With user acquisition costs at an all-time high, app marketers are always looking for ways to optimise their advertising spend. In our experience, for many advertisers, it’s too easy to overlook the benefits of retargeting in favour of focusing mostly on user acquisition, committing substantial resources to constantly bringing in new players. But that emphasis on bringing in new users doesn’t address the “leaky bucket” problem of users who churn and don’t come back.
That’s where retargeting is so important. Today, retargeting has evolved into a sophisticated, AI-driven strategy essential for maintaining growth and profitability. It’s much more than just bringing lapsed users back into an app or game.
Retargeting strategies will vary between DSP’s, but Bigabid’s pioneering use of a technique called probabilistic matching is what gives it an edge. Probabilistic matching is a process that uses machine learning to analyse behavioural patterns derived from billions of anonymised data points and then retarget users based on which segmentation, placement, ad type, and creative are most likely to generate a positive response.
Iren Raz is the Head of Customer Success, EMEA & US at Bigabid. We asked her to share her insights on why retargeting is a key part of any effective app marketing strategy and why Bigabid’s data-led approach sets it apart.
It’s definitely a challenge, but you can’t just give up on your most lucrative audience. At Bigabid, we use probabilistic matching to analyze non-personal signals and create probabilistic user profiles. It essentially allows us to reach the “unreachable” and cover close to 100% of an advertiser’s user base, even without an IDFA. In fact, we’ve seen this technology scale a client’s iOS activity by multiples, unlocking massive growth in an environment where most advertisers thought scale was no longer possible. So growth is still very much achievable, even in a privacy-first world.
We shift the focus from high-volume advertising to High-Intent Precision. Our models analyze behavioral signals to identify the ‘Peak Potential’ moment. If the data doesn’t show a high probability of a meaningful interaction, we simply back off. Our goal is meeting the users at the most relevant touchpoint. So it’s not about being everywhere – it’s about showing up exactly when it matters.
It’s easy for app companies to overspend by showing ads too frequently, and you are right that this just annoys users and decreases the likelihood the ad will work. You are also simply throwing money away.
This applies just as much to retargeting as any other kind of user acquisition. On any platform, you need to assess user interest by their level of engagement, and focus on the most responsive groups. If users don’t return, they’re likely uninterested, regardless of consent.
We use all available signals except IDFA to build highly accurate user profiles. While there is inherent uncertainty, our accuracy is about 90%. We confidently target users, even without absolute confirmation.
By constructing robust user profiles, we retain high-LTV users in the app. Bigabid has built a reputation for effectively targeting high-value users and allocating resources efficiently rather than distributing impressions indiscriminately.
The “best time” is dynamic, it’s when we see a ‘Habit Shift.’ We look for micro-signals where behavior shifts from a pattern – like a Daily user suddenly dropping to once every few days. It’s about being proactive and stepping in precisely when a user is in the right mindset to re-engage with the app.
We try to understand typical user behaviour, which enables effective real-time buying. We analyse actual user actions, such as the last purchase date, and key in-app events, to build a picture of how that user might behave if shown an advert for an app they have used in the past. Our platform can evaluate all these signals in real time. We also focus on providing the best possible user flow. By using deep linking, we drive customers directly to specific in-app locations or time-limited events, effectively shortening the path to conversion.
Well, apart from our predictive modelling, another newer development is incremental measurement.
Many advertisers are concerned with ROAS, which measures revenue against spend. The key question when measuring campaigns is whether the ROAS reflects the full impact of retargeting, or whether those users would have come back organically without it. Because of this ROAS uncertainty, some choose not to invest in retargeting, believing their organic return rates are high.
This problem led us to explore how we can better measure the uplift from retargeting. We were fortunate to have the support of a major mobile gaming publisher who also wanted a better way to measure ROAS, so together we built the infrastructure for incremental measurement that we could refine and improve, ultimately developing it into an automatic, ready-to-use product we could make available to all clients. The key here is making sure the measurement logic is accurate and reliable. When done right – you focus your spend on users who truly respond to your efforts.
But it’s not just about how good our technology is – it’s also about transparency, a partnership only thrives when there is absolute trust in the results. Any campaign we run is a collaboration in which everyone aims for success. So I’d like to think that our whole approach to working with our customers sets us apart. We want to make sure your next dollar is spent on the most relevant person, not just a random impression.
A common mistake is ignoring non-paying users. Most people only target the small group of acquired payers, but there is so much potential in the other 98% of users who spend little to nothing once they have installed the app. As a data-first company, we filter that group to find users with the highest potential to convert and treat them before they churn for good. This isn’t retargeting as you know it, it’s more of a strategy to maximize the ROI on your original acquisition spend.
Yes, exactly. They could be people who have been away from an app for a while, or who use it regularly but don’t spend any money. The bottom line is, they can have great LTV and have lapsed or be a non-spender but spending elsewhere. Our job is to find and convert those users.
At the end of the day, we need to remember that the real goal isn’t to get as many users as possible, it’s to get – and keep – the ones who spend time and money in your app. We know that around more than 90% of new users acquired through advertising don’t drive any revenue, so it’s essential to understand the behaviours of the users so you can reach them and keep them engaged in your app.
So to summarise, the path to an effective retargeting campaign needs to be as much about the psychology and intent of the users being targeted as the ads themselves. The development of probabilistic tools means Bigabid can dig into non-IDFA data and still create a picture of what an app user is likely to respond to, dynamically building campaigns that, in conjunction with the company’s expertise in ad creatives and measurement, deliver superior results.
We’ll leave it to Iren to have the final word. “When retargeting is done right, you can focus your spend on users who truly respond to your efforts.”
Ready to take your mobile retargeting campaigns to the next level? Reach out to Bigabid for expert guidance on optimizing your app’s user acquisition and retention strategies. Let us help you create campaigns that don’t just drive downloads, but build lasting relationships with your users.
