There’s a rumor circulating that sharing your data is bad. Very bad.
There’s a rumor circulating that sharing your in-app data with adtech partners is bad. Very bad.
Sharing your data with a shady company is bad. In the right hands, in-app data will help your marketing partner identify who to target, when and how to drive more revenue. That’s a good thing. A really good thing.
Advertisers should consider their app marketing providers an extension of their team and select wisely. The key is to share goals and data with ad tech partners who can offer similar transparency: a real-time dashboard, regular reports, realistic return goals and actionable data points.
For the curious, the skeptics and the brands struggling with the necessary set-up for data sharing, this post covers what data marketers should share and the benefits gained. 📈
Technology platforms require event postbacks, which combine Device ID, time stamp, event name and event parameters. It’s important to share both event postbacks attributed to your marketing partner (“Attributed Data only”) and those that are attributed across all your other channels combined, including organics (“All Data”). Otherwise, any data insights will always be incomplete.
Simply sending app marketing providers a list of device IDs won’t do. At Jampp, we process 100TB of data per day, over 1GB per second. This is the Real-Time World. If advertisers want to keep up, their data needs to be as dynamic and free-flowing as their users’ in-app activity.
‘Cos the advertisers wanted to apply programmatic and predictive technology to retarget their mobile app users, of course 🤓
Engaging users is harder on mobile than other media because on mobile “consumers browse far fewer products and expect relevant suggestions.” — emarketer report 2017. This means it’s more important than ever to understand user behavior, and leverage that knowledge to target users with customized messages.
Analyzing the performance of all in-app events is how app marketing providers identify user behavior patterns and why they occur: When are users most active? At what stage of the event funnel are users dropping off? Answering questions like these helps to determine what segmentation strategy is the most appropriate for each app.
Having access to that data plays a key role in saving advertiser dollars, and unlocks advanced targeting and engagement tools.
Real-time analysis of contextual and behavioral data can be used to predict the likelihood of conversion for each non-activated user.
The more data and conversions a programmatic platform analyzes, the more it learns about what makes a user convert. In other words: machine learning becomes more accurate with every new piece of data received, improving its ability to predict the characteristics of future purchasers and how best to target them.
Sharing all in-app data along with benchmarks makes it possible for some marketing partners to rapidly single out user activity that is not hitting the mark. It’s also used to identify whether performance is suffering because of an in-app bug, an update that affected deep links, or if it’s signalling fraudulent activity.
App marketing providers are only as good as the data advertisers share with them. Limiting data sharing not only limits results, but also reduces advertisers’ options in terms of types of campaigns, segmentation and messaging they can use.
This is not really a situation where a little data is better than none; what data is shared determines what technology advertisers can use — as some programmatic products require certain data just to function.