Over the past few years, the automotive industry has made positive strides in using data analytics for supply chain, maintenance, and marketing. However, when it comes to customer experience and engagement, there is still enormous scope for effectively using customer data and behavioral analytics to help OEMs understand:

• Key customer drivers to their brand
• The psychological attributes that determine customer purchase habits
• What makes customers switch to EV (electric vehicle) brands
• The experiences that shape a customer’s emotional connection with the brand

While traditional OEMs understand that competitive differentiation has shifted to customer experience, a traditional product and engineering focus mindset lingers on.

For instance, during the pandemic, most OEMs responded to market disruption by controlling costs and focusing on their best performing models but struggled to adjust to rapidly evolving customer behaviors. As a result, when the markets gradually moved toward normalcy, most OEMs found they were unable to compete with new entrants when it came to customer experience. The gap that already existed between OEMs and their customers, and in particular with their Millennial and Gen Z customer base, further widened and they were not able to capitalize on market changes such as:

• The purchasing journey moving online—from research and the customer review process to online offers and purchasing
• Evolving customer expectations and increasing demand for personalized experiences
• Brand affinity and loyalty driven by more emotive and experiential marketing tactics

By contrast, EV companies successfully connected with customers by:

• Positioning themselves as technology leaders in the electric vehicle category
• Using online as the primary channel of interaction for customer engagement
• Maintaining complete ownership of the customer journey—acquisition, engagement, and retention
• Focusing their retail centers on customer experience
• Offering value-added services such as Tesla supercharger network
• Running a tight network offering better residuals and used car values

Traditional OEMs are now responding to EV competition and addressing customer expectations of outstanding experience. To that end, OEMs have started adopting an agency model that puts consumers at the center and are taking ownership of the end-to-end car purchasing experience by engaging directly with consumers (see figure 1). The dealers, on the other hand, play the role of customer experience centers, with publishers and media agencies working closely with OEMs to promote the brand.

A CDP simply integrates customer data from various sources, such as CRM systems, transactional data, data collected from mobile apps and web sites, social media, customer service data, and many other sources to create a single view of the customer, enabling OEMs to perform a variety of analytics (see figure 2).

Leading cloud service providers, such as GCP, AWS, and Azure, provide reference architecture, pre-existing solutions, tools, and integration capabilities that can be used to build your own CDP.

Building a CDP involves seven key steps:

1. Data ingestion. This step brings together online and offline data from various sources, such as dealers, agencies, and publishers, and combines it with existing data within OEMs. These sources of data provide insights into buying behavior, demographics, preferences, interactions and engagement, and so on. Cloud providers’ existing components make it easy for data ingestion. For example, GCP provides solutions to ingest data easily into cloud storage.
2. Data integration and standardization. This is an important step in which duplicates and inaccurate data are removed to ensure high-quality, reliable data.
3. ID unification. This step stitches together data about a single customer from various sources under one unique customer identifier.
4. Data enrichment. The collected data is enriched by adding additional information that improves data quality.
5. Audience segmentation. The cleansed and standardized data is analyzed using various AI/ML tools, such as TensorFlow, and segmented into distinct audiences.
6. Customer journey analytics. The data can be used to create journey insights and analytics using various native tools offered by cloud providers, such as Looker.
7. Data activation. The audience segments can be fed into email, marketing, and advertising tools to deliver personalized experiences and engagement.

Working with specialist tech providers, such as Epsilon, BinaryCore helps OEMs develop a customer data strategy, bring together data from numerous sources to build customer profiles, and develop a single view of the customer (see sidebar: Spotlight on Epsilon). To find out more, please contact one of our experts below.