Data analytics is increasingly playing a vital role in helping automotive companies improve efficiencies within operations, accelerate new revenue potential, and obtain competitive advantages. There are several types of analytics objectives to consider, which help make better and informed decisions for OEMs, dealers, data purveyors, remarketers, lenders, and automotive suppliers.
The effective use of analytics requires technology to handle and process vast quantities of data. Today, the availability of data analytics platforms and analytics tools make possible the processing of large amounts of data compiled from a wide variety of sources.
Canada’s auto industry has accepted this and for lack of a better term, ‘jumped in with both feet’.
Data Analytics Defined:
Data analytics at its core is the process of discovery, interpretation, and communication of meaningful patterns in data to improve business performance. This process often includes studying historical data to research potential patterns, to quantify the effects of certain decisions or events, or to evaluate the performance under a particular scenario.
As an example, descriptive data analytics is often used to evaluate and scrutinize historical depreciation patterns that can help a variety of automotive companies with their current and forward decision-making on pricing, inventories, and portfolios. Complex predictive modeling analytics can help automotive companies anticipate business challenges and make smarter decisions based on data visibility that impact profit potential or loss mitigation, elevating customer service levels and customer retention opportunities. With predictive analytics, historical data sets such as vehicle and segment pricing performance are mined for trends indicative of forecasted behaviors and patterns.
How Automotive Leverages This:
Data analytics allow automotive companies to leverage data mining tactics for the purpose of analyzing trends, to discover customer behaviors, resulting in enhanced decision-making based on analytical modeling.
For example, automotive dealers, manufacturers, and lenders are now studying specific historical data sets to analyze which vehicle segments react to varying times of the year. Users can get insights into trends and expectations for the spring selling season, summer sales patterns and new inventory arriving in the fall. What’s more, data is being scrutinized by region or province to determine remarketing pricing that can impact profit potential as well as days-to-turn ratios.
However, today’s operations are changing as spring selling season has broadened throughout the year, while new inventory also arrives year round. Therefore, data is being utilized, analyzed, and scrutinized further in order to get increased visibility into sales, pricing, incentives, lease return, and inventory trends, down to the exact day in many cases.
Data and analytics are being employed by automotive companies throughout Canada and the world, with strategies focused on cost-reduction, profit maximization, operational efficiencies, and smarter decision-making.
Here Is An Example: Residuals in the Ride Sharing Economy
Ride sharing has grown swiftly over the last few years, in Canada. To date ride sharing/hailing services are only legislated as legal in Alberta, Ontario and Quebec, which makes up well over half of the population of Canada. In August, it was announced by the Passenger Transportation Board in British Columbia that on September 3, ride share applications will be accepted and services will begin before year’s end.
Canadian Black Book research conducted earlier in 2019 by IPSOS, which surveyed over 1000 Canadians, suggested that 12% of Canadian rely on ride sharing. That number grows substantially to 27% of those aged 18-34 years old and an even higher number of 30% of those with less than a high school education. These numbers were calculated to include regions of Canada where these services do not exist, so the actual numbers in ride-sharing regions is likely considerably higher. Either way, this service is growing and certainly will have an effect on vehicle residuals.
The rise of more everyday vehicles into ride sharing programs means we are putting more miles on those cars every day. What kind of impact might this have on vehicle depreciation over time?
Below is an illustration of how ride sharing programs may have an exaggerated impact on depreciation for vehicles in certain segments where there is an increased likelihood of vehicles used for those programs.
Based on data analytics, a new vehicle valued at $30,000 would see drastic changes to its depreciation, depending on how much it was used as a ride sharing vehicle. Twenty-four months later, a vehicle of this value would be worth $18,000 when average miles were factored into its usage with no ride sharing. However, if the same vehicle that saw three times its normal usage or five times its normal usage, its 24-month residual would drop to $7,350, or $3,500, respectively.
Automotive players involved in retail, remarketing, lending, and even at the OEM level regarding overall brand/model retention would be interested in this type of data analytics to better understand the impact of increased ride sharing on ongoing depreciation levels.
Analytics go beyond the analysis of daily, weekly, or monthly pricing data. Instead, automotive audiences are relying on analytics as a way to customize analysis around specific and unique industry needs that can impact profit potential and balance sheets. The ability to mine specific data sets, with unique and custom analysis is enabling automotive audiences to maximize profit potential, reduce losses, and mitigate risk at every step of the automotive value chain.
Through its suite of wholesale, retail and residual values, updated daily, Canadian Black Book offers innovative data analytics technologies and comprehensive consulting services via a team of seasoned data analytics experts.