Where behavioral science meets
data-driven strategy

Frequentist A/B testing answers a question nobody asked: 'If the null hypothesis were true, how surprising is this data?' Bayesian testing answers the question that matters: 'Given this data, what's the probability that B is actually better?'

In digital markets, the marginal cost of adding one more product to a bundle is zero. Conventional wisdom says bundle everything. The data says the opposite — past a threshold, each addition dilutes the bundle's perceived value and total willingness to pay drops.

An empirical examination of default effects in digital commerce — from Thaler and Sunstein's nudge theory to the precise mechanics of how pre-selected options generate billions in revenue most consumers never consciously chose to spend.

Your Q1 OKR was 'increase activation rate by 15%.' It's March and you're at 3%. The problem isn't execution — it's that activation rate is an output. You can't execute on an output. Input metrics bridge the gap between strategy and daily action.

Prospect theory predicts that losses hurt 2.25x more than gains. Our data across 14 million marketplace users shows the real ratio depends on something economists have overlooked.

Your company's monthly revenue is growing 20% year-over-year. Your unit economics are deteriorating. Both statements are true simultaneously — and you'll never see the second one in an aggregate P&L.

Brands don't compete for preference. They compete for mental availability — being thought of in the buying situation. Category Entry Points are the specific occasions, needs, and contexts that trigger category thoughts. Most brands measure the wrong ones.

'You'll know product-market fit when you feel it' is advice that has burned through billions in venture capital. Here's a quantitative framework that replaces gut feeling with a composite score — and it starts with retention curves, not surveys.