What is the incrementality factor and how can I leverage it?
Should I be testing Applovin?
What’s up marketers?
I hope this email finds you well and enjoying Black Friday weekend. It’s one of my favorites.
I’ll keep this post short to let you get back to crushing your holiday goals.
This one will be all about incrementality factors and how to leverage them in your marketing decisions.
Enjoy 🚀
I’ve been seeing a lot of discussion on X lately about Applovin being the next Meta like platform for scaling DTC brands.
Some of the best, including Olivia at Haus, have already shared data that shows a strong incrementality factor across the brands that are testing Applovin within the Haus platform.
The data that is coming in is still early, but it’s an exciting signal for those of us stuck in the Meta and Google duopoly.
Olivia’s post sparked a few questions for me:
What is the incrementality factor, really?
How can I leverage incrementality factors as a metric to guide my marketing decisions?
Should I be testing Applovin? 😝
What is the incrementality factor
The incrementality factor in marketing measurement represents the extent to which a marketing activity drives incremental outcomes (e.g., sales, leads, or other key metrics) that would not have occurred without that specific activity.
Essentially, it measures how much additional value a marketing effort contributes beyond what would have happened naturally or from other influences.
Key Concepts:
1. Incremental vs. Baseline:
Baseline: The results you’d achieve without any marketing intervention (e.g., organic sales).
Incremental: The lift or additional results directly attributable to the marketing effort.
2. Expressed as a Percentage:
The incrementality factor can often be expressed as a percentage of the total observed outcome.
For example, if a campaign results in 1,000 conversions, and 400 of them are incremental (would not have happened otherwise), the incrementality factor is 40%.
3. Testing for Incrementality:
Incrementality is typically measured using methods such as:
Holdout tests: Comparing outcomes between a test group exposed to marketing and a control group that isn’t.
Geo-lift experiments: Running campaigns in specific geographic areas while others act as controls.
Time-based tests: Observing performance before, during, and after a campaign.
4. Why It Matters:
Efficiency: Helps identify which channels, campaigns, or strategies deliver the most incremental value.
Budget Allocation: Informs smarter spending by focusing resources on high-incrementality channels.
Avoid Double-Counting: Reduces overestimating the impact of marketing when results may be driven by baseline or overlapping effects.
By tracking the incrementality factor, marketers can move beyond vanity metrics (e.g., total clicks, impressions) to understand the true ROI of their efforts.
As for me testing Applovin, the early data that has been shared is promising, but I think it’s a bit early for our team at Live it Up to expand into other channels.
My guess is there is still a ton of head room on Meta.
I may need to do another post on how to think about channel expansion and priorities, but let’s save that for another time.
Enjoy those leftovers and good luck tomorrow!
Cheers,
Trent