Incrementality Testing
Incrementality testing uses the principles of controlled experimentation on marketing channels beyond your website. It serves as a valuable complement to Marketing Mix Modeling (MMM), particularly in cases where historical effectiveness data is unavailable. Paramark assists you in conducting the tests and integrating the results into MMM. Read more here.
How incrementality testing works
Incrementality testing uncovers the causal relationship between marketing and sales. The process begins by dividing your audience into two groups—a control group and a test group—and exclusively implementing your marketing to the test group. Metrics such as traffic, leads, units, or sales are then assessed across each group. The difference in results is attributed to the tested marketing activity.
How to execute incrementality tests
There are three approaches to conducting incrementality tests. The first method is conversion lift, with certain ad platforms (e.g., Meta) offering native capabilities for such tests. The second is geo testing, available on some platforms, allowing you to define control and test geographies. When user-level or geo-level targeting is not feasible, the third approach—time testing—is the preferred option. This entails applying a marketing strategy to the entire channel for a specified period.
What can you test
We suggest starting with five categories of hypotheses:
Adjusting spend levels on a specific channel or campaign
Exploring investment in an entirely new channel
Experimenting with a new campaign (creative, audience)
Assessing a new bidding strategy (CPMs, CPCs)
Testing a new price or offer
Paramark helps you avoid common pitfalls
Paramark helps you navigate the challenges in experimentation, including prioritizing short-term gains, neglecting proper experimental controls, and using incorrect metrics for design and analysis. It ensures that your analyses are conducted correctly to draw actionable conclusions.
Paramark is committed to nurturing a culture of experimentation. Recognizing that up to 80% of experiments may not succeed, we offer strategic guidance on budget allocation, identifying high-potential hypotheses, integrating results into future forecasting, and establishing a single source of truth for marketing experiments.
