Eureka Moments

A travel brand needed an updated marketing mix model and an interactive planning tool. Three sophisticated specs failed to identify channel effects in a short, correlated panel. The version that shipped was the most conceptually humble one — and it works.

A financial-data client needed two new attributes classified across more than ten thousand firms. A 100-record calibration pass found structural problems that would have rendered the entire production run directionally wrong.

A healthcare data client needed practice-level information for thousands of specialty physicians. The dataset wasn't for sale — because nobody had built it. We did, with an AI pipeline.

A developer-productivity platform was tracking everything — and still couldn't tell its customers which of their teams were quietly fragile. We brought an idea from outside software, and it shipped as a product feature.

A consumer brand was sending 37% of its media to zip codes that produced 6% of its sales. A continuous prioritization layer fixed it without changing CPMs — cutting wasted delivery from 37% to 7% on the same budget.

A building-materials manufacturer was discounting heavily and competitively. The data said most of those discounts weren't doing anything — close rates were flat across a wide middle range. Significant margin recovery, with no measurable impact on win rate.

A private equity team needed a defensible TAM for a healthcare services target. Monte Carlo simulation replaced the standard point-estimate with a full distribution — most-likely $231M, plausible range $173M to $377M.

A luxury travel brand had shifted significant media weight to a new product line. Three independent measurement reads — web behavior, lead regression, and Monte Carlo simulation — converged on the answer.

A Major League franchise needed a defensible price tag for the social, digital, and email assets in its partnership deals. We built one by reverse-engineering value from the deals it had already signed.

A medical device sales team had thousands of open opportunities and finite rep capacity. A classification model put the highest-likelihood ones at the top of the queue — the top decile closed at 86%, the bottom at 10%.

A specialty pharma launching into a generic-dominated category narrowed a 247K-prescriber universe to a 25K target list. The real intelligence wasn't volume — it was the gap between expected and actual branded share.

A Major League franchise wanted to put a real number on the value of an Instagram post. Econometric modeling produced a defensible per-follower value — and a framework that could be re-run as the data evolves.

An emerging professional sports franchise wanted to know which neighborhoods to court next. A zip-code-level penetration framework spanning 1,548 zips revealed where the next thousand fans would come from.

A multi-country travel platform was forecasting bookings by multiplying spend by a ratio. The math was making decisions worse, not better. Replacing it with country-level response curves changed how budget got allocated across markets.

A workforce-services firm needed incentive plans for 25 different roles spanning four business units. The hard part wasn't the math — it was building one common architecture that worked across all of them.

A caregiver-support program wanted to predict which consumers would consume disproportionate staff time. The standardized assessment data — designed for exactly this purpose — barely moved the needle. The honest answer mattered more than a fitted model.

A direct-to-consumer brand needed to defend brand media that wouldn't show up in any standard ROAS report. Three measurement layers — brand metrics, direct sales lift, and indirect impact on response media — produced an honest picture of contribution.

Top-down forecasts tell you the total. A data-driven goaling model tells you how to split it across territories in a way that's defensible, motivating, and grounded in what the data actually predicts.

How do you tell a struggling store from an untapped one? For a national specialty retailer, the answer was a store penetration framework that evaluated each location against the household opportunity within its catchment area.

A B2B distributor needed reliable revenue potential estimates for 230,000 accounts — most with limited transaction history. Two statistical models, working in tandem, produced a single defensible score for every account in the universe.

Territory goals work for some teams. Account-level goals work for others. Separating baseline from growth made both expectations and accountability clearer for leadership and reps alike.

A national hospitality brand was spending across eight media channels with limited visibility into what was working. Marketing mix modeling provided channel-level attribution — and surfaced a few surprises worth acting on.

A streaming service wanted to know which channels were moving the needle across 13 tentpole shows. A marketing mix model revealed where the real efficiency was — and where budget could be working harder.