Where behavioral science meets
data-driven strategy

Binary churn models answer the wrong question. 'Will this user churn?' matters less than 'When will this user churn?' Survival analysis models the timing — and the when determines whether intervention is profitable.

Your bidding algorithm gets better every quarter. Your brand gets weaker every year. This is not a coincidence — it's Goodhart's Law applied to marketing, and the compounding damage is invisible until it's too late.

A 4% revenue drop on a Tuesday could be a payment processor outage, a pricing bug, or just normal variance. The difference between these explanations is millions of dollars — and your monitoring system can't tell them apart.

A single well-written article generates traffic for years. That makes content a capital asset, not an operating expense — and like any capital asset, it depreciates. The companies that model this correctly build content moats that compound. The rest produce content that decays.

Economists call it irrational. Product managers call it retention. The sunk cost fallacy — when properly channeled through customization and effort investment — becomes the most reliable predictor of long-term user engagement.

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.