liyouzhang/churn_prediction

Predict churning or not from the real-world data of a ridesharing app

Churn Prediction for a Rideshare Company

A ride-sharing company is interested in predicting rider retention.

Findings

• High-income users are less likely to churn: Using iphone, living in King's Landing (and to a lesser extent in Winterfell), and riding luxury cars are consistently negatively associated with the churn probability.
• Users who ride on both weekdays and weekend days are less likely to churn, as compared to users who ride only during weekdays or only during the weekend.
• Users who experiement low shares of rides with surge are less likely to churn. However, this trend is more evident in which suggests that surge seems to have a short-term effect on user's behavior.
• Ratings and average distance of rides do not seem to be associated with churn.

Suggestions

The best strategy to be adopted will depend on the stage and priorities of the company.

• If the churn definition is aggressive (i.e. a user is considered to have churned if he/she has not used the service in 30 days), the company can make a profit of up to $8 per user by sending out $20 per year in promotions to users predicted to churn.
• If the churn definition is standard (i.e. 90 days), the company can make a profit of up to $4 per user by sending out $20 per year in promotions to users predicted to churn.

Assumptions:

• User value: $40 user/year
• Campaign cost: $20 user/year
• 100% success rate of campaign (profit is non-negative at >= 50% success rate)
• These figures are consistent with an annual revenue of $ 1 Billion and 5 million users (Lyft 2017).

Data

• city: city this user signed up in phone: primary device for this user signup_date: date of account registration, in the form YYYYMMDD
• last_trip_date: the last time this user completed a trip, in the form YYYYMMDD
• avg_dist: the average distance (in miles) per trip taken in the first 30 days after signup
• avg_rating_by_driver: the rider’s average rating over all of their trips
• avg_rating_of_driver: the rider’s average rating of their drivers over all of their trips
• surge_pct: the percent of trips taken with surge multiplier > 1
• avg_surge: The average surge multiplier over all of this user’s trips
• trips_in_first_30_days: the number of trips this user took in the first 30 days after signing up
• luxury_car_user: TRUE if the user took a luxury car in their first 30 days; FALSE otherwise
• weekday_pct: the percent of the user’s trips occurring during a weekday

Methodology

1. Feature Engineering

• Churn: The models try two definitions for churn:
  • Standard: A user is considered to have churned if he/she has not taken a ride in the last 90 days. This definition is broadly in line with the industry standard.
  • Aggressive: A user is considered to have churned if he/she has not taken a ride in the last 30 days. This definition is suitable for a company in an expansive stage, when it is willing to pay a relatively high retention cost to keep a user.
• Exogenous features
  • Categorical features with missing values were filled in with the mode for their specific group (by city and luxury_car_user). Missing value sin continuous features were filled in with the median value for such groups.
  • Categorical features, such as city, phone, and luxury_car_user were converted to dummy variables.
  • Continuous features that exhibited a log-normal distribution were logarized.
  • user_lifespan and user_rated_driver were created.

2. Modeling

The four models run were:

• Logistic Regression
• Random Forest
• Gradient Boosting
• Adaboost
• Gradient Boosting + Logistic Regression ensemble
• Random Forest + Logistic Regression ensemble

For each model, a Cross-Validation with Grid Search to optimize hyperparameters was conducted. The metric used to compare performance was the Area Under the Curve for the Reiceiver-Operator Curve (AUC/ROC), which portrays the tradeoff between true positive rate (TPR) and false positive rate (FPR) for Classification Models.

3. Results

• ROC curves

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