Cars are perhaps one of the biggest consumer products that someone can buy. Automobile makers have huge incentives to have satisfied customers since many will be repeat customers and recommend the same maker’s cars to family and friends. As with most industries, the automotive industry has become extremely competitive, so satisfying and retaining customers has become increasingly important. The use of big data analytics has become part of many automakers’ operations, using data from many sources to target marketing campaigns and provide good service.
Where’s the Data?
In the automotive industry, a great deal of data can be collected, including data from call centers, dealers, service centers, warranty systems, and sales and marketing databases. However, the ability to integrate all the data has been the barrier to effective use of all the information. In “The Evolution of the Customer Experience”, Ashwin Patil describes how some automobile manufacturers – with the aid of data analysis technology – are now able to integrate the data to form what he calls “household” segments.
This is done by analyzing the data for similarities on measures of such things as motivating factors for buying a vehicle, interests, risk factors, and likes and dislikes. By taking a “household” approach as opposed to an individual approach, manufacturers can better target marketing to the potential needs of the household as opposed to just an individual car owner. For example, manufacturers might target households who have young adults approaching driving age with advertisements for a vehicle in their line of products that is geared toward newer drivers. The ads themselves can be made in such a way as to appeal to these new drivers.
By using customer service data, manufacturers can also target owners of their cars who may have problems with offers for special services or products that are aimed to turn what might have been a negative experience into a positive one. Such actions can turn an owner’s thoughts from “It’s time to try another brand” to “This is a good company; they stand behind their products.” Importantly, Patil writes, the information that is culled from the big data analysis has to be shared across all the stakeholders in the company, such as marketing, sales, and service, so that the entire company – not just one part of it – benefits and can use the data to increase sales, service, and customer relations and loyalty.
The Key is Customer Relations
Preventing “leakage” – losing a customer’s loyalty after purchasing a product that prevents the customer from making a repurchase – is a primary goal of automotive customer relations according to Mishra and Kurihara in “Customer Service Analytics: A Game Changer in Automotive Customer Relations”. Analysis of data such as customer contact information, customer engagement and satisfaction with the automobile, services, and products, all play a part in preventing leakage. Manufacturers must go beyond straightforward mathematical/computer analysis of the data they get to determine when and why a customer is lost.
Mishra and Kurihara describe two data analytics categories, direction setting and strategy planning, that automobile manufacturers can use to improve customer retention. Direction setting analytics includes three sub-categories: leakage, causation, and predictive analytics. Analyzing data for leakage allows manufacturers to understand when, how much, and why people who purchase a particular make of car do not use dealer services or company products or purchase another vehicle of the same make.
With big data, manufacturers can study individual behaviors to understand the actions of specific segments of their customers and can develop action plans to reduce the leakage from these groups. Customer satisfaction is an important indicator of customers’ return business, so satisfaction indicators receive a great deal of attention in the analysis of data. Studying customer behavior after purchasing a vehicle can help manufacturers in predicting future customers’ behavior and developing ways to change those behaviors and to plan for the company’s future.
Strategy planning analytics also includes three sub-categories: price elasticity, geo-mapping, and simulation analytics. For example, “price elasticity analytics of after-sales services can identify potential retention opportunities through different pricing strategies”. Geo-mapping analytics can aid manufacturers in selecting sites for service locations, which can potentially provide increased use of the manufacturers’ services and products. Manufacturers can also use simulations using historical data to provide insight as to how business decisions will play out.
All of these types of analytics are available today. The big unknown is how much automobile manufacturers want to invest in analytics of big data. There is much to consider, including the costs for software, hardware, and employee training. This has to be balanced with the return on the investment in customer loyalty and, in turn, increased retention through to repurchase.
However, there is not doubt that some of these analytics tools will be valuable to automotive companies in the future. As more tools to gather data are developed, companies will search for people to interpret that information. To find out more on how to start your career in data analytics, click here.
Patil, Ashwin, and Craig Giffi. “Big Data and Analytics in the Automotive Industry | Deloitte US | Automotive Manufacturing Industry.” Deloitte United States, 24 May 2017, www2.deloitte.com/us/en/pages/manufacturing/articles/big-data-and-analytics-in-the-automotive-industry.html.
Mishra, Sharad Mohan, and Masanobu Kurihara. “Customer Behavior Analytics: A Game Changer in Automotive Customer Retention .” Big Data and Analytics in the Automotive Industry, 2015, www2.deloitte.com/content/dam/Deloitte/us/Documents/manufacturing/us-auto-automotive-news-supplement.pdf.
Author: Neil Starr