ML Interview Q Series: How would you go about creating a model to predict when a Robinhood user is likely to stop using the platform?
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Short Compact solution
First, clarify what “churn” means in the context of Robinhood by understanding how the platform monetizes and what constitutes a lapsed user. For instance, consider inactivity, canceled premium membership, or negligible account balance over a set time period.
Next, figure out how you will apply the model’s predictions to your business goals. Ask what interventions you might take once you identify at-risk users.
When building the churn model, pick methods that can output probabilities (for example, logistic regression or decision trees). Keep in mind model explainability; if stakeholders need interpretable reasons for churn, simpler models or tree-based approaches are often helpful.
Gather features such as raw account balance, trend of withdrawals, history of significant losses, user activity patterns (e.g., login frequency), and basic demographics. Train and evaluate the model using standard metrics like precision, recall, F1 score, and ROC AUC. Once the model is in production, continuously monitor performance for data or concept drift, refine features as needed, and run A/B tests to confirm the effectiveness of any retention measures.
Comprehensive Explanation
Defining and Understanding Churn
Churn refers to users who abandon or significantly reduce their engagement with a product. In the case of an online trading platform like Robinhood, one way to define churn could be any user who hasn’t made a trade or logged in for a certain period, or someone who has withdrawn most of their balance. The key is to ensure you have a concrete definition of what “churn” means numerically and temporally (e.g., no trades for 60 consecutive days, or total balance falling below a specified threshold for more than one quarter).
Clarifying this definition is crucial because it not only informs how you label data for supervised modeling but also guides the post-model interventions. For instance, if your target is users who have not made trades in the last 90 days, that will directly shape your training labels.
Business Use and Model Goals
It’s essential to determine how the model outputs will be used. If the purpose is to create a retention campaign, you might target users with a high probability of churn, offering incentives or personalized follow-ups. If the end goal is to forecast revenue impact, you might integrate churn probability into a financial model that estimates lost revenue if users stop trading.
Without clarity on how the model’s output affects business decisions, even a well-performing model may not be practically useful.
Data and Feature Engineering
Typical features for a Robinhood churn model could include:
• Raw Account Balance Low balances might indicate users have effectively “given up” or concluded the account is no longer worth it.
• Balance Trends A steady decline over time may signal users slowly migrating away from the platform.
• Losses Incurred If a user suffers substantial losses, they might grow disillusioned, leading them to reduce trading.
• Usage Patterns Indicators like frequency of login, how often they check watchlists, or how many trades they make over a given time can be powerful predictors. A decline in these metrics often precedes full churn.
• Demographics Age, location, or even funding sources might help in building user segments that exhibit different churn behaviors.
• Other Potential Signals
Whether the user unsubscribed from premium services like Robinhood Gold.
The type of assets traded (e.g., only high-volatility cryptocurrencies).
Support ticket data or negative feedback.
During feature engineering, be mindful of data leakage (e.g., including data that only becomes known after the label is determined). Ensure your training data simulates real-world usage conditions.
Model Selection
Many supervised classification methods can work:
• Logistic Regression Provides probabilities directly and is highly interpretable. You can see which features contribute most to the churn prediction by examining coefficients. If interpretability is a priority for business stakeholders, this is often an excellent choice.
• Decision Trees / Random Forests Tree-based models inherently capture non-linearities and interactions among features. They also offer some degree of transparency by examining feature importances and decision paths. Random forests add robustness and usually perform well with tabular data.
• Gradient Boosted Trees (e.g., XGBoost, LightGBM) Often top performers on structured data. They can be more challenging to interpret than a single decision tree, but feature importance metrics and SHAP values can provide interpretability.
• Neural Networks or SVMs Might be used if your dataset is large and complex, but they can be more opaque. Unless there is a compelling reason (e.g., image or textual data that’s crucial), simpler tree-based or linear models may be preferable for churn use cases.
Training and Evaluation
When building a churn model, your dataset may be imbalanced (far fewer churners than active users). Consequently, standard accuracy can be misleading. You should consider metrics like:
Precision and Recall (or F1 score) to assess how well you are identifying churners.
ROC AUC to measure overall ranking performance.
Precision-Recall AUC if the churn class is rare.
Confusion Matrix to understand the distribution of predictions (true positives, false positives, etc.).
You should maintain a clear train/test/validation split based on time to mimic real-world usage. For example, train on data from users’ history up to a certain date, and then test on data from a subsequent period.
Deployment and Ongoing Monitoring
Post-deployment, monitor metrics over time. Watch for model performance degradation due to changes in user behavior or external market shifts (e.g., major cryptocurrency or stock movements that drastically change engagement patterns). A few processes to keep in mind:
Ongoing Evaluation Regularly compute performance metrics on recent data.
Error Analysis Investigate significant misclassifications. Are there specific segments of users for whom the model fails?
Refinement and Retraining Retrain the model with fresh data as user behaviors evolve.
A/B Testing When implementing interventions (e.g., special offers for high-risk churn users), measure their effectiveness by comparing them to a control group.
This iterative process ensures the churn model remains relevant and continues to provide business value.
How do you handle an imbalanced dataset for churn?
Dealing with imbalance starts with the data splitting strategy. Ensure your training set and validation set reflect realistic proportions of churn versus non-churn. You could also try:
Class Weights Adjust the training objective for algorithms that allow weighting to give more emphasis to the minority class (churners).
Sampling Techniques Use undersampling of the majority class or oversampling methods (e.g., SMOTE for synthetic minority oversampling) to balance the training data. However, ensure you do not distort important relationships in the data.
Appropriate Metrics Rely heavily on metrics that are suitable for imbalance, such as F1, precision, recall, or AUC-PR. Accuracy alone might not accurately reflect model performance.
Ensemble Methods Combine predictions from multiple models (e.g., using bagging or boosting) to handle imbalance more robustly.
Why might probabilities be more valuable than a binary classification in churn?
Having a probability instead of a simple “churn vs. not churn” flag helps in prioritizing intervention efforts. Users with a 90% likelihood of churning presumably need a more urgent or expensive retention strategy than those at 40%. These probabilities let you:
Segment your user base by risk level and apply different interventions accordingly.
Measure Potential ROI for each user segment. If there is a costly retention campaign, you might only target users above a certain churn-risk threshold.
Monitor Probability Shifts over time. If user behavior improves or declines, you can see the churn probability move up or down.
How would you interpret logistic regression coefficients in a churn model?
In logistic regression, each coefficient indicates how changes in that feature influence the log-odds of churn, holding other features constant. Suppose you have a feature “average number of trades per month” with a negative coefficient:
Negative Coefficient Implies that as the number of trades goes up, the log-odds of churn go down (i.e., the probability of churn decreases).
What approaches would you use to handle data drift or concept drift in a churn model?
Data drift occurs when the statistical properties of the input features change over time, while concept drift refers to changes in the relationship between features and the target (churn). To handle them:
Continuous Monitoring Track distribution changes in key features (e.g., average user balances, frequency of trades).
Regular Retraining Periodically retrain the model with new data (weekly, monthly, quarterly) depending on the volatility of the environment.
Versioning Keep multiple model versions. Compare performance metrics to see if the new environment requires immediate updates.
Adaptive Online Learning In rapidly changing conditions, implement streaming techniques that update model parameters on the fly.
How do you measure the business impact and ROI of a churn model?
Start by comparing churn rates or user retention rates before and after your interventions. For the high-risk users identified by the model, if you apply marketing or retention campaigns:
Treatment vs. Control Segment out a control group from the predicted “high churn risk” segment and do not treat them. Compare the difference in retention and revenue.
Lift Over Baseline Evaluate how many users were retained beyond what you would have expected without the intervention.
Cost-Benefit Analysis Factor in the cost of retention actions (e.g., promotions, extra customer service) against the additional revenue from retained users.
If the net effect is positive, it signifies good ROI. Over time, keep refining thresholds or targeting strategies to optimize the balance of marketing costs versus savings from reduced churn.
Below are additional follow-up questions
How do you define the correct time window to label a user as churned, especially in a fast-moving environment like stock trading?
Determining the most meaningful time window is challenging because user behavior on a trading platform can vary drastically based on market conditions and individual trading patterns. A time frame that’s too short (e.g., 7 days of no activity) might classify active “long-term investors” as churners prematurely, while a very long window (e.g., 6 months of no trades) might miss opportunities to intervene early.
One approach is to analyze historical user data to see when users typically cease activity before eventually leaving the platform. For example, you could look at the median or average period of inactivity among users who ultimately withdrew funds or never returned. If a majority of actual churners go more than 60 days without a trade before fully abandoning the platform, then 60 days could be a strong candidate for defining churn. You might also consider additional business insights: perhaps Robinhood’s marketing team has found that most re-engagement campaigns after 30 days of inactivity are less effective, suggesting that the window could be 30 days.
Another consideration is the type of user segment you’re analyzing. Day traders or frequent options traders might need a shorter inactivity window, while passive investors might display more sporadic patterns. In practice, this may lead to different churn definitions for different segments. Ultimately, you’d test different time windows and evaluate how they perform in predicting true churn under real-world conditions.
If the data for certain features is incomplete or missing for a large portion of users, how would you handle that in your churn model?
Missing or incomplete data can arise for numerous reasons, such as partial integrations, newly rolled-out features, or privacy preferences. Handling missing data effectively requires both technical and business considerations:
Identify the Reason for Missingness First, determine if data is missing randomly or if there’s a pattern. For instance, high-value users might choose not to provide certain demographic information. This can introduce bias if you impute data without accounting for why it’s missing.
Imputation Techniques If the missing data is largely random, you can consider imputation methods like mean/median substitution for numerical features or most frequent category for categorical features. More advanced methods include regression imputation or iterative imputation (e.g., MICE), but care must be taken to avoid artificially inflating correlations.
Separate Indicator Features Sometimes you can create an additional binary feature indicating whether the data was missing or not. This alone can carry predictive power if missingness correlates with churn (e.g., perhaps users who withhold certain information are more likely to churn).
Omit the Features If a feature is missing for the majority of users, or if the cost of imputation is too high (in terms of bias or complexity), you might decide to drop that feature. However, dropping too many features can reduce model richness, so this decision should be based on robust experimentation and business input.
Modeling Approaches That Handle Missing Data Some algorithms, such as certain tree-based methods, can internally handle missing values by sending “missing” down a default branch. If you rely on such techniques, monitor performance carefully to ensure they generalize well.
How would you incorporate macroeconomic factors or external events, like major market fluctuations, into your churn model?
External economic conditions can significantly influence trading behavior, so ignoring them might reduce your model’s predictive power:
Exogenous Variables You can collect time-series data on indices (e.g., S&P 500, NASDAQ), volatility metrics (e.g., VIX), interest rates, or crypto market indicators. These external variables could be joined with user data based on the user’s last active date or daily snapshots of external conditions.
Lagged Features If you believe user churn might be affected by market volatility in the days or weeks leading up to a user’s inactivity, you could create lagged versions of these market indicators (e.g., the average VIX over the previous 14 days).
Segment-Specific Impact Different users will respond differently to macro conditions. For example, “meme stock” enthusiasts might react more strongly to social media hype than to interest rate changes. You could segment your user base (e.g., crypto-traders vs. index investors) and apply relevant macro features.
Exploratory Analysis Correlate user churn labels with macroeconomic events or announcements (Fed interest rate decisions, unexpected market crashes, etc.) to see if churn spikes coincide with these events. If so, it’s a strong sign that external variables should be part of your model.
In building the churn model, how would you ensure fairness across different demographic groups?
Ensuring fairness means that your model shouldn’t systematically disadvantage or mislabel a particular demographic group (e.g., age group, gender, or geographic region):
Identify Protected Groups and Relevant Fairness Metrics Step one is to decide which demographic attributes to consider sensitive or protected. Metrics like demographic parity or equalized odds can be used to measure fairness. For instance, you can check if the model is overestimating churn for a particular age bracket.
Measure Disparate Impact After training your model, you can compute how often each protected group is labeled as high-risk churners. If the rate is significantly different across groups, that might indicate bias.
Techniques to Improve Fairness
Pre-processing: Remove or modify features that strongly correlate with protected attributes while attempting to preserve information relevant to churn.
In-processing: Use specialized algorithms that optimize both accuracy and fairness metrics, such as fair logistic regression or fair classification constraints.
Post-processing: Adjust the decision thresholds or re-rank outputs to correct for disparate impact after the model is trained.
Trade-offs There is often a balance between maximizing predictive accuracy and enforcing fairness constraints. Working with business stakeholders is critical to decide acceptable trade-offs.
Could you describe your approach to hyperparameter tuning for the chosen ML model, and why it’s important for churn prediction?
Hyperparameter tuning is essential for maximizing model performance, especially given that small changes in parameters can significantly affect recall, precision, and other metrics critical to churn detection:
Hyperparameter Tuning Methods
Grid Search: Systematically explore parameter combinations, though it can be computationally expensive.
Random Search: Randomly sample the parameter space, often more efficient than grid search.
Bayesian Optimization: Iteratively refine the search based on prior evaluations, focusing on the more promising regions of the parameter space.
Cross-Validation Use time-based splits (if possible) so that each fold respects the chronological ordering, reflecting how the model will predict future churn from past data.
Key Parameters
For Logistic Regression: Regularization strength (C), type of regularization (L1 or L2).
For Tree-Based Methods: Number of trees, maximum depth, learning rate (in boosting), or minimum samples per leaf.
Model Stability Churn prediction often needs stable probabilities over time. If your model is too sensitive (overfit) to a small set of hyperparameters, it may degrade quickly when conditions change. Therefore, keep the final set of parameters that generalize best across all folds and do not over-optimize to the training data.
After deploying the model, how do you handle a situation where the model’s predictions drastically shift from the historical average?
Large, unexpected shifts can indicate either a significant external event or a data integrity issue:
Check Data Inputs Verify that feature distributions and schema are consistent. A model might give strange predictions if the format of incoming data changed unexpectedly (e.g., a numeric feature now appearing as a string).
Investigate External Factors Real-world events like a market crash, regulatory changes, or a new competitor in the market could legitimately alter user behavior. If so, consider retraining the model with new data that captures those changing patterns.
Fallback or Monitoring Alerts Create an alert system that triggers if the average predicted churn probability spikes or drops beyond normal variance. You might temporarily revert to a previous model or use a simpler rule-based system if you suspect critical errors in the new model.
Data Drift Analysis Look at distributions of each feature. Compare them against baseline training distributions to confirm if genuine drift is happening.
If your churn model strongly relies on certain features that are no longer available or collected, how would you adapt to that scenario?
When essential features stop being collected (perhaps due to privacy regulations or technical system changes), you can:
Identify Alternative Proxies Look for correlated features that approximate the missing signal. For example, if you lose “trade frequency,” maybe you still have “daily app open count,” which might serve as a partial replacement.
Retrain without the Missing Feature Evaluate how much performance degrades if you omit the unavailable feature. If it’s significant, consider deeper feature engineering or new data sources to fill the gap.
Ensemble or Hybrid Approaches If older user data included that feature and new data doesn’t, you can build separate models for “with feature” vs. “without feature” cohorts. Over time, the latter model will become the main one.
Model Monitoring Carefully track how predictions change once you remove or replace the feature. Sometimes performance may remain stable if the model can compensate with other correlated signals.
In the case of a well-performing model that doesn’t drive expected business outcomes, how would you investigate or address the discrepancy?
A strong confusion matrix or AUC score doesn’t guarantee that predicted at-risk users will respond to interventions:
Check the Intervention Strategy Even if the model correctly flags churners, the marketing or product team might not be using the model outputs effectively. For instance, a user flagged as high risk might receive a generic email that doesn’t incentivize them to stay.
Measure User Engagement with the Intervention Conduct A/B tests to see if at-risk users who receive a customized outreach campaign have lower churn compared to a control group. If not, the intervention may need redesign.
Review Segmentation The model might be accurate overall but miss important sub-segments (e.g., high-value users). You might need additional segment-specific strategies or refine the threshold for intervention.
Examine “False Positives” Sometimes the cost of an unnecessary intervention might overshadow the benefits. Even if the model is right about churners, the cost to retain them may be too high. Re-check the cost-benefit calculus to ensure you target only the most profitable at-risk users.
If your model ends up with high false positive rates but great recall, how do you address that trade-off in real business scenarios?
A high false positive rate means many users are flagged as at risk even though they wouldn’t churn. That can lead to:
Resource Wastage The business might spend money on retention campaigns for users who were never going to leave. You can mitigate this by adjusting decision thresholds. For instance, you could require a churn probability above a stricter threshold to label a user as at risk.
Tiered Interventions Instead of a single “churn or not” approach, design a multi-tier system. For users with very high churn probability, you apply a robust retention strategy. For moderate-risk users, you apply a less costly intervention.
Cost-Sensitive Learning Incorporate the cost of false positives and false negatives into the training objective. Some algorithms allow custom loss functions that reflect real business losses.
Periodic Review of Intervention Efficacy Even if an intervention is triggered for a user who wasn’t truly at risk, the cost might be small enough to justify occasionally “over-covering” some users, depending on your overall business priorities.
How do you handle user feedback or skepticism if the churn model incorrectly categorizes them as at risk or not at risk?
Users might become frustrated if they sense they are being “churn-labeled” incorrectly. While it’s rare for users to see the model outcome directly, there can be scenarios (e.g., a user wonders why they suddenly received a “We’d love to keep you” discount):
Transparent User Communication If a user contacts support about an unwanted retention push, encourage support teams to handle it empathetically. Possibly explain that the platform is proactively assisting users who might need help.
Internal Procedures Provide guidelines for customer support on how to respond to false positives. For instance, a user with plenty of activity who still got a retention email should receive a quick explanation and assurance that no negative marks are on their account.
Model Improvement Log these incidents and see if they correlate with certain user characteristics. Use that feedback loop to reduce false positives in subsequent model updates.
Opt-Out Options Offer an easy way for users to opt out of certain notifications or retention campaigns, respecting privacy and preference concerns.