Based out of New York, America, the company provides car rental services across various locations in North America, South America, Europe, Asia, and Australia. The company provides car rental services across major airports and off-airport pickups.
The company offers services that are billed by the hour or day or a monthly or annual card in addition to reservation charges. The customers can book any vehicle of their choice like a sedan, standard SUV, jeep, commercial van/truck, and more. For example, their fleet inventory provided a lot of options right from economic cars like Mitsubishi Mirage, Ford Fusion Hybrid to high range convertibles like BMW M4 Convertible, Porsche 911.
The company approached us to understand why their customers preferred some of their cars while some of their cars stayed so long in their inventory without being rented even once. It was crucial since many of their regular customers opted for other auto-rental services as their desired cars were not available from the company.
Most of the information was either incomplete or missing as they relied on spreadsheets and siloed systems. Large scale data had to be aggregated for about 8000 rental cars. More than 100 attributes like price, demand, location, extra amenities, car model, and more had to be collected for each vehicle.
The in-house analysts of the company stored all the information on Excel sheets and they were spending more than 47 hours a week to provide the rental insights. Even then the accuracy was shaky. This scenario made real-time fleet optimization nearly impossible and bogged down the customer satisfaction rate.
With their in-house teams tied up to do mundane tasks like extracting the right information to analyze, the company wanted a solution that could,
Automate their redundant tasks of finding the right data to analyze
Understand the exact reason for the high demand for certain rental cars
Enhance their customer experience ensuring the demanded car is available in their inventory
To better understand the information relevant to the company, our team analyzed the company’s existing information and reports. We analyzed the car models, technology, band of customers, and the regions in which the company operates.
Aggregating all the required information into a comprehensive system
Insights provided: Cadillac, Buick has a higher demand over other cars.
Delivering the right insight to meet the demand
Insights provided: Toyota is the most preferred car in the economy models.
Insights provided: Customers preferred cars that had BlueTooth wireless.
Better investment decisions on high demand cars
With our business intelligence, the company analyzed which models were often chosen by the customers. This enabled the company to buy cars that rented well and sell those that weren’t performing well. The company improved their inventory turnover by 28%.
Enhanced customer experience with higher revenue
With our demand monitoring system, the company predicted the demand based on various criteria like price, make, and features. An adequate fleet of cars was available even at times of maximum demand without lowering their tariffs.
Top 7 metrics identified
30+ KPIs analyzed
28% increase in inventory turnover
100+ attributes for 8000 cars aggregated