In other words, which channels produce the greatest incremental return on ad spend, and how can we shift funds toward those? Marketers are now explicitly posing this question, using incrementality data to guide budget splits. For instance, brands working with Bimodal and similar platforms focus on which mix of channels yields the most incremental outcome for the next dollar spent. By asking this, you ensure that each channel’s budget is justified by additional conversions, not just attributed ones, leading to a more efficient overall media plan.

For example, if you run a TV ad in some regions but not others, does the exposed region see higher sales than the holdout region? By isolating markets, you can measure how many sales occurred because of the ads versus how many would have happened anyway. This question helps you determine if a costly TV or OOH campaign is actually generating new demand or just maintaining baseline sales.

For example, which Meta campaigns (top-of-funnel vs. bottom-of-funnel) are actually incremental in acquiring new customers.

This is a common concern – one brand spending heavily on Google brand keywords tested this and found almost no lift, confirming that those sales would have happened without the ads.

Brands selling on retailer sites/spaces should ask if their paid placements there generate extra sales beyond what organic visibility would bring. For instance, you can run a test to measure marketing impact across DTC and retail channels, allowing them to see how ads translated into incremental sales on Amazon and Woolies in addition to their own site. This question helps determine if retail media ads are driving net-new customers or just capturing existing demand on those marketplaces.

Marketers should test if a sale or coupon truly boosts revenue. For example, run a discount (or BOGO offer) in one group/region and compare to a control group without it – did total sales increase, or did customers simply take the deal with no net gain? Incrementality testing can reveal if promotions add actual value or merely act as a nudge without increasing overall demand.

Our platform integrates multi-channel data, uses advanced modeling to isolate true incremental impact, providing clear ROI insights to optimise spending and justify budget decisions.

We plan geo-lift experiments with synthetic controls to measure causal effects, delivering statistically significant uplift estimates and actionable insights for channel, offer, market, or budget optimisation.

Our streamlined setup and real-time dashboards often yield preliminary insights within weeks, with comprehensive marketing effectiveness reports following each model refresh or incrementality tests completion.

We provide hands-on guidance from dedicated data scientists, extensive onboarding, training sessions, and continuous support to upskill teams and optimize model usage.

We’ve driven 10–20% ROI improvements across finance, retail, and consumer goods, using advanced modeling and incrementality testing to optimise multi-channel marketing strategies.