ClearCOGS is creating a new category in restaurant technology by bringing predictive analytics to an industry that operates almost entirely on retrospective data. With $3.8 million raised, the company analyzes 100 million data points daily per restaurant to forecast demand and optimize prep decisions. In a recent episode of Category Visionaries, we sat down with Matt Wampler, CEO and Co-Founder of ClearCOGS, to explore how his experience turning around failing Jimmy John's franchises led him to build forecasting software that's fundamentally changing how restaurants operate—and how he's defining a category that doesn't yet exist.
Topics Discussed:
- Matt's transition from 21-year-old Jimmy John's franchisee working 110-hour weeks to identifying systematic inefficiencies in food prep decisions across five locations
- Why restaurants remain stuck in reactive mode while sports betting and fantasy football have sophisticated predictive analytics
- ClearCOGS's data infrastructure processing 100 million variables daily—from 15-minute POS intervals and weather patterns to dew point and local events
- The product discovery process where Matt's co-founder kept asking "why" until every feature request collapsed into one core problem: uncertainty about tomorrow's demand
- Category creation through the Restaurant AI podcast despite no clear attribution model
- Building in public on LinkedIn as an enterprise lead generation channel that landed major brands within six weeks
- The ICP evolution from enterprise fast-casual chains (15-1,000 locations) to a freemium Toast integration targeting independents
GTM Lessons For B2B Founders:
- Let outsiders interrogate your domain expertise: Matt wanted to build dashboards restaurant operators requested. His technical co-founder repeatedly asked "why do you want that dashboard?" then "why do you need to see that?" Every answer eventually reached the same root cause: operators didn't know who would walk in tomorrow, making food prep, ordering, and staffing decisions inefficient. This pattern held across dozens of restaurant brands. The yin-yang of insider knowledge plus relentless outside questioning revealed the actual problem worth solving versus building a feature graveyard of requested tools.
- Reframe category education through familiar high-stakes analogies: "Predictive analytics" meant nothing to restaurant operators. Matt's breakthrough was pointing out the cognitive dissonance in their lives: they studied dozens of variables and probabilistic forecasts for fantasy football lineups but ran six-figure businesses on Excel sheets and gut instinct. This wasn't explaining predictive analytics—it was exposing the absurdity of having better forecasting tools for fantasy sports than for their livelihood, making the gap visceral and the solution obvious.
- Convert forecast errors into customer intelligence touchpoints: When ClearCOGS's predictions missed, the team initially spent weeks reoptimizing algorithms. The pivot: immediately call the customer, acknowledge the miss, and say "we're on it." Customers didn't expect perfection from a system replacing Excel and guesswork—they valued having someone actually watching their operation. In a software landscape where vendors disappear post-sale, proactive error acknowledgment became relationship acceleration. Every miss became an opportunity to demonstrate attentiveness that competitors couldn't match.
- Segment messaging by incentive structure, not org chart: ClearCOGS discovered the messaging split wasn't finance versus operations—it was franchisors versus franchisees. Franchisors earning royalties on top-line revenue needed consistency and scalability messaging. Franchisees and on-ground operators living on bottom-line profitability needed waste reduction and margin improvement messaging. The same product solving the same problem required different value propositions based on how buyers were compensated, not what department they sat in.
- Test public vulnerability as enterprise sales acceleration: Matt had zero social media presence before ClearCOGS. He started posting about struggles and failures on LinkedIn. Within six weeks, a major restaurant brand reached out for partnership discussions. Later, he posted their first website draft asking for brutal feedback—50 people responded with detailed reviews, video walkthroughs, and unsolicited legal advice. When he launched the Restaurant AI podcast with unclear ROI, he treated it as category education infrastructure. In oversaturated B2B markets, authentic struggle documentation cuts through polished competitor noise and creates asymmetric enterprise access that paid channels can't replicate.
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