The Science Behind App Uninstalls Prediction – Part 2/2

App uninstall part 2
So fellow app marketers and product developers, as promised, we build on where we left off in the previous blog. We talked about compelling app uninstalls stats and also drew insights on an app marketer’s woes. If we do not have the means to retarget and bring them back, we lose the customers forever once they uninstall the app. That’s where we touched upon the point that you and I need a magic wand to prevent these customers from uninstalling in the first place.

Machine learning predictions are the magic we are all looking for. Sounds like a challenging implementation task, don’t you think? Well, it is and it is not. At Tatvic, we call it the PredictN Model. Our data science team and implementation team have built this product which accurately predicts the cohort of users who are likely to uninstall. Let’s jump into the hows and whats of our PredictN Model.PredictN Model Features

PredictN Model Features

  1. Tatvic’s Uninstall Library

     

    machine learningThis library captures all the critical mobile device-specific attributes and then some ranging from battery usage, and data connections to advert ids.

    Key features of the library –

    1. The light weight of 24.2 KB size and 220 bytes of static config file
    2. Easy to integrate into native Android app
    3. Data can be sent to App Analytics tools/ server
  • App Analytics Tools

    App Analytics tools like Google Analytics. Firebase and Localytics, allow us to extract the behavior and transactional properties of users. This minute user-level data (for e.g. average time on the app, days since the last visit, the number of sessions, the number of products checked, acquisition source and campaign, etc) are being tracked. These data points will help us understand the App usage pattern to be used in the model.

Model Process

predictive analytics for apps,

 

Our model looks at historical user behavior data, history of uninstalled user data and collected data using uninstall library to predict the probability of likely to churn users.

  • Connect your App Analytics Tool with our PredictN model
  • Integrate our Uninstall library to start a collection of advanced attributes
  • Build & Run PredictN Uninstall model
  • Generate a list of high probable churning users
  • Target the probable churning users using different marketing channels

Our PredictN model gets trained based on historical app usage data of users and uninstalls library parameters. As more data is collected into app analytics/server, the PredictN model prediction accuracy gets improved.

prediction model

 

Once we build & run the predictN model, it will allow us to generate a list of high probable churning users using the advanced segment. Those users’ advertising ids/PII will help us to retarget using different marketing channels/activities like:

  • Email Campaigns
  • Push Notifications
  • Display Channels

We can also reach out to those users to obtain additional feedback which will help us to understand the reasons for uninstalling your app. Prediction of uninstalling users is a very important step to improve user engagement and app retention rate significantly.

To know more about how we developed PredictN for a marketplace app and more actionable insights, we invite you to attend our upcoming webinar.

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