The ride-hailing industry is fiercely competitive, where pricing, driver availability, and customer demand fluctuate rapidly. To stay ahead, companies must track competitor moves in real-time—from surge pricing and demand patterns to driver availability gaps and promotions. Web scraping, the automated extraction of data from websites and apps, has become a critical tool in competitive intelligence. Some of the major industry players use it to monitor rival pricing, incentives, and customer sentiment, enabling smarter pricing strategies and market expansion decisions.
Web scraping provides comprehensive competitive intelligence by extracting valuable data points across various dimensions:
Track fare fluctuations across different regions and time periods to inform dynamic pricing strategies.
Identify competitor service gaps during different periods, including availability of specialized services (comfort, accessibility, luxury, electric options).
Monitor ride request surges during peak times to optimize driver allocation and pricing.
Extract competitor coupon codes and promotional discounts to develop counter-strategies.
Track competitor surge multipliers in real-time to make strategic pricing adjustments.
Extract competitor Estimated Time of Arrival data to improve service efficiency metrics.
Analyze how competitors structure fares (base, per-mile, per-minute, surge, taxes) to refine pricing models.
Identify differentiated pricing for various user segments (new users, frequent riders, corporate customers).
This real-time intelligence feeds directly into pricing models and demand forecasting, ensuring companies maintain a competitive edge through data-driven decision making.
Companies can adjust pricing dynamically to stay competitive by tracking competitor fare fluctuations. If Company A's price surges in a neighborhood, Company B can update its price relatively by either choosing to increase, decrease, or keep it the same. One of the fundamental formulas utilised to calculate the price is as follows. This can be further refined based on each company's specific conditions. A pricing optimization model might use:
Allowing dynamic fare adjustments that maximize revenue while staying competitive.
Components:
Impact:
This model ensures pricing decisions factor in brand strength, leading to more effective revenue optimization while maintaining a competitive edge.
Scraping competitors' app data on ETA (Estimated Time of Arrival) helps companies identify areas where customers face long wait times. If Company A shows high ETAs in a busy area, Company B can adjust pricing or deploy promotions to attract riders seeking faster service. Companies can ensure better availability, reduce rider wait times, and improve overall customer satisfaction by analyzing competitor delays.
Monitoring surge pricing and wait times across competitors reveals demand hotspots. A forecasting model might use:
This equation predicts ride-hailing demand D(city,hour) in a given city (this can be made more specific by considering area zipcode over an entire city) and hour by analyzing past demand from both the company's own platform D(self) and competitor platforms D(comp).
Components:
Usage:
Ride-hailing companies track competitor promotions, personalized discounts, and seasonal offers to ensure customers receive the best value. By analyzing competitor discounts and persona-based pricing for new riders, frequent users, or corporate accounts, companies can adjust their own promotions to attract and retain customers. If a competitor launches a holiday discount or loyalty program, you can win price-sensitive customers by proactively offering targeted cashback or limited-time fare reductions. This ensures customers always feel they are getting the best deal and a superior experience.
Data-driven decision-making using scraped competitor data leads to higher revenue, better cost efficiency, and improved market positioning. We have consistently been having conversations with our clients about the impact they've seen when it comes to strategically utilizing competitor data. Here are the average stats they have reported in terms of pricing forecast error, market share growth, and revenue growth -
A ride-hailing firm using competitor intelligence sees:
In today's fast-changing ride-hailing industry, using web scraping to gather competitor data isn't just helpful but essential. Companies that use real-time insights from their competitors can set better prices, improve service in key areas, and grow into new markets more effectively. By tracking parameters like competitor fares, surge pricing, and ETAs, ride-hailing businesses can make smart, data-driven decisions that boost revenue and help them win more customers.
At Anakin, we specialize in delivering precise, timely competitor intelligence data tailored specifically for the ride-hailing industry. Schedule a call with us today to explore how our customized data solutions can help your business gain a competitive edge.
Written by Anakin Team
