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Incrementality and Optimization: You’re Probably Doing it Wrong

featured image - incrementality and optimization
How can you be sure that every dollar you spend on retargeted ads is effectively growing your bottom line? Measuring and optimizing toward incrementality is critical for helping digital advertisers determine the true return from their retargeting ad spend. By measuring the lift in value — like revenue — that ads provide, you can accurately assess and optimize the impact of your retargeting campaigns in order to more strategically scale your ROI.

As vital as incrementality is, not all advertisers know how to properly optimize for it. Here’s what they may be overlooking, and how they can correct these oversights.

Why optimize toward incrementality?

Many advertisers manage retargeting with the end goal of driving revenue from people who initially failed to convert on their website or mobile app. However, just because you retargeted an ad to a specific customer, doesn’t mean that your ad is responsible for ultimately converting that sale. That particular person may have returned and purchased regardless of whether or not they saw an ad at all. Therefore, attributing this sale to your retargeting ad campaign doesn’t tell the whole story.

In the chart below, you can see that most overall purchases come from people (represented by blue dots) with a higher likelihood of purchasing. Showing ads to these people is largely unnecessary and wasteful, as they’re already likely to come back to purchase regardless. However, the dots circled in red represent people who would normally not return to purchase, but did in fact come back after being retargeted. In those instances, retargeting made all the difference. That’s why retargeting campaigns must focus less on simple predicted purchase rates and more on true incremental lift.

Revenue Optimization Chart - Optimizing for Incrementality

Focusing on incrementality means that you’ll zero in on which customers are actually impacted by your ad dollars. By knowing exactly who your ads effectively converted, you can optimize for similar lift in the future.

Why don’t all performance advertisers optimize toward incrementality?

Today, most performance advertising is incorrectly optimized on attributed conversions, such as cost per acquisition (CPA) and return on ad spend (ROAS). These metrics are simple to measure and relatively easy to optimize. However, they’re not causally related to incremental growth. By showing ads to customers with high purchase rates, the industry is wrongly assuming causality.

For many retargeting platforms, optimizing toward incrementality isn’t in their best interests. It’s much easier to emphasize conversions or ROAS because they can more easily take advantage of the data assest and predictive conversion rates they already have. This allows them to take credit for purchases that would have occurred organically regardless of whether or not a retargeted ad was shown.

How do you judge incremental success?

There are several factors to look at if you want to determine whether your retargeting campaigns are driving incremental return. Here’s what one ecommerce advertising team focused on to determine whether its cross-channel retargeting campaign was a success.

The team leveled up their success metrics and focused on incrementality to measure the results of their retargeting ad. By comparing incremental revenue to cross-channel media spend, they were able to measure true incremental ROI. Additionally, an increase in order volume and reduction in average CPMs indicated that incrementality was helping them run more efficient and more effective retargeting campaigns.

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2 Responses

  1. So, I got excited about this article since I agree with the premise, yet don’t find enough content on the “How” of optimizing to incrementality. Call me crazy, but I still don’t see any “how” in this article.

    Yes… lift testing against holdouts (which is hard, and sometimes misleading since there are more factors at play)
    Yes… Big, expensive attribution suites with sophisticated models

    Anything easier? or is the hard reality that there isn’t an easy way to approach this?

    Perhaps trying to create predictive models on which of your users have a statistically higher chance to come back and convert anyways, then run them as a suppression list if you have the analytics team and data to dig into it?

    • Thanks for your comment, Tim! We’re planning to dive into specific strategies for measuring and optimizing incrementality in future posts coming soon. Stay tuned!

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