Optimizing Ride-Hailing Discount Rates to Maximize Order Growth and Total Revenue

Project background: The ride‑hailing business’s profit mainly comes from the margin between sale price and cost price, and determining a discount that can generate revenue without harming user experience is a key research topic.

Revenue model: Total revenue = successful orders × average profit per order. Generally, higher discounts increase order volume but reduce average profit, prompting the question of which discount maximizes order growth speed and total revenue.

Solution approach and plan

Variable and sample selection

Data preprocessing

Identify price thresholds and the revenue‑maximizing “golden point”

Methodology: Apply least‑squares curve fitting to the relationships between discount & successful orders and discount & total revenue, then use calculus (first derivative of a cubic function for orders and vertex of a quadratic function for revenue) to locate the optimal discount points.

Tools: SPSS

Specific solution details

The analysis shows that successful orders follow a cubic function of discount with R² = 0.83, indicating a good fit. The derivative peaks at a discount of 84% (i.e., 16% off), which is the point where order growth speed is fastest.

Total revenue follows a quadratic (downward‑opening) function of discount with R² = 0.98. The vertex occurs at a discount of 86% (i.e., 14% off), representing the maximum total revenue.

These two optimal points are close for the examined city, allowing a single discount strategy to simultaneously boost order volume and maximize revenue. However, in many other cities the optimal points differ significantly, requiring tailored strategies based on specific goals.

Author: Shi Jiajia, Data Analyst, Tongcheng‑eLong Transportation Division (10+ years of data analysis experience).