Provalytics was built for a world where cookies are disappearing, spikes no longer tell the full story, and finance needs an answer it can trust. Our methodology combines Bayesian inference with Seemingly Unrelated Regressions to measure true incremental impact across channels, outcomes, and time.
At the core of Provalytics is a machine learning framework designed to measure what marketing actually drives. We analyze the natural variation already present in your campaigns — changes in spend, timing, creative, placement, daypart, and channel mix — and use that variation to identify what is truly moving outcomes.
We use Hierarchical Bayesian estimation with Seemingly Unrelated Regressions so multiple KPIs and media variables can be modeled together, instead of forcing a narrow channel-by-channel view.
The model detects thousands of micro-experiments already occurring in your campaigns — variations in flighting, creative, station, placement, spend, and sequencing — to isolate incremental impact from baseline behavior.
Whether media runs live, asynchronously, on-demand, on-site, or through marketplaces like Amazon, Walmart, and Chewy, Provalytics is designed to measure what legacy tags, cookies, and spikes can no longer see.
A model is only useful if it holds up against reality. Provalytics validates accuracy against your actual business outcomes, not just model fit in isolation.
We use cross-validation, holdout logic, and real-world response comparisons to ensure the model is measuring signal, not noise. The output is granular, explainable, and designed for teams that need to make and defend budget decisions.
See how it works on your data →Consumers do not move in neat, trackable paths anymore. They stream, browse, compare, delay, buy off-site, and convert across marketplaces and devices. Provalytics was built to measure that reality — not an older one.
Measurement does not break when pixels disappear, browsers restrict tracking, or marketplace data remains closed.
CTV, linear, radio, podcast, retail media, and digital can be evaluated side by side inside one consistent framework.
Not black-box averages. Not static MMM reports. Clear, actionable insights tied to how campaigns are actually planned, bought, and managed.
A practical field guide for proving impact when the cookie is gone, the click is weaker, and finance needs one answer. Includes examples from retail, CPG, financial services, and subscription brands, plus the frameworks behind a finance-ready measurement rollout.
A field guide for marketing leaders who need to defend every dollar.