Predictive Analytics for App Marketing — Without SDKs

Using predictive analytics to enhance your mobile app marketing is a powerful strategy. As we recently discussed in our recent post on campaign optimization, the potential to improve your ROAS with predictive analytics is immense. But is the effort required to implement predictive analytics really worth it?

Without question, the answer is yes — and the effort can be a lot less than you think. 

Weaving predictive analytics into your existing data flows can be seamless and straightforward using server-to-server integration — as we do at Pecan. SDKs won’t stand in your way when data and predictions pass directly between servers. This approach avoids a heavy tech lift by your team and fast-tracks the growth you’ll realize with actionable predictions.

Let’s dig into how this integration works, and see how it can make adding predictive analytics to your app marketing strategy even more accessible and worthwhile.

Keep your app as is, but gain foresight of user behavior and pLTV for marketing

Pecan’s server-to-server integration means you don’t need to update your app to start using information from predictive analytics. You can skip the complex cross-team collaboration required to develop new infrastructure for data flows and push out app updates. Instead, focus on the exciting, rewarding task of effectively using predictions to enhance marketing campaigns that improve user acquisition and retention.

With server-to-server integration, no changes to your app are necessary to send data directly to Pecan, nor do you have to use the MMP’s SDK to build a connection. 

Instead, Pecan can connect with your user data wherever it lives — for example, in a data warehouse — and combine relevant data to generate predictions regarding your users’ behavior (for example, churn likelihood) and their pLTV. 

These predictions are high-resolution, meaning they don’t just offer a broad category or segment for each user. Instead, they provide a precise number to reflect the likelihood of a user’s behavior. Those numeric values let you define your desired segments, then prioritize and/or group users to fit your campaign goals. 

In addition, Pecan’s predictions are accompanied by granular information about the primary variables that influence each prediction. That information gives you deeper insight into causes of likely user behavior. That information is readily available through easy-to-read dashboards provided in the Pecan platform.