The car rental industry today is enjoying a boom amid rising disposable incomes, improved air connectivity across smaller cities, and healthy growth in business and leisure travel. The U.S. market had its best year ever in terms of revenue in 2018, clocking a 4.8% jump in top line to over $30bn. Even more impressive was the fact this record revenue was realized on smaller fleet base, with revenue per unit hitting an all-time high of $1,131 per month.

While the above tailwinds are expected to continue over the near term, the industry cannot afford complacency – particularly with regard to revenue management and price optimization. The highly dynamic and competitive nature of the complex marketplace makes it imperative for operators look beyond historical trends for fine-tuning their demand, price and yield management strategy.

AI insights for dynamic pricing

Conventionally, car rental companies set prices and business rules by deploying large teams who carried out time-consuming, exhaustive manual analysis to anticipate the fees customers will pay for products and services. However, with on-demand, personalized, omni-channel consumption exploding in the digital age, and unprecedented volumes of customer data being generated at various sources, this legacy method is no longer optimal.

This is where the industry must leverage artificial intelligence (AI) to attain dynamic pricing, and boost revenue per unit, average rates and utilization. AI can seamlessly factor in numerous variables–including location, seasonality, real-time demand, individual buying patterns, competitors’ pricing and time of day–to match demand and supply, and uncover evolving demand patterns. Other factors the algorithm considers include inventory availability and in-house historical sales data.

For example, AI can figure out if a given car attracts higher demand in a locality–at a specific time of the day–and trigger surge pricing without impacting other vehicle segments. Moreover, self-learning algorithms can configure such patterns for one car segment and one car out of thousands of combinations.

Another unique feature of dynamic pricing algorithms driven by machine learning and other cognitive technologies is that it can also project the likely responsiveness of individual clients to special offers. AI does this by mining the different data patterns associated with a buyer, spanning multiple sources such as postal codes and loyalty cards, and assessing the marketplace conditions.

That’s not all–dynamic pricing enabled by AI can help operators simulate different price ranges to test if buyers would opt for newer vehicle models instead of older ones. Another use case can be to test the potential efficacy of a planned promotion, and change the pricing to maximize adoption.

Overall, AI-based dynamic pricing does not depend on sub-scalable approaches like linear programming and traditional pricing rules, but learns from data to refine decision making continuously.


For revenue managers like you, AI is not a threat but a major enabler that can help you become more productive. Look at this disruptive technology as the next chapter in the evolution of rental pricing. Unlike in the past where you had to pitch rates to many stakeholders and key in new prices, today you can use AI to enhance price prediction and optimization.
In short, AI offers a significant opportunity for you to target each of your customers strategically at the right moment in their buying journey, and deliver a streamlined, on-demand experience.

About the Author

Anup Dhiraj

Associate Vice President – Product Management, RateGain
Leading Product Management Group and driving Engineering/DevOps team with a vision of creating most innovative price intelligence and revenue management products in travel technology.