Churn Prediction with AI using Data Streaming and Data Machines

In our series of use case posts, we are discussing the different ways AI can be added to software apps and business processes using Data Machines. In this article, we will discuss how AI based Churn Prediction can help businesses can monitor their software apps for user churn and introduce remediation measures to retain users.

Detecting churn in software apps is crucial for businesses to understand and mitigate user attrition. Churn refers to the rate at which users stop using a product or service, and in the realm of software apps, it holds significant implications for business success. Identifying and addressing churn can contribute to customer retention, revenue stability, and overall business growth.

Why is identifying Churn important?

One primary reason to prioritize churn detection is the financial impact it has on a business. Losing customers means losing revenue, and in the competitive landscape of software apps, every customer matters. By employing sophisticated analytics and monitoring tools, businesses can track user engagement patterns, identify when users start disengaging, and take proactive measures to retain them. This can involve targeted marketing efforts, personalized communication, or improvements to the app based on user feedback.

Beyond financial considerations, understanding churn provides invaluable insight into product performance and user satisfaction. When users abandon an app, it often signifies dissatisfaction with the product or that a better alternative was found elsewhere. Regularly monitoring churn helps businesses identify pain points in their UX, enabling them to make data-driven enhancement decisions. This iterative process fosters continuous improvement and ensures that the software aligns with user expectations.

Churn detection aids in the development of customer-centric strategies and recognizing patterns in user behavior allows businesses to segment their user base and tailor strategies to specific groups. For instance, if certain user types consistently exhibit higher churn rates, businesses can create targeted campaigns to address their unique needs and preferences. This personalized approach reduces churn proactively and builds stronger customer relationships, loyalty, and brand advocacy.

Building a Churn Prediction Engine with Data Machines

Data Machines provides a simple no-code development interface to build and test a Churn prediction system based on the activity data of users from any software or business app. The technical details of the model such as the input(s) required and the outputs are available here.

For Churn Prediction to provide accurate results, the activity data of all users of the app has to be analyzed at regular intervals to derive the measures needed to predict churn. Once created your Churn Prediction data machine can be integrated with customer-facing software applications or software supported business processes. The same Churn Prediction Data Machine you build can be used multiple times by connecting it to any apps or user experiences you want to measure allowing it to train against and deliver unique and accurate insights for each app it is connected to. 

To build a simple AI Based Churn Prediction engine, use the Data Machines interface in your Qualetics Portal to build it in a few simple steps.

Prerequisite: Setup Qualetics Data Streaming Integration

  1. Login to your Qualetics Portal
  2. Connect your software application to Qualetics Data Streaming to capture the activity data of the users
  3. Ensure that data is being streamed by using Qualetics Live Analytics or any of the processed insights
  4. If user activity data is streaming successfully, the Qualetics platform will start analyzing activity data of users

Prerequisite: Check User Engagement Metrics

Before a churn prediction Data Machine can be created, Qualetics has to analyze user engagement metrics for all users. This is an automatic process that doesn’t require any user input. To verify if this process is completed, please check the User Engagement insight under the User Analytics section. A functioning User Engagement insight will present data similar to the view below.


If you’re seeing User Engagement metrics as shown above, proceed to the next section.

Create A Churn Prediction Data Machine

  1. Click on the Data Machines navigation menu in the left navigation
  2. Click on Add Data Machine
  3. Drag an Operational Step from the toolbox
  4. Select “Activity Models” in the category
  5. Select “Churn Prediction” in the list of models
  6. Drag and Drop the Final Step from the toolbox
  7. Configure the options in the Final step based on your need
  8. Test and Publish the Data Machine
  9. Integrate the Data Machine using the available Rest API options or No Code options using Zapier

Checkout the video below to see the steps in action.

In the competitive tech landscape, where user experience is paramount, timely churn detection is a strategic advantage. It enables businesses to adapt swiftly to changing market dynamics, stay ahead of customer expectations, and remain agile in an ever-evolving industry.

Qualetics Data Machines represents a minimal investment in time and money to adopt leading-edge churn prediction using AI, transforming churn from a threat into an opportunity for growth and creating a customer-centric culture that pays dividends in the long run. In essence, the ability to detect and respond to churn is not just a metric; it’s a strategic imperative for software app success.

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