green flag
Companies must look beyond traditional data management strategies that inhibit speed, scale and innovation PHOTO: David Merrett

With increased digitalization in the automotive industry — and retail in general — customers these days expect much more connected experiences, all occurring with total respect for their privacy and data security.

Customers want their local retailers to know their history with the company and what their needs are likely to be whenever they call. Similarly, if a retailer contacts them, customers expect the information conveyed to be relevant to their situations. 

Designing a Brand New Data Platform 

At Toyota Motor Europe, we have spent the last year designing a data platform that helps our national distributors and local retailers provide the best possible service to meet those customer care standards and goals.

Our project required identifying and working through legacy challenges related to how we had collected, managed and used data in the past. 

Creating Seamless Omnichannel Access 

We began with the premise that our customers deserved to have seamless access to their information whenever they used apps, logged into websites, booked services or visited our retail locations — all with their data synched and updated in real time. 

It became increasingly clear that to provide our customers with the highest possible level of service, we needed to improve our data operations. 

To initiate the project, we created a very specific set of objectives that reflect the needs of almost any company in retail: 

  • Have all relevant information about our customers when they contacted us
  • Ensure that our customers or potential customers could contact us and access our offerings using whatever methods they preferred
  • Understand and predict the needs of our customers and potential customers so we could offer them the right products and services at the right moment using the right methods of communication, whether digital or physical
  • Learn how to improve our products and services on an ongoing basis, based on our customers’ feedback 
  • Respect our customers’ privacy and security expectations, as well as legal requirements, at all times

Seeking a More Efficient Data Structure

Before we designed our new data platform, all of our customer communication had been managed through our retailers or national distributors. Unfortunately, we started to realize that the data infrastructure supporting each distributor was causing inefficiencies in our marketing and sales practices and damaging our ability to provide innovative and personalized customer experiences across all channels. 

Customers might have had their names represented differently across two or more systems and we were unable to correlate them. For example, a customer who bought a Prius from one retailer and had that car serviced at another retailer might be listed two or more times in our national organization’s customer database.

We also found that the level of data consolidation varied greatly by country of operation. Some spent significant amounts of time creating central customer databases, while others built one for each business area. 

Different countries were also structuring their customer data in different ways, resulting in differing levels of quality and completeness. 

Searching for a Scalable Solution

We had previously attempted to employ several methods of consolidating customer data across the national distributors. We used external agencies, handcrafted batch scripts and full-on master data management (MDM) suites. 

While we found that these approaches could be effective with small amounts of relatively static data, none could be scaled effectively to meet our ever-increasing volume and data complexity requirements. 

We needed a solution that would consolidate customer data across the entire European continent, that was scalable and efficient and respected local needs and security requirements. 

We also needed a solution that would be acceptable to all distributors. However, we quickly realized that no single data structure would work for everyone, and that any attempt to create one would eventually cause resistance and limit the value of the platform. 

Scalability, Flexibility, Collaboration

Addressing these issues led us to an enterprise data unification approach, and a vendor, Tamr. We rejected traditional commercial offerings like MDM tools because their top-down approach required a single data model. 

Instead we wanted to pursue a bottom-up approach, and identified three mission-critical elements that guided our project: 

Scalability

The machine learning aspect of this approach eliminated a large portion of our manual effort. With one platform, we could map our many disparate data sources to a single view of the customer. 

Not only would this capability have been prohibitively cost- and time-intensive using traditional approaches, but the economies of scale generated by machine learning let us capitalize on our increased ability to add and integrate more sources of data.

Flexibility

We knew upfront that our data would evolve over time in both volume and structure, so we wanted to bake that flexibility into our data solution from the very beginning. 

We were drawn to the enterprise data unification approach because we knew that consolidating our data would never be a one-off exercise, and enterprise data unification would let us accept entropy, or disorder, in our data as a fundamental property. 

We also didn’t want to have to re-engineer our schemas and data models manually every time we added a new data source and our new model gave us the flexibility to adapt to new inputs and expert feedback seamlessly.

Collaboration

Including expert feedback in the generation of our model ensured trust and accuracy by making those closest to our customers responsible for data quality. 

Investing in Customer Service

Our experience illustrates that companies must look beyond traditional data management strategies that inhibit speed, scale and innovation. A forward-looking approach to managing customer data needs to include the ability to incorporate new data easily and quickly, while meeting rising privacy concerns. 

Our new data platform gives us the vital flexibility to change models and structures as our business needs change and evolve. We believe that our investment in planning and executing our new platform has already paid off by giving us the ability to satisfy our customers’ ever-increasing expectations for innovative, customized experiences.