Using predictive analytics to enhance your 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
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, but instead 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, giving you deeper insight into causes of likely user behavior. That information is readily available through easy-to-read dashboards provided in the Pecan platform.
Move predictions swiftly and easily to your database and MMP
From Pecan’s platform, predictions can be returned to your database and also sent directly as app events to your MMP (such as AppsFlyer, Singular, or Adjust) via a server-to-server integration from the Pecan platform. This approach minimizes the engineering effort required to create a data connection. Your MMP can pass predictions directly to your marketing channels, including Facebook and Google, for a much more precise optimization of your spend.
Take action on predictions to shape campaigns and boost performance
Armed with foresight about your users’ predicted future behavior and pLTV, you can take action to acquire and engage users better than ever before.
You’ll be able to focus resources on the channels and creative that bring high-LTV, long-term users, designing data-focused strategies that bring you more of the kinds of users you really want. These future-informed actions can improve your ROAS, increase user retention, and boost overall user pLTV.
We’ll talk more about how you can take action on your predictions in an upcoming post, so stay tuned for more details!
Tracking and optimizing your campaigns with predictive analytics via Pecan’s server-to-server integration requires no changes to your app code, avoids ongoing updates for future SDK changes, and enhances campaign performance.
Crushing your app marketing KPIs by gaining foresight with predictive analytics is more appealing than ever.
Curious to know more about how predictive analytics could supercharge your app’s revenue lifecycle? Please let us know how Pecan can assist you in taking the next steps! We’re pumped about how predictive analytics can help app publishers, without data science resources.
We can assess your predictive readiness with a quick, easy use-case consultation. We’ll help you find the best way to get future-ready.