Without a doubt, user acquisition campaigns are the app marketer’s biggest headache. Increased competition for users, high install abandonment rates, and diminishing returns on social media platforms means marketers are under a lot of pressure to get things right out of the gate. And to their credit, marketers apply the time-tested strategy for user acquisition: identify the attributes of existing app users and create lookalike models for targeting.
But are you preaching to the choir? Are you spending precious ad dollars targeting users who would download your app without ever seeing an ad? Conversely, are you targeting users who will never, under any circumstance, install and use your app? Are there users who will convert on their own, but will convert faster if seeing an ad, and if so, what’s the impact on your bottom line of that revenue acceleration?
As the cost of UA campaigns increases, all marketers are keen to find ways to drive efficiency in campaigns, and one way to do that is to calculate the incrementality of UA ads and use that metric to sharpen your targeting strategy.
What exactly do I mean by incrementality? Every advertiser since John Wanamaker has understood the inherent challenges of measuring advertising effectiveness. Understanding which users are most likely to change their behavior based on seeing your ad will help you drive the media efficiency of your UA campaigns.
Let’s break that down a bit. Incrementality requires you to identify three types of users: those who will never convert so you can delete them and stop spending money on them; those who will convert organically, so you can also delete them from your targeting list as well, and those who will convert faster if targeted by an ad. The latter is the ideal user group to target because they present the best opportunity to accelerate revenue.
AI and user analytics will go a long way in helping you to distinguish between these three groups of users, but they won’t take you over the finish line. Even if you have access to those tools there’s no getting around the need to test all of your assumptions and measure the results.
Guide for Testing Targeting Assumptions
Ultimately, the goal of incrementality is to ensure that all your ad dollars deliver a measurable lift to your bottom line. And that, in turn, means incrementality is the combination of the number of new users who will only convert as a result of seeing an ad, along with the additional revenue your company earns — either via sales or ad revenue — by onboarding that customer sooner rather than later.
To test your assumptions, divide your target audience into two groups, one which you target with UA ads and one that isn’t targeted at all. Is there a measurable difference in user behavior between the two? In other words, does this particular ad change a user’s behavior?
Let’s say you want to target 18 to 25-year-old females for your UA campaign because that’s the most profitable cohort for a particular app. A smart strategy would be to target some of them with an ad and others with a blank screen but capture an App ID from both for tracking purposes. You can use this data to answer very strategic questions over time. For instance, will the users who see no ad download your app at the same rate as those who do? How long did it take the latter to install the app, and how does that delay translate into lost revenue?
There’s an interesting trade-off between customer acquisition cost vs. lost revenue opportunities. If the cost of acquiring a subset of users is lower than the revenue you’ll realize by getting those users sooner, then obviously targeting them is a smart strategy. Ditto for users who are likely to convert organically, but will install faster and be more profitable as a result of seeing your ad. But if the costs of acquiring other users is higher than the revenue you’ll ultimately realize from them, it’s probably better to eliminate them from your targeting list.
Incrementality isn’t a new concept to advertisers, but I have run into many mobile marketers who deploy it for UA campaigns. We now have the tools to measure the impact of advertising on both behavior and revenue acceleration, which in turn allows marketers to get incremental value of their UA campaigns.