From referral chaos to clean pipeline in under 60 days
How we rebuilt the CRM and data foundation powering a 10-person company doing tens of millions in revenue.
The company
StoreLocators is the only retail intelligence platform that automatically discovers where a brand's products are sold and tracks live inventory across every location. They power omnichannel marketing for some of the fastest-growing consumer brands in the country, including BlendJet, Simple Modern, and True Classic, with real-time data feeds across more than 122,000 retail doors and 188 retailers including Walmart, Target, Costco, Kohl's, Whole Foods, and CVS.
Their growth model is unusual: a true product-led motion through their Shopify app, alongside multi-million-dollar enterprise contracts with the largest CPG brands in retail. That dual motion, freemium plus enterprise, is what most companies aspire to and few execute well.
The problem
We met StoreLocators at a conference earlier in the year. Growth was real, mostly through warm referrals from their network. The infrastructure underneath it wasn't keeping up. Three problems had stacked on top of each other.
The first was data. StoreLocators had interacted with more than 30,000 companies across pipeline, freemium signups, and inbound demos, but the records were thin, inconsistent, and missing the detail needed to act on them. The second was pipeline. Referrals were coming in faster than the team could route and qualify, and without clear ICP definitions, a Shopify-native challenger brand got the same treatment as a multi-billion-dollar CPG. The third was CRM. HubSpot had been set up early, before any of the current scale was on the table, and never re-architected. No documented intake. No standard stages. No clean handoff between the product-led motion and the enterprise motion.
People were doing the same work in different ways, which meant the data couldn't be trusted across the team. The CRM was making everyone's life harder, including the customer's.
The approach
We didn't start with a multi-month discovery phase. We started by getting into the actual data and the actual workflows.
The diagnosis was clear within the first week. The 30,000-company database was a goldmine that had been sitting unused because nobody could trust it. The CRM workflows were fragmented because nobody had ever mapped out what good looked like. And the personas the team actually wins with, brands in big-box retail, restaurants and food service, and franchise brands, had never been translated into qualification criteria the CRM could enforce.
The solution
The system has four layers:
- The data foundation. All 30,000+ companies enriched with firmographics, technographics, retail footprint, and contact-level data, then scored against the buyer personas StoreLocators actually wins with.
- The CRM rebuild. Full migration off HubSpot and onto Attio, with the entire pipeline architecture rebuilt from scratch. Every stage from first intake through closed-won got a clear definition, a clean handoff, and an enforceable set of fields. Product-led signups and enterprise deals now run as two coordinated tracks inside one system.
- The workflow layer. Every workflow mapped end to end, the way an architect maps a building before construction: who owns each step, what triggers the next stage, what data is required to move forward. The CRM enforces those workflows, so the data the team generates is finally trustworthy.
- The hygiene engine. Clean data on day one means nothing if it rots over the next twelve months. Ongoing enrichment, deduplication, and routing are built into the foundation, so the system gets fed daily and stays alive instead of going stale.
What had been 30,000 names became a tiered, scored list the team could actually work.
What came next
The work has held. StoreLocators continues to scale a true dual-motion go-to-market through the rebuilt infrastructure, with product-led signups and enterprise contracts running side by side without stepping on each other. Data hygiene has stayed clean because the workflows enforce it automatically. Multi-year deals close faster because reps spend their time selling instead of cleaning records.
What started as a rescue mission for a company growing faster than its operations could handle became the playbook we now run for any business straddling product-led and enterprise sales motions.