How to measure incrementality?

What is incrementality?

Incrementality refers to the additional outcomes—such as sales, leads, or engagement—attributed to advertising efforts, beyond what would naturally occur without those efforts. In simple terms: it helps figure out if your marketing strategies are making a difference by showing what results are specifically caused by your ads, separate from what would happen anyway.

How to measure incrementality?

At its heart, an incrementality test is a randomized, controlled experiment that divides individuals into two groups:

• Control group: Individuals in this group are not exposed to the marketing campaign. This group serves as a baseline to understand what actions people take in the absence of the campaign.
• Treatment group: Conversely, this group is exposed to the marketing campaign. Observing their actions allows businesses to measure the direct impact of their marketing efforts.

How incrementality is calculated?

The process involves stopping ads for the control group while continuing for the treatment group. The incrementality is the sales difference between these groups.

A use-case of incrementality testing:

A marketing team suspected that their Facebook Ads were driving more sales than reports initially attributed. To test this, they conducted an incrementality test, creating two groups: one saw the ads (treatment group) and one didn’t (control group). After a month, data showed the treatment group’s sales increased by 30% compared to the control group. This 30% sales increase confirmed the ads’ incremental impact, prompting the team to increase their Facebook advertising budget for future campaigns.

How to use incrementality testing for optimization

1. Form a hypothesis: Decide what you want to find out, like What is the incremental impact of this channel? or which new channel should we diversify into?
2. Conduct a test: Pause ad spend in one area while continuing in another to compare the effects.
3. Measure the difference: After a set period, assess the performance differences between the test and control groups.
4. Learn and adjust: Use what you learn to calibrate your budget allocation decisions.
5. Keep testing: Keep hypothesizing and testing to keep improving your results.

Incrementality testing vs attribution

Incrementality testing and attribution modelling are two powerful tools in marketing analysis, each with its strengths for improving marketing strategies.

Incrementality testing:

• What it does: Measures the direct impact of specific marketing actions by comparing a group that saw the campaign with one that didn’t.
• Benefit: Provides clear insights on the extra value generated by a campaign, guiding immediate strategy tweaks and budget adjustments.

Attribution modelling:

• What it does: Attribution modelling allocates conversion credit across various touchpoints in the customer journey, identifying influential channels and interactions leading to conversions.
• Benefit: Provides various attribution lenses, including advanced models that capture the value of visual channels often overlooked by click-only metrics. For example, SegmentStream introduces a nuanced perspective with its post-view and post-click attribution model. This method is especially valuable for evaluating visual-focused channels and campaigns, which traditional models, focused solely on clicks, often undervalue.

Combining both: You don’t have to pick one over the other. Using incrementality testing for pinpointing the impact of specific actions and attribution modelling for a wider view of your marketing ecosystem offers a comprehensive strategy. Tools like SegmentStream bring these approaches together, allowing for both quick tactical moves and informed, strategic budget decisions. This blended approach gives you a complete toolkit for making your marketing more effective.

Want to explore how Incrementality testing with SegmentStream works? Read this update.