Practice area · Kansas City and remote
Most dashboards show what moved together. Causal inference asks what would have happened if you had made a different decision, which is the question a budget owner actually needs answered. Vector 1 builds marketing mix models, causal graphs and experiments that separate real effects from coincidence, and reports every estimate with an honest range.
Tell us what you're trying to measure, forecast or decide. We work with teams in Kansas City and remotely, and we reply within two business days.
Email [email protected]See what each marketing channel really returns, with honest uncertainty, and plan a weekly budget against a simulation where the true answer is known.
Open demo →Discover a causal graph from data and see how far the naive correlation is from the real effect of speeding up a page.
Open demo →Three common ways to measure an AI pilot, side by side on data where the true effect is known, and what each one would tell your CFO about ROI.
Open demo →
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Marketing mix modeling (MMM) is a statistical model of how each marketing channel, plus price, promotions and seasonality, contributes to sales over time. It uses aggregate weekly or daily data, so it doesn't need user-level tracking, and it estimates carryover (adstock) and diminishing returns (saturation) to support budget decisions.
Attribution and correlation describe which touchpoints and outcomes occur together. Causal inference estimates what an outcome would have been under a different decision, using experiments, quasi-experiments such as difference-in-differences, or models that adjust for confounders. That difference is why two channels can look equally good in a dashboard and have very different true returns.
Yes. Vector 1 is based in Kansas City and works with teams in the metro and remotely.