Cheers

What do they actually do

Cheers helps multi-location service businesses get recommended in AI assistants and local search by creating the proof those systems look for: recent reviews, consistent citations, and correct structured data. They start with a GEO audit to identify gaps and produce a prioritized plan across listings, JSON‑LD schema, and review channels (site, YC).

They ship low-friction frontline capture tools (NFC badges, QR/mobile flows) so employees can turn positive in-person interactions into public reviews on third‑party sites, with attribution down to the employee and location in dashboards. Cheers also fixes citations and schema, routes reviews to the sites assistants cite, and monitors how often assistants mention the business and on which queries (site, launch post). Public examples include Vivint Smart Home deploying across hundreds of locations and generating large review volume as a proof point (site, YC).

Who are their target customer(s)

• Operations leader at a multi-location home‑service or retail chain (regional ops/VP): Inconsistent local visibility and reviews across dozens or hundreds of locations make lead flow unpredictable from AI assistants and local search. They also lack a scalable way to fix listings, schema, and citations that cause some locations to be effectively invisible (site, about).
• Local store/branch manager: They don’t have an easy, repeatable way to capture feedback at the point of service, so good visits don’t become public reviews. Without attribution, they can’t see which employees drive reputation or where to coach (site).
• Head of local SEO or digital marketing for a chain: Keeping JSON‑LD, citations, and listings correct across many locations is manual and error-prone, and AI assistants increasingly surface businesses based on these signals, making outcomes volatile without automation (about).
• Field/frontline employee (technician, server, associate): Existing review flows are clunky and lose momentum after the visit. They also don’t get clear credit for reviews they influence, so motivation to ask is low (launch post).
• Franchisee or area owner: They rely on corporate or vendors to fix incorrect listings and improve discovery, which is slow and inconsistent. As assistants start recommending directly, weak local presence cuts bookings and revenue (site, about).

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder-led, high-touch pilots using the Vivint case as social proof; offer a free or discounted GEO audit and a small NFC/QR badge rollout to prove review velocity and attribution, with hands-on onboarding to produce a measurable case study (about, site).
• First 50: Stand up outbound to ops leaders in home services, hospitality, and retail with a productized “GEO audit + 30‑day pilot,” standardized badge kits, and templated onboarding; amplify with LinkedIn, trade shows, and partners in local‑SEO/franchise consulting (YC, launch post).
• First 100: Pursue channel and integrations (franchise/POS/platform vendors) to resell Cheers, launch a self‑serve tier for smaller chains, and scale onboarding via automated citation/schema fixes and monitoring; fuel demand with case studies, paid social, and marketplace listings (about, site).

What is the rough total addressable market

Top-down context:

The addressable base is large: the U.S. alone has millions of employer establishments, including more than 2.0 million establishments that belong to multi‑unit enterprises, and franchised units are expected to surpass ~821,000 locations in 2025 (U.S. Census SUSB, Forbes citing IFA). Related software budgets are multi‑billion‑dollar categories: local SEO software (~$8.7B in 2024) and online reputation management (~$13.2B in 2024) (MRFR local SEO, MRFR ORM).

Bottom-up calculation:

Initial SAM: assume 100,000 relevant North American service locations across home services, hospitality, and retail; at ~$50/location/month, that’s ~$60M ARR. Broader TAM: at 1,000,000 consumer‑facing multi‑unit locations globally at ~$40/location/month, TAM is roughly ~$480M ARR.

Assumptions:

• Focus on multi‑location, consumer‑facing service categories (subset of all multi‑unit establishments).
• Average price point of $40–$60 per location per month for reviews, citations, schema, and visibility monitoring.
• Adoption limited by operational readiness (frontline tooling rollout) and need for third‑party review site routing.

Who are some of their notable competitors

• Yext: Enterprise listings and knowledge‑graph platform to centralize location data, publish schema/JSON‑LD, and manage reviews; overlaps on citations/schema at scale but not on frontline NFC/QR capture with employee‑level attribution.
• Reputation.com: Enterprise reputation suite spanning listings, review generation/monitoring, and analytics for multi‑location brands; stronger in enterprise workflows than in on‑the‑ground capture hardware.
• Birdeye: Broad CX/reputation and listings platform with an emphasis on AI‑driven review generation and “search AI” visibility; closest on the AI‑visibility goal but less focused on employee‑level capture + attribution as a wedge.
• Podium: Messaging‑first customer interaction platform that converts in‑person visits to reviews via SMS/web flows; overlaps on review capture but lighter on citations/schema automation and AI visibility monitoring.
• BrightLocal: Local‑SEO and citation toolset for auditing/fixing listings and tracking local rankings; more of a DIY toolkit than a frontline capture + employee attribution platform.