Projecting Driver Tenure

Solution & Explanation

Estimating Driver Tenure

To estimate how long a new driver will remain active on the platform, we can use several approaches:

1. Survival Analysis:

   • Definition: Survival analysis is a statistical method used to predict the time until an event of interest occurs. Here, the event is the driver quitting the platform.
   • Data Preparation: Prepare the dataset by marking the start and end dates of each driver's activity within the 90-day period.
   • Modeling: Use the Kaplan-Meier estimator or Cox Proportional Hazards model to estimate the survival function, which gives the probability that a driver will stay active beyond a certain time.
   • Interpretation: The survival curve will show the likelihood of drivers remaining active over time. This can help identify the median tenure and factors influencing churn.

2. Cohort Analysis:

   • Definition: Cohort analysis involves grouping drivers who started at the same time and tracking their activity over the 90 days.
   • Application: Calculate the retention rate for each cohort at different intervals (e.g., 30, 60, and 90 days).
   • Extrapolation: Use these retention rates to predict future trends and estimate the average tenure by fitting a model to the retention curve.

3. Machine Learning Classification:

   • Feature Engineering: Extract features like ride frequency, earnings, ratings, and participation in promotions.
   • Modeling: Train a classification model (e.g., Random Forest, XGBoost) to predict whether a driver will churn based on these features.
   • Evaluation: Use metrics like accuracy, precision, and recall to assess the model's performance.

Estimating Lifetime Value (LTV) of a Driver

The lifetime value of a driver is the total profit they generate for the platform during their active tenure. Here's how to estimate it:

1. Calculate Daily Revenue:

   • Data: Use the 90-day ride data to calculate the total revenue generated by each driver.
   • Average Daily Revenue: Divide total revenue by the number of active days for each driver to get the average daily revenue.

2. Estimate Lifetime:

   • Use Estimated Tenure: Multiply the average daily revenue by the estimated tenure derived from the survival analysis or cohort analysis.

3. Adjust for Costs:

   • Net Revenue: Subtract any direct costs associated with the driver (e.g., incentives, bonuses) to get the net revenue.

4. Consider Seasonality:

   • Seasonal Adjustments: If possible, adjust for seasonal variations in demand and driver availability, which may affect revenue.

5. Sensitivity Analysis:

   • Scenario Testing: Test different scenarios (e.g., high churn, low churn) to understand the range of possible LTV outcomes.

Conclusion

While the 90-day dataset provides a snapshot, it is essential to acknowledge its limitations, such as potential seasonality and the short observation period. Ideally, access to a more extended dataset would allow for more accurate predictions. However, by leveraging statistical techniques and machine learning models, we can derive meaningful insights about driver tenure and lifetime value even from limited data.