Applying artificial intelligence to automotive marketing and sales

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“AI is here today in automotive and can provide tremendous value for OEMs”—what we described in our January 2018 report on artificial intelligence (AI) in automotive remains true. And it’s particularly relevant to automotive marketing and sales (M&S). That’s why we chose to focus research on the subject; our findings are captured in a new report, How to win tomorrow’s car buyers—artificial intelligence in marketing and sales. This article provides highlights from the full report.

Artificial intelligence in marketing and sales

Artificial intelligence (AI) is the ability of a machine to perform the cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics, autonomous vehicles, computer vision, virtual agents, and machine learning.

AI is already used in numerous sectors, particularly consumer businesses, which have been collecting large volumes of diverse customer data for a considerable amount of time, allowing the development of particularly effective AI systems.

Emerging trends

AI is driving three megatrends in marketing and sales (M&S)—automation, forecasting, and personalization—representing the areas in which marketing leaders should focus their energies on building capabilities:

Automation allows machines to make decisions, or at least to assist human decision makers. Typical examples include dynamic pricing and systems for product recommendations. Automation is currently particularly relevant to online retail and has clear disruptive potential. This will only become more pronounced as technology evolves to make completely independent decisions.
Forecasting depends on having a strong set of historical data to feed AI, which can then find patterns and develop models of future scenarios. In this way, we can predict typical customer behavior, such as purchasing decisions and patterns of fraud. These systems are not static; they are always learning and adapting to new data contexts.
Personalization refers to the application of forecast results to tailored customer segments and individuals. Using comprehensive customer information, the system analyzes each person to make carefully targeted, personalized promotions, products, and price offers. AI is particularly powerful in this context, because it is able to execute such a scenario on a large scale, meaning it can deliver personalized marketing to every consumer.

Accordingly, the AI-enabled use cases that we discuss in the full report How to win tomorrow’s car buyers—artificial intelligence in marketing and sales address the trends of automation, forecasting, and personalization as well as their related challenges or pain points in automotive M&S.

In many industries, leading companies are already effectively utilizing AI in their M&S activities (see sidebar, “Artificial intelligence in marketing and sales”). This allows them to take their performance to new levels in a few different ways:

Utilizing rapidly expanding data availability. These companies are, for example, drastically increasing their marketing efficiency through programmatic advertising with AI at its core.
Drawing from data connected throughout their customers’ entire journeys. These companies are using AI to personalize their sales efforts to up- and cross-sell to customers with individualized offers and personalized pricing.
Deploying AI to all M&S operations. These companies, most notably, are demonstrating how AI can be used to automate and optimize M&S operations, such as sales forecasting, product configurations, stock allocation, and after-sales operations.

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In this context, we can offer a comprehensive perspective for automotive players on three high-priority questions concerning the “AI-enabled new normal” that are already emerging in other industries:

What is an automotive OEM’s starting point—that is, where do these organizations stand as they face emerging new realities in automotive and adjacent industries’ M&S, and which value-capturing opportunities does AI in automotive M&S provide?
What are the key contexts, use cases, and enablers for introducing the AI-enabled new normal in M&S from adjacent industries to automotive, and how can these be turbocharged?
Which steps should automotive OEMs take right now to lay the foundation for and facilitate fast and substantial AI-enabled performance improvements in their M&S?

At the heart of our research is a detailed discussion of the AI-enabled use-case landscape along the automotive M&S value chain. An exhibit lays out what a transformation might look like.

Three facets are crucial to an artificial-intelligence transformation for automotive marketing-and-sales operations.

In our attempt to answer the questions above, we drafted four key messages on the value and application of AI in automotive M&S. Each of these statements is explained in more detail in the report:

AI’s M&S value comes from improved customer experience, higher sales, and lower costs. M&S teams of automotive OEMs and their franchised retailers can capture massive value if they can translate AI’s potential—and that of its increasingly sophisticated techniques and tools—into concrete change. This value is primarily generated in three dimensions: improved and consistent customer experience, which is becoming increasingly important, especially since customer experience is expected to soon beat brand loyalty when it comes to determinants of customer decision making; higher sales, resulting from more relevant offers to customers and an increase in customer satisfaction; and reduced M&S costs.
AI in M&S is not just a competitive advantage but benefits the entire automotive industry. The benefits of this technology transformation will be, on the one hand, valuable to the entire industry, in that the efficiency of media budgets, tactical sales budgets, and margin improvements from more customer-centric packages and pricing are improved. The industry-wide benefit of those effects is estimated to reach $44 billion by 2025, or up to 2 percent of the total operating margin. On the other hand, applying AI to automotive M&S is expected to be a crucial differentiating factor among automotive OEMs and between incumbents and new, digital competitors.
AI can and should be deployed in all areas of M&S, from marketing to sales to operations. AI is at the heart of future-proving technology, turbocharging M&S organizations. We propose a three-faceted approach to deploying AI in automotive M&S:

Explore AI in automotive marketing to reach customers at the right moment with a tailored, individual message. Through programmatic advertising, with AI at its very core, automotive players can let AI determine which customers they should target, with which messages, at which time, and via which channel.
Integrate AI into automotive sales to make selling consistent and personal. In connecting sales to previously used marketing data sets, automotive players can utilize AI for more effective sales. For example, AI can be employed to automate lead-management activities, predict the products most likely to be up- and cross-sold to customers throughout their life cycles, and enable personalized and dynamic pricing.
Transform all M&S operations through AI to improve M&S insights and increase the efficiency of functions. Automotive players can apply AI throughout all automotive M&S operations to improve sales forecasting or vehicle configuration and distribution significantly. AI’s ability to extract meaning from unstructured data (such as text, sound, images, and video) can be used to automate and optimize after-sales operations, for example, through chatbots and virtual assistants or by automating the visual inspection and residual-value calculation of returned used cars.

To conduct an AI transformation of an organization’s M&S successfully, three steps are key:

Understand the individual starting point. Create an understanding of where and how AI will deliver the most value to the organization and of the capabilities and enablers required.
Pilot/build first AI-enabled use cases. Develop and implement the most promising use cases to test, learn, and demonstrate the value AI can provide and, finally, to develop a personal AI strategy.
Transform the core. Finally, transform the organization, building the required AI capabilities to scale up AI-enabled use cases and utilizing the value of the technology in every function, process, and aspect of a business.

Download How to win tomorrow’s car buyers—artificial intelligence in marketing and sales, the full report on which this article is based (PDF–2.1MB).