Will new breed of data scientists enable dealerships to profit from imminent data deluge?

How will dealership sales staff cope when their legendary sex appeal is challenged by new dealership hires with a penchant for selling the joys of statistics rather than sleek saloons? These red-hot recruits are the techies soon to be tasked with analysing the huge amounts of data that automotive retailers will have to scrutinise as emerging omnichannel sales models become the norm.

The new breed of Data Scientists, as the heralded byte-crunchers are known, will serve as the interfaces between web search-based data and showroom sales agents. Before you scoff at the suggestion that these next generation techies can also have sex appeal, have a read of the influential Harvard Business Review, a publication not known for relying on clickbait headlines to gain attention, which recently heralded the Data Scientist as 'the sexiest job of the 21st century'.

These specialists are already among us. Vacancies for data scientists are advertised in business and technology magazines and on jobsites (albeit with scant reference to the libidinous benefits that apparently go with the occupation). The techniques of data analytics – the data scientist's stock-in-trade – are the subject of multiple conferences and seminars this year. Right now most of the vacancies are in well-healed sectors such as financial services and luxury retail but according to Nick Gill, a senior VP at Capgemini and Chairman of its Automotive Council, the automotive market is set to join the ranks of early adopter markets.

According to Gill, dealerships should be getting ready for their own version of the 'data deluge' that's been flooding other retail sectors for some time now. There is a growing realisation that massive ‘Big Data’ sets may prove key to developing reliable and consistent new and used car sales opportunities in the 2020s and beyond.

Such opportunities will be achieved by 'mining' big data sets to profile car buyers, and predictively reveal characteristics that will inform courses of action that dealers can leverage to win loyalty, boost customer experience satisfaction and, most importantly, close more sales.

Gill explains;

Data scientists start with a hypothesis - such as people living in a certain area, and of a particular demographic, are more likely to buy an electric car, they will then 'mine' data from a wide range of sources to substantiate the theory, and if it stands up, they identify the people to target with that particular message.

There are various definitions of data analytics knocking around. One that will probably be of most use to car dealerships is explaining it as 'the process of examining large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other advantaging business information'.

Delving into their data would enable dealers to uncover finer detail, such as which customers and prospects will be buying a car in the next three-to-six months; the type of vehicle they are considering; and the price they want to pay, adds Gill: “Having an accurate picture of [which customers are] likely to remain loyal and which will churn, is valuable... If you know one of your customers is switching from one brand to another, as a group with both brands in your portfolio, you can….introduce them to the sister dealership – and [thereby] keep their custom in-group.” 

The ability to gain predictive insight would naturally enable dealers to focus their sales and marketing strategies on demographic categories likely to yield greatest success. Gill predicts that, initially, dealer groups will commission data science services ‘from specialist providers’. Who knows, perhaps they will call on Capgemini itself! But he thinks that within five years dealers will be employing their own data scientists. For the appointment to work, however, dealers need to change their view of data, Gill warns, and recognise it as “a genuinely scientific approach to communicating with car buyers”.

Whatever approach they adopt, car dealers will have no dearth of data to mine. CRM solutions, omnichannel platforms, and online customer enquiry systems are already generating petabytes of the stuff. But it is the connected vehicle revolution that many market-watchers see as unlocking masses of commercially-actionable car usage-based intelligence. Smart dealerships will position themselves to ensure they are qualified to exploit this rich seam of intelligence. 

However there is no doubt that data science still has real technical challenges itself. One of the bugbears of analytics is that vast amounts of data have to be analysed in order to locate often very small seams of insight, which then have to be further scrutinised and verified manually. 

This process can take ages, by which time the business leads it reveals may well have timed out. The hope is that the arrival of the connected car in large numbers will be a driver for tech investment that will deliver the increased processing power and the more efficient algorithms needed, to sift the big data sets quickly enough, to put the new intelligence to work before it goes stale.