Car companies excel at gathering data about everything: markets, trends, competitors, customer demographics and behaviours, marketing, sales, surveys, customer journeys, telematics, maintenance and repair records, campaign and dealer performance, and much more.
Yet, they seem remarkably inept at using it to enhance omnichannel customer experiences, boost sales, or increase customer retention. The prevalence of bottlenecks and blunders makes the lack of sophistication in utilising data quite apparent.
So, why is this the case? My theory involves three factors.
Firstly, the data isn't joined up to produce any meaningful insights.
A few years back, we linked NSC marketing with online traffic, leads, and dealership sales for the first time. This information was scattered across four databases with no apparent links between the data. When we finally connected it, it turned out that 80% of the people who bought cars at dealerships were nowhere near the "Buyer Personas" targeted for years by the marketing department.
Secondly, there might be an over-engineering problem: customer analytics seems to be treated with the same rigour as designing a new car. While changes on an assembly line can have dire consequences, tweaking email tracking certainly does not.
Meaningful and actionable insights don't require the most advanced "digital tracking layers" or yottabytes of data. A few key conversion points to start with, which can be tracked, refined, and expanded upon, are all that's necessary.
The third factor is mindset and skills.
Collecting and analysing data or designing "customer experience" doesn't demand the mindset of a top seller who wakes up every morning determined to sell one more car than the day before.
Also, the skill sets of data scientists and customer experience professionals don't align with those of salespeople, who better grasp the effective levers in their expertise – and vice versa.
So, who would have the mandate, expertise and skills to pull it all together and drive the change?
There is a solution: learning from retailers. They have honed the skill of transforming data into actionable insights. Gradually.
Imagine a cash-strapped retailer with an e-commerce site and a few pop-up stands in shopping centres, needing to self-fund growth.
They'd establish a 'war room', bring in the right digital sharks, set up key omnichannel conversion funnels, and ensure data collection to monitor the drop-offs at each step.
They'd then launch campaigns to drive traffic to the website and stands, track the results, analyse, learn, test, and optimise. Over time, they'd enhance the sophistication and complexity of their data and insights, eventually growing into predicting and influencing customer behaviour.
While starting small may seem like a step backward, at least the results wouldn't resemble outcomes from an abacus used in the middle of a data centre.
