Tackling the App Deletion Problem: A Machine Learning Approach (Part-1)
#9: Breaking Down a Popular Interview Question from Problem Framing to Model Design
The rise of mobile applications has revolutionized how we interact with technology, from productivity and entertainment to fitness and finance. However, a significant challenge app developers face is understanding why users delete their apps. App deletion not only impacts user retention but also indicates potential dissatisfaction with the product or service. By leveraging machine learning, we can build predictive models to anticipate app deletions, enabling businesses to take proactive measures to improve user experiences and retention strategies.
Video by Pabitra Sarkar: https://www.pexels.com/video/person-scrolling-through-apps-on-android-play-store-5150801/
This two-part blog will walk you through designing a machine learning model to predict app deletion. From asking the right diagnostic questions to selecting features, crafting a problem statement, choosing metrics, and implementing the ML pipeline, we’ll break down each step of the interview process. By the end, you’ll have a clear approach to solving this real-world problem and demonstrating your expertise in machine learning system design.
In this first part, we will cover problem framing, essential features, and the binary classification problem statement for app deletion prediction. In the second part, we will dive deeper into model selection, metrics, and implementation strategies, providing a holistic view of solving this real-world machine learning challenge.
Questions to be asked to interviewer
Should the model make predictions in real time (e.g., based on live user interactions) or in batch mode (e.g., once a day or week)?
[Interviewer] Batch mode, updated once a day, should suffice for this use case.
If real-time predictions are required, what is the acceptable latency for inference?
[Interviewer] Ideally under 200 ms for real-time scenarios.
Deletion events are typically rare compared to retention events. How critical is it to address class imbalance in this dataset?
[Interviewer] Very critical; the model needs to handle this to ensure accurate predictions.
Beyond internal app usage data, will external data sources like social media sentiment, app reviews, or competitive trends be made available?
[Interviewer] Yes, external sentiment and review data will be accessible.
How frequently will this external data (e.g., sentiment from social media or user reviews) be updated—daily, weekly, or monthly?
[Interviewer] It will be updated weekly.
Will the model be deployed on edge devices or a cloud server?
[Interview] For purpose of this interview, lets consider cloud server deployment.
What latency is acceptable for predictions when serving users across various geographies?
[Interviewer] Latency under 300 ms is acceptable for a cloud-based deployment.
Features
Selecting the right features is critical to building an effective machine learning model for predicting app deletions. Features should capture user demographics, behavior patterns, app-specific attributes, and contextual information. Below is a well-structured list of features that could inform the model, divided into relevant categories:
User-Specific Features
These features provide insights into the user’s demographics and acquisition source, helping the model identify potential patterns in app deletions across different user groups.
Country (Categorical): Captures the geographic location of the user, which could influence app usage patterns.
Gender (Categorical): Helps identify any trends in app usage or deletions based on gender.
Age (Numeric): A user’s age may correlate with specific behaviors or preferences that impact app retention.
Acquisition Source (Categorical): Indicates how the user discovered the app (e.g., email campaigns, promotions, YouTube ads). Different acquisition channels may lead to varying retention rates.
User Behavior Features
Behavioral data provides a detailed picture of how users interact with the app and can highlight signs of disengagement.
Number of Interactions (7D, 14D, 30D): Tracks user engagement levels over time, helping the model recognize declining activity.
Time Spent on App (Daily Average): Measures how actively users engage with the app, which can be a key indicator of satisfaction or dissatisfaction.
Time Since Last Use: Identifies inactive users, a potential precursor to app deletion.
Static Context Features
Contextual information helps the model understand patterns related to external factors that are not user-specific but still influence behavior.
Time of the Day: Determines whether users are more likely to delete the app during specific times (e.g., late-night hours).
Day of the Week: Captures trends such as higher deletion rates on weekends or specific days when users may clean up their devices.
App Version: Older app versions may have bugs or missing features, which could lead to higher deletion rates.
App-Specific Features
App-specific data focuses on the technical and platform-related attributes that might influence the user’s decision to delete.
Device (iOS/Android): Differences in app performance, design, or compatibility across platforms can affect user experience and retention.
Sentiments from External Events
Incorporating external data helps the model account for broader sentiment and events that might impact user decisions.
Sentiment Data from Social Media and News Articles: Analyzes user sentiment toward the app or its brand from platforms like Twitter and Facebook. Negative trends online may correlate with spikes in deletions.
Tracking External Events: Events such as news controversies or viral trends can significantly influence user sentiment and behavior, impacting app deletions.
Time-Based Patterns: Historical data on app deletions can reveal time-based trends or seasonal patterns (e.g., deletions following a negative app review).
Why These Features?
By combining user-specific, behavioral, contextual, and external sentiment data, the model gains a holistic view of factors influencing app deletions. This feature set also supports interpretability, enabling the team to draw actionable insights from the predictions. Robust feature engineering and data preprocessing will refine these features to maximize the model’s predictive performance.
ML Problem Statement
The task of predicting app deletion can be framed as a binary classification problem, where the objective is to determine whether a user is likely to delete the app or not. The model should output a probability score, which can then be thresholded to classify the prediction into one of the following categories:
Yes (1): The user is likely to delete the app.
No (0): The user is likely to retain the app.
In this first part, we’ve laid the groundwork for tackling the app deletion prediction problem. From asking the right questions to understanding the features that matter most, we’ve outlined the essential steps for framing the problem and preparing the data. These elements are the cornerstone of a successful machine learning pipeline, as they directly impact the model’s performance and the insights it can generate.
In the next part, we’ll dive deeper into the technical aspects, including model selection, feature engineering techniques, and evaluating performance with appropriate metrics. We’ll also explore how Gradient Boosting Models (GBMs) can be leveraged for this task, along with their pros and cons. By the end, you’ll have a comprehensive understanding of how to approach this problem from end to end, whether it’s for a business challenge or a machine learning interview. Stay tuned!