Have you ever shown up to a car rental counter only to find the lights off and a sign stating “no cars available”? The chances are good considering that revenue management for car rental companies has been referenced as being more difficult than nuclear physics. A $100 billion global industry with a >20% no-show rate, rental car companies are beginning to utilize artificial intelligence to minimize oversell situations and maximize revenue on rapidly depreciating assets.
An existing (and oversimplified) view of factors considered by mainstream rental agencies in vehicle pricing, positioning, and segmenting.
From rideshare giants to platforms that allow private citizens to rent out their own cars to complete strangers, rental car companies are being challenged by the sharing economy. These nimble players are delivering real price pressure and improved customer experiences for today’s tech-savvy travelers. So, like their peers in the evolving travel industry, rental car agencies find themselves quickly needing to up their game in terms of customized experiences and improved accessibility. This race is a sprint and rental car management teams need to leverage new technology. Beginning with drastic changes to the pricing and availability components of their revenue management systems, operators can better determine pricing, allocation, and fleet distribution decisions over an extended horizon.
Today’s delicate line between profitability and an oversell situation can be overcome by utilizing a series of inputs, with revenue management systems ingesting incoming flight data, local events and seasonal trends, all factors that drive deep analytical insight. However, as proven in other segments of the travel industry, weather data can be a force multiplier in terms of driving profits for the rental car agencies. By using a weather data API to learn from historical weather influences as well as incorporating current conditions and forecasts, today’s operators can thread the needle on supply levels, improve pricing models and maximize ROI on automobiles, the world’s favorite example of a “bad investment”.
While typically part of a robust revenue management strategy, demand in and of itself is a hard to calculate metric. With branches holding a limited number of cars and one-way rentals constantly reshuffling inventory away from high-demand pick up points, the science of shifting cars to where they are needed most can be influenced by a number of factors. A car rental agency’s asset management strategy needs to factor in near-term demand for forecastable natural disasters and tropical weather events, such as when Hurricane Harvey made rental cars a hot commodity, as thousands of Houstonians lost their personal vehicles. Auto inventory is also impacted by data points like snow coverage and severe weather alerts, which can result in trip cancellations. Utilizing these and other factors can change the long-term operational success of an independent or multi-branch organization.
Many renters play fast and loose with their insurance policies, remaining uninsured or utilizing a secondary insurance provider. Pricing for these ancillaries can be optimized by weighing demand centered around factors such as inclement weather. Quantified higher demand for an insurance product can be crunched against increased risk factors within certain classes of cars to adequately price against the conditions. Optimizing ROI and reducing risk of catastrophic damage in the wake of an accident involving an uninsured driver results in less headache for both consumers and rental agencies. Minimizing this risk gives a massive boost to driver peace of mind as well as financial security of an agency, as writing off a luxury class car due to a rainy day hydroplane can seriously dampen a business’s outlook.
Ultimately, the applications of weather data within the car rental revenue management space are endless. With countless data sets being used to shift price, inventory, and staff, a high-value weather data source can pay for itself many times over. AerisWeather provides a comprehensive weather data API and matches this data with a tile mapping service that encompasses historical and current observations alongside future weather forecasts to power thousands of travel industry use-cases.
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