E-commerce Statistics - Best For Ecommerce

Incrementality test adoption measures how often ecommerce and retail media teams use experiments, holdouts or lift studies to validate whether marketing spend creates net-new sales. This page summarizes practical adoption benchmarks and test types. Back to the hub: E-commerce Statistics. This page belongs to the Attribution & Measurement silo. For measurement context, compare it with attribution model usage share, incrementality test adoption, view-through conversion share, multi-touch attribution benchmarks, cookie-loss impact benchmarks, ROAS benchmarks, MER benchmarks and LTV to CAC benchmarks. Metric: Share of marketers using incrementality tests and experiments Scope: E-commerce, retail media, paid social, search, CRM and performance marketing measurement Updated: 2026-05-31 Category: Attribution, incrementality and budget validation Key benchmarks Test types How to use this data Methodology note Sources Benchmarks Incrementality test adoption benchmarks Incrementality testing is increasingly used to answer whether marketing spend creates net-new revenue, not only whether a platform can attribute an order to an ad impression or click. US brand/agency usage 52% EMARKETER reported that over half of US brand and agency marketers use incrementality testing and experiments to measure campaigns. Retail media challenge 36% Skai reported difficulty proving investment incrementality as a top challenge that could reduce retail media investment. Best-fit use case Budget validation Incrementality is most useful when platform ROAS, last-click revenue and blended revenue tell different stories. Benchmark Reference point How to interpret it Incrementality testing adoption 52% of US brand and agency marketers use incrementality testing and experiments A majority-level adoption signal, but many teams still rely mainly on platform attribution. Retail media incrementality challenge 36% cite difficulty proving investment incrementality as a top challenge Retail media growth increases the need to separate attributed sales from net-new sales. Recommended testing cadence Periodic tests, not constant testing for every campaign Run tests around budget changes, new channels, major promos and large retargeting pools. Common test formats Geo holdout, audience holdout, PSA/control, lift study, matched-market test The right method depends on spend level, market structure and platform support. Test design Common ecommerce incrementality test types Test type Best for Caution Geo holdout Large campaigns where markets can be separated geographically Matched markets must be similar enough to avoid biased lift estimates. Audience holdout Retargeting, CRM, app and platform-managed audiences Audience leakage and overlapping campaigns can contaminate results. Conversion lift study Meta, Google, retail media and large paid-media tests Platform methods are useful, but store revenue should still be reconciled. PSA or ghost-ad test Understanding ad exposure without serving a real treatment ad Requires specialist setup and enough volume. Before/after test Small stores with limited test tooling Weakest design because seasonality and external factors can distort results. Definition: incrementality test adoption measures whether an ecommerce team uses controlled experiments or holdouts to estimate the net-new sales, revenue or profit caused by a marketing action. Usage How to use incrementality adoption benchmarks Use incrementality benchmarks to decide when attribution is no longer enough. The highest-priority tests are usually retargeting, branded search, retail media, discount campaigns, affiliate deals and broad-reach awareness campaigns because these areas can report strong attributed revenue while capturing demand that may have converted anyway. Pair this dataset with ROAS benchmarks, MER benchmarks and multi-touch attribution benchmarks. Methodology Methodology note Attribution and measurement benchmarks are not universal constants. They change by platform, consent rate, cookie availability, app/web mix, lookback window, attribution model, return policy, product category, average consideration time and whether the report counts modeled conversions. Use these figures as directional reference points and always reconcile platform-reported conversions against store revenue, orders and margin. Sources Sources and notes Use these sources as directional benchmarks. Measurement statistics should be interpreted together with your analytics setup, consent mode, platform attribution windows and first-party order data. EMARKETER: FAQ on incrementality — reports that over half of US brand and agency marketers use incrementality testing and experiments to measure campaigns. Skai: 2025 State of Incrementality in Retail Media — retail media measurement context and incrementality challenges. Appier: The incrementality imperative — overview of geo tests, matched markets and incrementality measurement methods. Stella: Guide to measuring true marketing impact — context on combining MMM and incrementality testing for marketing measurement. Cite this page How to cite this dataset E-commerce Incrementality Test Adoption. Best For Ecommerce. Updated 2026-05-31. Available at: https://bestforecommerce.com/ecommerce-statistics/attribution-measurement/incrementality-test-adoption/