Each month it seems like we hear automotive original equipment manufacturers (OEMs) reporting record growth, but what is driving this growth? Pent up demand? Attractive new products? Is the market actually growing, or could it be that the automotive industry is starting to use big data and analytics to better understand their customer, what they want to buy, how they want to buy it and how they want to interact with their car and their car company?
Let’s start with a quick definition of big data and analytics. Very simply put, big data combined with analytics generates the ability to analyze any and all types of data, whether internal or external to the organization, structured or unstructured, at rest or in-motion (streaming).
Big data platforms, such as Apache Hadoop, are enabling automotive OEMs to understand their customers like never before. Traditionally, OEMs have struggled to build customer data and analytics beyond the typical silos of business functions, such as call centers, sales and marketing. In fact, many OEMs outsourced these business processes and the data that goes with them. With the evolution of big data and analytics capabilities, OEMs are now looking for ways to bring all of this rich data back in-house, combine it with other internal data such as VIN, warranty and telematics and also leverage external sources of data such as social media and insurance to create a deeper understanding of the customer and the customer’s household and extended relationships.
Big data and analytics is the answer to uncover insight and improve decision making.
- For example, a family may have a teenager who will likely receive a driver’s license in the next six months. The automotive OEM marketing department may use this event, along with an understanding of the vehicles currently owned by the parents, as an opportunity to market vehicles to this family touting safety and reliability ratings.
- Another scenario might be that a current vehicle owner has been seen on the internet searching for boat and trailer insurance information. Knowing that the customer does not own a vehicle with sufficient towing capacity for a boat, the marketing department may use this information to market vehicles with the powertrain to tow a boat.
The examples above show how big data and analytics are being used to improve next-best-action. Taking the best action based on a deeper understanding of the customer at the point of engagement is driving a new level of sentiment and loyalty with customers, resulting in increased growth for OEMs. Next-best-action is a data-driven interaction with the customer as opposed to the very one dimensional “scripted” dialogues defined with if-then-else interaction. Using big data capabilities, such as master data management and Hadoop, to create a 360 degree view of a customer, their relationship and engagement history and their current situation, and prescribe a resolution or treatment based on customer preference is driving better outcomes and results in customer loyalty and future sales.
Automotive OEMs spend millions of dollars on incentives and loyalty programs to acquire, grow and retain customers. Using big data, automotive OEMs are able to better understand their customers’ buying behaviors, sentiment and response to incentives and goodwill. With a new, deeper understanding of the customer, OEMs are able to track customer interactions across channels such as call centers, web and mobile, and map that customer behavior to incentives and treatments offered by the OEMs intended to retain customers and drive loyalty. OEMs can now track the effectiveness of programs against real customer responses and hopefully their next purchase.
Big data is indeed available to automotive OEMs and we are seeing more and more examples of the value in the data. Those who see big data as a valuable asset, and act on it, will see their business acquire, grow and retain customers.
- Enhance your 360-degree view of the customer
- The next revolution in decision management: Capturing big data