Machine Learning in the Automotive Industry: Aligning Investments and Incentives

In the automotive industry, machine learning (ML) is most often associated with product innovations, such as self-driving cars, parking and lane-change assists, and smart energy systems. But ML is also having a significant effect on the marketing function, from how marketers in the automotive sector establish goals and measure returns on their investments to how they connect with consumers. ML is poised to become as much an organizing principle as an analytic ingredient for sophisticated marketing campaigns across industries. This is especially true in the automotive industry, a capital-intensive, high-tech sector riven by disruption.

Our global executive study of strategic measurement, “Leading With Next-Generation Key Performance Indicators,” highlights the widespread but uneven adoption of machine learning among marketers.1 78 percent of automotive companies invest in skills and training for ML. We see a gap, however, between the automotive industry’s ambition to use ML in marketing and the creation of incentives to use ML for marketing.

Even though most players in the automotive sector are investing in ML for their marketing efforts, a much smaller group is putting in place incentives and key performance indicators (KPIs) to use more ML and automation. Closing the gap requires a stronger commitment to developing a ML capability that is not just useful but also used.

The Auto Industry’s Adoption of Machine Learning

We surveyed more than 1,600 North American senior marketing executives and managers about their use of KPIs and the role of machine learning in their marketing activities; of these, 336 were from the automotive sector. In this group, 78 percent report that their organization is investing in new skills or training to allow marketing to more effectively use automation and machine learning. That percentage was 63 percent in the overall sample. Furthermore, 63 percent of automotive executives say that their organization has incentives or internal functional KPIs to use more automation and ML technologies to drive marketing activities. In the overall sample, just under half (49 percent) have such incentives. (See Figure 1.)