions from comparison and learning questions. Give the location owner a clear approval task: check the service promises in the body, title and structured data. The AI content review guide (https://www.cheers.tech/geo-academy/local-businesses-review-ai-generated-website-content-under) shows how to make that review part of publication. ## Sources Every link below was opened and checked on September 23, 2026. - Business links policies & guidelines (https://support.google.com/business/answer/13769188?hl=en). Google Business Profile Help. The rule that multi-location action links must lead to a page for a specific location, and that links must let customers complete the action. - Spam policies for Google web search (https://developers.google.com/search/docs/essentials/spam-policies). Google Search Central, last updated August 28, 2026. The doorway abuse examples quoted above. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 10, 2026. No special files or markup for Google's AI features; Business Profile data feeds them. - Local business (LocalBusiness) structured data (https://developers.google.com/search/docs/appearance/structured-data/local-business). Google Search Central, last updated September 8, 2026. The LocalBusiness markup reference. - Cheers Market Baseline, August 31 to September 20, 2026: 32 home-services questions, 100 US and Canadian metros, 34,742 usable answers, 74,969 citations to contractor websites, aggregate only. Research methodology (https://www.cheers.tech/research/methodology). *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.* --- ## Do Google Reviews Affect AI Recommendations? Slug: reviews-and-geo Category: Reviews Published: 2025-10-05 Last updated: 2026-09-28 URL: https://www.cheers.tech/geo-academy/reviews-and-geo On Google's own AI, Google reviews sit beside nearly every local recommendation, and the businesses it names have strong ones. Across 9,435 Google AI Mode answers to 32 home-services questions in 100 US and Canadian metros, collected by Cheers between August 31 and September 20, 2026, the engine showed a Google star rating and review count for 90% of the businesses it named, 83% of those businesses had 4.8 stars or more, and the business it named first had a median of 342 reviews against 195 for the third. ChatGPT works differently: it cited Google in 0.1% of its answers and leaned on Expertise.com, Angi and BBB instead. For a brand with 40 locations, that splits the review work in two. Google reviews are the evidence Google AI Mode puts beside each branch, while a separate set of review sites shapes how the same branch looks on ChatGPT and Perplexity. To see what each engine says about one of your locations today, run the AI Visibility Grader (https://www.cheers.tech/visibility). Last verified: September 23, 2026. Important: Everything below is an association in the answers we recorded, not a ranking formula, and it describes the businesses the engines chose to name. For the count to aim for, read how many Google reviews you need for AI recommendations (https://www.cheers.tech/geo-academy/how-many-google-reviews-do-you-need-for-ai). For the ChatGPT rating gap, read why ChatGPT shows a lower star rating than Google (https://www.cheers.tech/geo-academy/why-chatgpt-shows-lower-star-rating-than-google). ## What Google says reviews do Google is the only engine maker here that says outright that reviews affect where a business shows up. Its Business Profile help page on local ranking says prominence is "based on info like how many websites link to your business and how many reviews you have," and puts it in one line: "More reviews and positive ratings can help your business's local ranking." Its guide to generative AI features in Search, last updated July 10, 2026, says those features "are rooted in our core Search ranking and quality systems," and it points local businesses to their Business Profiles. Neither page says how Google AI Mode weighs a review. What they establish is that Google's local ranking uses reviews and that its AI features sit on the same core systems, which fits what we measured. ## Google AI Mode puts a rating next to almost every name Google AI Mode showed rated business cards in 90% of its answers, and the share barely moved with the kind of question: 90% for "best X for Y" questions, 92% for emergencies and 93% for deals. For 90% of the businesses it named, the card carried a Google star rating and review count. The ratings bunched near the top. Of the rated businesses it named, 82.7% had 4.8 stars or more, 12.8% had 4.5 to 4.7, 3.5% had 4.0 to 4.4 and 1.0% were under 4.0, with a median of 4.9. Review counts spread far wider, from 74 at the 25th percentile to 783 at the 75th, around a median of 226. Only 3.7% of answers named any business under 4.0 stars, while 23.5% named at least one with fewer than 20 reviews, so in this data a low rating was much rarer among the named businesses than a low count. ## The first business named had the most reviews The order of an answer followed review count on all three engines that show business cards. In Google AI Mode answers naming three or more rated businesses, the first had a median of 342 reviews, the second 237 and the third 195, and the most-reviewed business came first 36% of the time against 25% expected by chance. On ChatGPT's map cards, recorded September 18 to 22, the order ran 184, 139 and 125, with the most-reviewed business first 32% of the time against 19%. Gemini's cards, recorded September 14 to 22, ran 373.5, 277.5 and 222.5, with the most-reviewed first 33% of the time against 26%. Ratings barely moved by position: Google AI Mode's first-named business averaged 4.86 stars and its third 4.83. Review count also tracks how big and how old a company is, so read this as a pattern among the named businesses, not proof that reviews move a branch up the list. Consistency followed the count as well; 43% of businesses with 1,000 or more reviews were named again in at least two of three weekly checks, against 26% of those with fewer than 20. ## ChatGPT and Perplexity read reviews somewhere else Google reviews live on Google's own properties, and the other engines rarely cite those. Across 9,434 ChatGPT answers, ChatGPT cited Expertise.com in 32.1%, Angi or HomeAdvisor in 29.4%, HomeStars in 16.0%, BBB in 14.5%, Yelp in 0.6% and Google in 0.1%. Google AI Mode cited Google properties in 95.5% of its answers and businesses' own websites in 92.5%, with Yelp at 19.7%, Angi or HomeAdvisor at 9.3%, BBB at 1.7% and Expertise.com at 0.2%. Perplexity cited BBB in 31.0% of its answers, Expertise.com in 30.9%, Angi or HomeAdvisor in 30.8%, Thumbtack in 20.0% and Yelp in 13.9%. Counting every directory, review platform and "best of" list together, 84% of ChatGPT answers and 93% of Perplexity answers cited at least one, against 38% for Google AI Mode. Gemini is harder to read because we captured citations for only 44% of its answers, so I've left it out of this comparison. Country changes the list. Angi and HomeAdvisor were effectively absent from Canadian answers (1 of 3,775 Canadian ChatGPT answers cited them), and HomeStars took their place, appearing in 40% of ChatGPT's Canadian answers. BBB was cited about twice as often in Canada as in the US. ChatGPT also has a review channel that its citations don't show. About one in three of the rated business cards on its map linked to Yelp, and those cards ran lower than Google's; why ChatGPT shows a lower star rating than Google (https://www.cheers.tech/geo-academy/why-chatgpt-shows-lower-star-rating-than-google) has the numbers. ## The answers say the ratings out loud Ratings aren't only on the cards. When we asked which electricians, HVAC companies, plumbing companies or roofers "have the strongest reviews and fair pricing," 82.3% of Google AI Mode answers, 78.9% of ChatGPT answers and 78.0% of Gemini answers mentioned a star rating in the text, against 43.0% for Perplexity. Across every question we ask, ChatGPT mentioned a review count in 53.6% of its answers. A mention counts wherever it appears, including general advice, so read these as how often answers talk about ratings, not as the reason an engine chose a business. ## What it means for a multi-location brand The review work splits by engine, and the split shifts with trade and country. For an HVAC group in the US, Google reviews are what Google AI Mode shows beside each branch, and the HVAC businesses it named had a median of 449 of them; the same group's ChatGPT picture leans on Expertise.com, Angi and each branch's Yelp page. For a plumbing brand with Canadian branches, HomeStars turns up in ChatGPT answers in a way it never will in Texas. For a roofing company the directories carry even more of the load, since ChatGPT cited the contractor's own website in only 17.8% of its roofing answers. Every factor we measured across the four engines is on one page in AI local ranking factors for 2026 (https://www.cheers.tech/geo-academy/ai-local-recommendation-factors-2026). The rules differ by platform too. Google lets you ask every customer for a review and prohibits incentives, selective asking and staff review quotas, while Yelp asks businesses not to request reviews at all. The compliant review collection playbook (https://www.cheers.tech/geo-academy/compliance-playbook) covers the Google side in full. ## What to do this week Pick one market and the question customers ask most for your trade there. Ask it in Google AI Mode, ChatGPT and Perplexity, and for your branch and every business named ahead of it, write down the Google rating and review count, which review sites each engine cited, and whether ChatGPT's card links to Yelp. If your branch is missing from Google AI Mode and sits under 4.5 stars, fix the service complaint and reply to reviews before asking for more of them. If ChatGPT or Perplexity cites Angi, BBB or HomeStars for that question, make sure the branch's profile there is claimed and accurate. Then repeat the exercise in your next two markets with the same question, so the results stay comparable. ## Methodology Every week, Cheers asks ChatGPT, Gemini, Perplexity and Google AI Mode the same 32 home-services questions in 100 metro areas across the United States and Canada, and records every business each engine names, with its star rating, review count and map position where the engine shows one, and every source it cites. This analysis covers the three weeks from August 31 to September 20, 2026: 34,742 usable answers (9,434 from ChatGPT, 9,435 from Google AI Mode, 9,364 from Gemini and 6,509 from Perplexity) across 60 US and 40 Canadian metros. We excluded empty or cut-off answers, most of them from a Perplexity collection outage. Each question was asked once per city per week, so week-to-week changes include normal answer-to-answer variation. Business names were extracted automatically; a hand check of 40 answers found the extraction caught about three in four of the businesses a reader would count, so counts are lower bounds. Star ratings and review counts are the figures each engine displayed; for ChatGPT and Gemini we captured them from September 18 and September 14 onward. Findings describe associations in this sample, not how any engine ranks businesses. Ratings and review counts here are what each engine displayed on its business cards for businesses also named in the answer text: Google AI Mode for the full window (34,017 rated business appearances in 8,523 answers), ChatGPT map cards from September 18 to 22, 2026, and Gemini place cards from September 14 to 22, 2026. The order figures use the 7,873 Google AI Mode answers and 2,085 ChatGPT answers that named three or more rated businesses. The rating-mention figures use 1,130 Google AI Mode, 1,129 ChatGPT, 1,128 Gemini and 681 Perplexity answers to the four strongest-reviews-and-fair-pricing questions. Google AI Mode's cards can hide business names in about 5% of answers. Citation shares are the share of usable answers citing a domain at least once, with URLs de-duplicated within each answer. Text mentions come from pattern matching on the answer text; a star rating counts only when the number sits next to "stars", "/5" or "out of 5", so Google AI Mode's bare card ratings are not counted there. ## Sources - Cheers Market Baseline, August 31 to September 20, 2026, 34,742 answers from four engines in 100 US and Canadian metros. Cheers Research methodology (https://www.cheers.tech/research/methodology). Source of every rating, review, order, citation and text-mention figure above. - Tips to improve your local ranking on Google (https://support.google.com/business/answer/7091). Google Business Profile Help, undated, read September 23, 2026. Source of the prominence and review sentences quoted above. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, last updated July 10, 2026, read September 23, 2026. States that Google's generative AI features are rooted in its core Search ranking and quality systems. - Prohibited and restricted content (https://support.google.com/contributionpolicy/answer/7400114). Google Maps user-generated content policy, undated, read September 23, 2026. Source of the incentive, selective-solicitation and staff-quota prohibitions. - Don't Ask for Reviews (https://www.yelp-support.com/article/Don-t-Ask-for-Reviews?l=en_US). Yelp Support Center, undated, read September 23, 2026. Source of Yelp's rule against asking for reviews. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.* --- ## What Is GEO? Generative Engine Optimization Explained Slug: what-is-geo Category: Fundamentals Published: 2025-09-15 Last updated: 2026-10-19 URL: https://www.cheers.tech/geo-academy/what-is-geo Generative engine optimization (GEO) means improving how a business appears in AI answers; in our 13-company home-services panel, the median company appeared in 29.5% of its checks. That finding comes from the 28 days ending September 2, 2026, in our corrected AI visibility study (https://www.cheers.tech/research/home-services-ai-visibility-index-2026). It measures how often those companies were named, not the chance that another business will win a customer. For a local service brand, the work starts with a specific question in a specific market. Does the answer name the right branch? Are its hours and services correct? Which pages does it cite? Use those questions to decide what needs fixing before deciding what else to publish. You can start with the AI Visibility Grader (https://www.cheers.tech/visibility) for a one-profile snapshot. A repeatable program needs a saved set of buyer questions, the locations they represent, and a record of the answers over time. Last verified: October 19, 2026. ## What GEO measures A mention, a citation, and a booked job answer different questions. Keep them separate in the report you give a branch manager. Our research methodology (https://www.cheers.tech/research/methodology) defines an appearance as an answer naming or recommending the tracked company. A citation means the answer carries a source attributed to that company. A business can be named without being linked, which is why those rates need separate counts and labels. For an HVAC branch, a useful test might ask who services heat pumps in its actual market. Save the exact wording, engine, date, location context, answer, and cited URLs. Review whether the branch was named and whether the service description is accurate. The example is a testing plan, not an answer we observed. A booking belongs in a separate business-outcome record. Attribute it only when the referral, form, call, or customer record supports that connection. A rising appearance rate alone cannot establish new revenue. ## Read the benchmark with its denominator Our panel's median company appeared in 29.5% of its checks, with the 25th percentile at 12.8% and the 75th at 34.9%. Each of the 13 companies counted once when calculating those percentiles. The underlying window contained 958,754 checks across the questions, locations, and engines those companies tracked. These are historical observations, corrected September 28, 2026. They exclude questions that name the company being tested and exclude the test account described in the methodology. The sample clears our ten-company publication floor, but it is still a small customer panel with its own mix of prompts and markets. Use it to understand the measurement. Set your operating baseline from your own locations and a stable question set. A company running many easy branded questions and a company testing unfamiliar category questions are answering different research questions, even if both dashboards call the result a visibility score. The AI visibility score guide (https://www.cheers.tech/geo-academy/what-is-a-good-ai-visibility-score) explains how to read that comparison. ## How GEO and SEO fit together Google's guide to generative AI search, updated July 10, 2026, describes GEO as a term for work on AI-search visibility. Google treats optimization for its generative search experience as SEO, and says those features use its core Search ranking and quality systems. That keeps the first jobs familiar: useful service information, accessible pages, relevant internal links, and business details a customer can verify. Google's guide says its Search systems ignore llms.txt files and require no special AI markup. A file added to the website is therefore a poor substitute for a location page that still gives customers the wrong hours. For Google's current eligibility and reporting steps, follow that guide's linked Search Console instructions. Treat its rules as Google-specific. A measurement taken in one product cannot establish what a different engine showed. For the implementation layer, read How Local Businesses Can Show Up in Google AI Search (https://www.cheers.tech/geo-academy/how-local-businesses-can-show-up-in-google-ai-search) and What Is JSON-LD? (https://www.cheers.tech/geo-academy/what-is-json-ld). Structured data should describe the business facts the page actually shows. ## Follow the error to its source The useful output of a GEO audit is a fix someone can own. Record the observed problem, open the cited source, and compare it with the business's current operating facts before changing anything. Suppose a plumbing company's website lists an old service area. Confirm the current coverage with the business, then correct the page and any profile carrying the same error. If the cited page already states the correct coverage, preserve it and record the mismatch in the answer instead of changing a true fact to chase one response. For a thin location page, answer the customer's hiring question: which services the branch provides, where it works, how to contact it, and what evidence supports its claims. Use actual branch details. A copied city name does little for the customer deciding whether the business can take the job. Google's local-ranking guidance says complete and accurate Business Profile information helps it match a business to relevant searches. That supports maintaining the profile. It does not turn an individual profile edit into proof of an AI recommendation lift. For the source research, use What Sources Does ChatGPT Use to Give Recommendations? (https://www.cheers.tech/geo-academy/what-sources-does-chatgpt-use). Then assign the correction to whoever controls that page or profile, with a link to the before state and a way to verify the saved change. ## Where customer reviews fit Reviews help customers judge the business they might hire. Google says more reviews and positive ratings can help local ranking, while its review-request guidance prohibits incentives for reviews. Those are specific Google rules; they do not supply a universal review formula for AI answers. Keep a neutral, optional review request in the normal customer process, and use feedback to identify service problems worth fixing. The reviews and AI evidence guide (https://www.cheers.tech/geo-academy/reviews-and-geo) separates the observed relationships from claims about what an engine must reward. ## Start with one market this week Pick one branch and one service the business wants to grow. Write a small set of real hiring questions without adding the brand name, then save a baseline with the same engine and location context for each check. Keep a separate branded set if you also need to test whether AI describes the company accurately. Choose the customer-facing error with the clearest evidence. It might be outdated hours, missing service information, or an incorrect booking link. Confirm the fact with the business, make the source correction, and have someone else verify the resulting page or profile. Record the date and the source that changed. Repeat the original questions on a recorded schedule and compare like with like. Preserve answers that disagree with the expected result. Report whether the business was named, cited, and accurately described, while keeping attributable calls or bookings in their own column. That gives the next person a result they can inspect and a specific decision to make. For a longer operating sequence, use The GEO Playbook: Get Recommended by AI (https://www.cheers.tech/geo-academy/how-to-get-ai-to-recommend-my-business). Agree on a measurement schedule before promising a result date. The GEO timing guide (https://www.cheers.tech/geo-academy/long-generative-engine-optimization-take-work) separates source processing, repeated appearances and customer outcomes. ## Sources Sources read October 19, 2026. - Optimizing your website for generative AI features on Google Search (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google Search Central, updated July 10, 2026. Supports the relationship to SEO, Search-system foundations, llms.txt guidance, and Google-specific eligibility and reporting instructions. - Tips to improve your local ranking on Google (https://support.google.com/business/answer/7091?hl=en). Google Business Profile Help; no publication date shown. Supports accurate business information and the stated role of reviews in local ranking. - Tips to get more Google reviews (https://support.google.com/business/answer/3474122?hl=en). Google Business Profile Help; no publication date shown. Supports the prohibition on incentives for reviews. - Home Services AI Visibility Index 2026 (https://www.cheers.tech/research/home-services-ai-visibility-index-2026) and research methodology (https://www.cheers.tech/research/methodology). Cheers, 13 home-services companies, 958,754 checks, non-branded prompts, 28 days ending September 2, 2026; corrected September 28, 2026. Supports the appearance-rate distribution and measurement definitions. Company rates are calculated first, then summarized across companies; these observations do not establish an intervention's effect on bookings. *Dylan Allen-Arnegård (https://www.linkedin.com/in/dylan-allen/) is the CEO & Co-Founder of Cheers, the local search platform for multi-location service businesses.*