We’ve talked about the wide range of campaign models, and why CPI has moved to the forefront in a mobile-first world. But there’s also a wide range in app products too, and one of the first measurements to consider is what kind of app your product is – and what’s the behavior profile for people who use it. Publishers in the extensive ADATHA network are keenly attuned to the differences between users playing games, shoppers on the go, and readers engaged in news and information content. So it’s important to remember that when we’re talking about “user quality,” we understand the engagement of those users – and how they’re likely to install and use the app your CPI campaign is designed for. A detailed understanding of not just the outlets, but the targets is important – and paying close attention to the details will yield better results, and better quality users.
- Tracking and user metrics. A CPI campaign without attention to details can be very costly, and may result in a large number of low-value users. Paying close attention to user metrics will increase the quality of users, as well as the value.
- Post-install engagement. Fortunately, it is possible to track what happens after the install. Do users interact frequently with the app? Do they make purchases or take advantage of value-added services? Tracking who makes the most use of the app after download, and the demographics of those users, result in a more finely-tuned campaign.
- Lifetime value of users. The mobile model, often dependent on free or very low-cost apps, makes money from a longer perspective with models such as value-added services or subscription models. Tracking the lifetime value of users and their demographics is the key to a high quality campaign.
That’s just the beginning, though. The advances in mobile-driven data technologies that help us to understand your advertising targets also help us to know more about them, and align your campaign with your users so that you’re delivering an optimal an attractive experience. If that sounds like an explanation of how the ad works and not the user, it should – the data reflects how interdependent this understanding of users is, and it informs the process of placing your ads in the hands of the right user.