Have you ever noticed that if you and your friend book an Uber cab for the same route and distance, both of you may be charged differently? That is Uber’s dynamic pricing coming into play! Pricing changes dynamically depending on customer data, local events and demand, besides many other factors.
In a market where 78% of the customers consider themselves bargain buyers; dynamic pricing is a winner strategy for customer generation and retention. This is just one example from a host of ways in which technological advancement has changed the way car rentals function and operates.
Data is the Answer
Data Science has truly made conventional methods obsolete. In this age of data deluge, heaps of data is available. This reservoir can be analyzed to answer innumerable questions. However, the challenge here is no longer to seek answers. Instead, the challenge is to ask innovative questions. Data science and artificial intelligence have been massively adopted, by businesses, with 83% of the adopters known to achieve more than 30% economic benefits from it .
Technological progression has allowed car rental companies to use technology for gaining competitive advantage. In fact, these technologies have been the center force behind changing the nature of competition across businesses in the following ways:
- Data-driven innovation and discovery
- Data integration from heterogeneous sources
- Real-time analysis
- Radical personalization
- Improved decision making
Many innovative applications for car rentals have been identified, which include demand prediction, rate analysis, price discovery, meeting customer demand and determining customer satisfaction scores.
Data Pipeline for Car Rentals
Car rental services generate data at multiple levels. Tracking of car movement within and across cities can provide a huge amount of data when accumulated for all the cars running for this business. Analysis of data related to movement of vehicles can provide information related to the following questions:
- What is the demand for outstation car rentals between any two cities?
- What is the demand for short-term rentals at different locations?
- Is there an aggregation of rental cars at a specific location in the city?
- Is the demand seasonal with periodic variations?
- Who are the key competitors?
This information can be mighty valuable for rental companies to develop dynamically changing business strategies. For example, if there is aggregation of rental vehicles at a particular location around the same dates every year, this might indicate a repeated demand because of an annual event. A car rental company can accordingly plan for special offers and pricing models to win competitive advantage.
Using Data Across Domains
The beauty of data science comes from the fact that data from one lateral can benefit another lateral in unique and unimaginable ways. Going back to the previous example, an annual event with excessive car rental use also indicates significant tourist inflow, which opens doors of opportunity for hotels and homestays. In a similar manner, data collected from car rentals can be analyzed for the benefit of a plethora of businesses like asset management companies, real estate firms and investment companies, to name a few.
Some of the insights that can be directly inferred from car rental data include an analysis of a car rental firm’s health. More specifically, data related to a location can also be indicative of the health of a franchise. This information can be rather useful for investors and insurance or asset management companies to scrutinize the potential of possible investment. On similar lines, other possible insights that this data is capable of giving, include:
- Is there any new urban development in the area?
- Is there a festival or event happening in the location concerned?
- Is there is specific trend in demand for car rentals?
The analyzed trends and patterns can laterally have an impact on other related parameters. Demand for car rentals has an impact on car ownership patters and trends, which in turn affect the automobile industry.
The Bottom Line
The traditional method of conceptualizing a problem and collecting data to support a solution for the problem is bygone. Data science uses live data from different laterals to provide cross-domain insights, saving the costs of data collection and taking decision making to another level altogether. For all this and more, business intelligence is the future of decision making.
About the Author
Senior Vice President Car