Show, don't tell

See exactly what Outmatch writes for businesses like yours.

Real (fabricated, privacy-clean) examples across 6 verticals. Each email references something specific about the prospect — a job, a review, a local market signal. No templates, ever.

🔍 Personalization hook

Pulled from Storm Scouts public weather alert data for zip 85351 and cross-referenced with the prospect's Google Business listing, which lists HOA property management in the East Valley. The 14-contract detail came from the prospect's own website portfolio page. Outmatch's research engine surfaces all three in under 90 seconds.

Full sequence — auto-pauses when Eric replies

Day 0
Opener
Storm alert in their zip + portfolio exposure angle
Day 3 — FU1
Reframe
"Circling back — insurance window is tight" + reference to opener thread
Day 7 — FU2
Case study
HOA in Chandler: 3 buildings, $40K scope, 8-day turnaround — "similar portfolio size"
Eric replied — sequence paused
Day 14 — FU3
Soft close
Final ask — skipped because Eric already replied
📬 What happened

Eric replied after FU2: "Actually yes — can you get out to the Greenfield Estates location this week?" Sequence auto-paused. FU3 never sent. Outmatch flagged it as a Hot Reply and surfaced it in the dashboard for Tom to action.

🔍 Personalization hook

Pulled from High Desert PM Group's Google Business listing — a tenant review mentioned ceiling discoloration after rain at a specific property. Outmatch flagged this as a "structural maintenance signal" and routed it to Summit Ridge's campaign targeting Phoenix-area commercial and multi-unit owners.

🔍 Personalization hook

Referenced a public permit filing from the City of Phoenix's permit search portal. Outmatch monitors permit databases daily and matches new commercial filings to prospect lists — surfacing renovation and construction projects before competitors even know they exist.

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🔍 Personalization hook

Triggered by a commercial lease announcement in the Colorado Springs Business Journal. Outmatch cross-referenced the property address with the company's public LinkedIn page (which mentioned the "new facility" in a company update post), and flagged the building class from Loopnet records. The 7-facilities-in-the-corridor stat came from the HVAC company's own job history — Outmatch structures your past work as a signal source.

🔍 Personalization hook

Pulled from Norris Property Group's Yelp listing — a February tenant review mentioned HVAC inconsistency at a named property complex. Outmatch's research engine scans Google and Yelp reviews for maintenance signal language ("HVAC", "heat", "AC", "inconsistent", "broken") and routes the prospect to trades contractors who can solve the problem.

🔍 Personalization hook

Referenced a real (fabricated for this example) regulatory change affecting senior living facilities and cross-referenced with the prospect's Colorado state business license (which shows facility type). Outmatch monitors regulatory calendars for changes that create urgency for specific industry verticals — then routes the signal to contractors who can solve the compliance problem.

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🔍 Personalization hook

Triggered by a public City of Columbus infrastructure work notice. Outmatch monitors municipal project boards and cross-references with property manager portfolios — when a city project overlaps with a prospect's known properties, it generates an urgency-based outreach. The Arbor Court and Elm View names came from the prospect's own website property listings.

🔍 Personalization hook

Pulled from a tenant review on Stonebrooke Apartments' Yelp listing. Outmatch scans reviews for plumbing signal language — "pressure," "drain," "leak," "running water," "hot water" — and generates targeted outreach for the contractor. The floor-unit specificity in the email came directly from the review text, which builds credibility with the recipient immediately.

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🔍 Personalization hook

Triggered by an RFP post on a public NextDoor community page. Outmatch monitors local community boards, Nextdoor public posts, and municipal RFP databases for service requests. Cross-referenced with county water restriction schedules (which are public records) to add a compliance urgency angle the prospect wasn't expecting.

🔍 Personalization hook

Triggered by a public LinkedIn post announcing a new development phase. Outmatch monitors LinkedIn company pages and activity feeds for construction and expansion signals — then generates outreach timed to the pre-groundbreak window when vendors are still being selected. The county green-building credit angle came from Pierce County's public incentive program database.

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🔍 Personalization hook

Triggered by a Denver municipal code update (public record). Outmatch cross-referenced the effective date and scope with the property management company's portfolio — which listed building addresses that Outmatch matched against the city's pre-1990 building registry to infer panel age. The cost savings figure ($2,000) came from the electrical contractor's own past project data, structured as a campaign context input.

🔍 Personalization hook

Referenced fleet operations mentioned on the prospect's own website (tenant listing page). Combined with Colorado's public EV tax credit program (CDPHE public database) to build an ROI-based angle. The 50% credit figure and 18–24 month payback math came from real state program data, not invented — which makes the email verifiable and builds immediate credibility.

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🔍 Personalization hook

Pulled from a specific Yelp review that praised the food but cited a long phone wait. Cross-referenced with the restaurant's Yelp listing to count recent 5-star reviews (143 in 90 days). The "15–20 calls per hour" estimate comes from BiteUp's benchmark data for independent pizzerias in the NYC metro on weekend evenings.

🔍 Personalization hook

Google Business listing rating timeline showed the drop from 4.2 to 3.8. Three recent 1-star reviews were scanned for common language — all mentioned unanswered calls. Cross-referenced with a local storm advisory API that flagged flood damage in the diner's zip code last Tuesday. BiteUp's engine connected the dots: storm → closure → unanswered phones → bad reviews.

🔍 Personalization hook

Local food blog (Austin Eats Blog) review cited specific praise and a phone-related complaint. Google reviews scanned for recurring "chaos" and "wait time" language on Friday/Saturday nights. Cover count (237) estimated from OpenTable and Google Popular Times data for a 90-seat restaurant running 2.5 turns on a Saturday. BiteUp's engine builds the case from the prospect's own public footprint.

🔍 Personalization hook

Pulled from a specific Google review mentioning an order mistake on Friday night. Cross-referenced with the diner's Google Business profile showing peak hours and recent reviews mentioning short-staffed weekends. The $380 walkout figure was cited in the review itself. BiteUp's engine flags "order error" and "walkout" language as high-intent signals for restaurants running phone-heavy operations.

Full sequence — auto-pauses when Kevin replies

Day 0
Opener
Google review about order screw-up + walkout revenue loss angle
Day 3 — FU1
Reframe
"Came across another review" — order management stats + server stress angle
Day 7 — FU2
Soft close
Referral to nearby restaurant using BiteUp for call overflow during rushes
Kevin replied Day 9 — sequence paused
📬 What happened

Kevin replied Day 9 after FU2: "OK I'm interested — can you set up the demo for next Tuesday before our dinner rush?" Sequence paused. BiteUp flagged it as a Hot Reply and surfaced it in Sarah's dashboard for immediate follow-up.

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