What is incrementality in marketing?

In a nutshell, incrementality in marketing measures the actual impact that a marketing activity has on an app’s key performance indicator (KPI) such as installs or in-app purchases (IAPs).

For example, an app’s campaign that generated 10,000 installs sounds great; but what if 80% of those installs would have happened organically without the marketing campaign at all? Incrementality answers the question, “What would have happened if I didn’t do X”. This allows mobile app marketers to understand which activities are truly making an impact on growth & revenue goals (and which are not), and empowers them to make better decisions in the mid-term.

This concept has gained significance in response to the evolving landscape of user privacy, where marketers are shifting towards more aggregated and privacy-centric measurement methodologies.

Attribution vs. incrementality

Attribution is the process of matching two touchpoints of data; For example, matching installs or in-app events to marketing sources like a channel or specific campaign. Incrementality, while related, differs from attribution and multi-touch attribution. It quantifies the true effectiveness of an app’s marketing activity–what the impact truly is worth.

However, the two can be used in conjunction with media mix modeling (MMM) for comprehensive next-gen measurement:

1. Attribution allows marketers to understand how to optimize campaigns in the short-term down to the most granular level—creative, country, ad, etc. across platforms from mobile to connected TV (CTV).
2. With always-on incrementality, marketers can test new channels, campaigns, and markets in the mid-term to understand what is cannibalizing organic and what is providing paid lift.
3. And MMM is a perfect pairing for long-term strategic planning. It allows marketers to forecast the best allocation of their budgets across all marketing efforts, not just those that can be attributed.

Traditionally, measuring incrementality involved control groups and test groups. Using this methodology, the test group was exposed to the campaign while the control group was not. By comparing the performance of both groups, marketers could determine the incremental lift generated by the campaign.

However, A/B testing can be expensive and time-consuming for marketers to perform. Plus, it’s growing increasingly difficult to implement the level of detail needed for incrementality A/B tests with privacy regulations. Consequently, predictive modeling has become the favored approach for quantifying incrementality.

This is the method that Adjust has based our Incrementality tool on. Adjust clients run marketing campaigns as normal while Adjust analyzes campaign performance against similar apps, which act as the control group. The resulting data predicts what would have happened if the client had not changed a test variable (budget, creative, channel, etc.). This method of incrementality measurement is a more future-proof methodology that requires no user-specific segmentation.

A few key terms related to incrementality include:

• Incremental lift: The positive effect a campaign had that would not have occurred without the target variable
• Organic cannibalization: Events/installs that would’ve occurred even without the marketing initiative.
• No incremental value: The analysis reveals neither cannibalization nor incremental lift. Therefore, the marketing effort does not significantly affect the target variable.
• Confidence interval: The range of expected values if no changes were made to the marketing initiative.
• Target variable: The variable that is tested against (installs or events).
• Incremental Return on Ad Spend (iROAS): A metric that quantifies the additional revenue generated from advertising efforts compared to a baseline, which usually represents organic or non-advertising-influenced performance.

Adjust is beginning to roll out a new product, Incrementality, which utilizes incrementality modeling–the process of creating statistical models to estimate the additional value generated by a marketing campaign–to allow mobile marketers to future-proof their return on investment (ROI). This product is currently in its Early Access stage for clients of Adjust.

By leveraging industry benchmarks, Incrementality assesses a campaign's impact on a client's target KPI, offering insights into whether the campaign is generating incremental growth, cannibalizing organic user acquisition (UA), or remaining neutral with no direct impact.

As we introduce this product to the market, Adjust clients will be able to measure four essential incrementality use cases:

1. Campaign Start
2. Campaign Pause
3. Campaign Budget Increase
4. Campaign Budget Decrease

This data will empower marketers to make insight-driven optimizations, refining their incremental strategy based on nuanced insights. Keep your eyes peeled on the Adjust product page for our launch later this year, or subscribe to our newsletter below to be notified of the latest news and developments as we continue to roll out powerful solutions like Incrementality.