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How can automotive OEMs use analytics to differentiate themselves in an evolving industry?

Nagarro | México

As the auto sector witnesses its biggest evolution yet, data science, analytics, and artificial intelligence can help OEMs keep up with the changing landscape and act as a differentiator by delivering value across functions, especially marketing, sales, and operations.

Data volume is constantly growing, and the decisioning system are also moving from passive to automated systems. Meanwhile, analytics is also evolving. In an automotive context, decisioning systems are gradually evolving from passive to automated. 

How OEMs can leverage analytics for automotive marketing

On average, a customer spends 14 hours online researching different models and specifications, and two-thirds of them make a decision online. Organizations can use different analytics-driven approaches to identify the right customers, choose the right channel and analyze the impact of their campaigns. Causal and incremental modelling can help identify the right content for a campaign, whereas programmatic advertising and attribution modelling can help select the right channels and assess a channel’s performance respectively. Incremental return on advertising spends helps assess the performance of marketing

How automakers can use analytics to achieve better sales

Buying a car can be stressful and tedious for consumers as well as for sales teams. Analytics can help in the following stages of the sales process:

• Lead management: Modeling and analytics can fully automate all three stages of the lead management process – lead generation, lead qualification, and lead conversion.
• Cross-selling and up-selling: OEMs possess essential customer data, which they can leverage using analytics to cull insights for successful upselling and cross-selling and help organizations understand which service/product a customer prefers.
• Dynamic pricing: Advanced analytics solutions can assess customer interest in a product and dynamic pricing can be set accordingly. 

Streamlining automotive operations with analytics

For efficient operations, OEMs must understand the market trends and demand for specific vehicles and models at the dealership or regional level. Building simple forecasting models and improving them over time is much more effective than conducting surveys. Also, machine learning and data science can help OEMs build models that can understand thousands of SKUs and create accurate forecasting models. Automakers can use analytics applications such as customer service bots and document processing automation for processing requirements. 

AOEMs can apply analytics within their organizations with three simple steps – discovery, piloting, and scaling. They can begin by identifying the areas for analytics intervention and understanding how analytics can bring value to these areas. Follow this with launching pilot projects that can be scaled depending on their success.

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