Glossary · Marketing Engineering
Incrementality Testing
also: geo-lift · conversion lift test
Definition
Incrementality testing measures the causal lift produced by a marketing intervention by comparing treated units to untreated control units, typically using geographic randomization (geo-lift), user holdouts, or ghost bids. It is the empirical gold standard for answering 'what did this spend actually cause?'
Geo-lift experiments split markets into treatment and control, deliver the intervention only to treatment, and measure the differential outcome. User-level conversion-lift tests randomize the auction bid, withholding ads from some users. Both produce true causal estimates if randomization is preserved. The result feeds into MMM calibration and attribution model validation.
Essays on this concept
- Marketing Engineering
Incrementality Testing at Scale: A Geo-Lift Framework for Measuring True Campaign Impact
Half your marketing budget is wasted. The classic joke, except now we can identify which half — geo-lift experiments measure what would have happened without the campaign, not just what happened with it.
- Business Analytics
Bayesian A/B Testing in Practice: When to Stop Experiments and How to Communicate Results to Non-Technical Stakeholders
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?'
- Marketing Strategy
Brand vs. Performance: A Portfolio Optimization Framework Using Markowitz Theory for Marketing Budget Allocation
Finance solved the allocation problem in 1952. Marketing still argues about it in 2026. Markowitz's portfolio theory — applied to marketing channels instead of stocks — reveals an efficient frontier that makes the brand-versus-performance debate quantitatively resolvable.
- Marketing Engineering
Causal Impact of SEO on Branded Search: A Synthetic Control Method for Organic Channel Measurement
SEO is the only major marketing channel where practitioners still argue about whether measurement is even possible. Synthetic control methods borrowed from policy economics prove it is — and the results will surprise you.
- Marketing Engineering
Marketing Mix Modeling in the Privacy-First Era: Bayesian Structural Time Series Without User-Level Data
Cookies are dying. Deterministic attribution is shrinking. The irony: the measurement approach from the 1960s — Marketing Mix Modeling — is making a comeback, now powered by Bayesian inference that would have been computationally impossible when it was first invented.
- Marketing Engineering
Unified Measurement Architecture: Connecting MMM, MTA, and Experimentation Into a Single Source of Truth
MMM says Facebook works. MTA says Google works. The incrementality test says neither works as well as you thought. Three measurement systems, three different answers — here's how to reconcile them into one coherent picture.
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