3 min read
15 Oct

First up, I don’t drive nor own a car. In fact, I can’t drive, and I have never taken a test. I might in the future, who knows? But my previous automotive clients LOVED cars and despaired at my response of ‘Red’ when I was trying to describe a car I liked. Over the years I’ve worked with Honda, Audi, Ford, Jaguar Land Rover, Volvo, Peugeot, Citroen and DS. Mostly on multi-market engagements related to long term budget setting.

For a non-driver, I had an unusually broad knowledge of these car brands – and their main competitors: which models are in which market segment; what ‘wheelbase’ meant; and the average age of a new car buyer. I say had, because things move quickly, and it’s been a few years since my last auto assignment now. Hybrids were around but Electrics were in their infancy.

I did a team-building thing once where we drove a Land Rover off-road while blind-folded – all the “non-drivers” in the group performed the best in this activity because they were 100% reliant on the verbal instructions provided to them by their team mates and none of their own ‘driving expertise’ got in the way. It’s similar if you’re a consultant for another business – it’s not necessary to have much specific knowledge (at first).

In fact, as far as data analysis is concerned, my unfamiliarity with cars and driving was an advantage. OK, I needed help with the terminology, but, for example, I was able to interpret new car buyers survey data objectively, not having bought a car myself. I had no pre-conceptions and made no assumptions – the data told its own story and I worked further with it to turn that into some recommendations.

I loved all the auto projects because they were so challenging. It’s not just marketing and sales. Models are put into production years before they are sold, and estimates need to be made about how many to produce – shutting a production line is a major decision that impacts a local economy hugely. Add to that the complication of switching the production line to accommodate left- & right-hand drive, manual and automatic. A guesstimate of how many of each kind may sell doesn’t cut it. Decent forecast required.

And then who are you marketing to? The youngsters who you want to become brand loyal when they can finally afford to buy/lease you? Or the late-middle-age relatively cash-rich driver who can afford you, but will only be driving for X many more years? Reaching those two different audiences cost-effectively probably means different messages and marketing channels to boot.

And which of your products do you market given that you don’t have an unlimited budget? One model has just had a ‘facelift’, so you need to tell people about that. But your shiny new launch needs advertising too. Maybe you choose to emulate Audi’s highly successful (IMHO) strategy of pushing the disturbingly throaty R8 – where you’re selling a dream for most people. If they buy into you based on that, they’ll find the A3.

Multiply that budget-setting conundrum by 15 markets where, although you sell the same makes, you have a different competitive set, different economic conditions and your customers have different preferences and you’ve got yourself a lovely, chewy challenge for a consultant to work on! Understand the problem first, then think about the data you may use to help you. Attempt to start with the data without an appreciation of the business challenge and you’ll come unstuck.

The data from the automotive industry presents another challenge. The purchase decision for buying a car is relatively long compared to many products and services. It’s not unusual for potential buyers who are in market to need to see a TV ad for a car 6+ times before they take any action (if they are going to). The longer the gap between marketing and a buying signal is, the more difficult it is to directly attribute exposure to advertising to a sale. Back in the day potential buyers would have probably visited a dealer and maybe taken a test drive. Both provide data capture opportunities.

A lot of dealerships are franchises and so the brand that they sell is not in full control of that operation. Data on footfall to dealerships for example, if available at all, may not be collated in a standard system. Sometimes it may be recorded manually. If you were lucky it was recorded by a sensor on the door – but you need to review it carefully. Since the smoking ban, dealership staff may pop in and out of the front door multiple times for a sneaky drag and end up being recorded as a customer.

More recently, of course, some of these data issues have been eradicated by being able to book actions like a test drive online. Configure-your-car apps are also a great leading indicator of interest. But lots of people play with these, so you probably only want to consider the completed ones as your evidence of the level of interest. Now, it’s possible to purchase a new car online for some brands, in some countries and for some types of customer, but it’s not yet the norm. It takes a long time for people to change the way they do things, especially if it’s a high-ticket item like a car.

Online purchases in some categories of goods and services have made data-linking an individual’s response to advertising with a purchase easy. But, don’t fall into the trap of thinking that what you’ve measured online is the whole story. For example, take a buyer who sees a sponsored post for a pair of trainers on Instagram. The buyer clicks on that ad and is taken to the brand’s website where they purchase. That ad may be credited with 100% of the reason that buyer bought. But what about the 6 similar posts they saw last week? Or the TV campaign last month? Or that their mate also has a pair? Those are likely to have played a part too but may get no credit.

Let’s assume in time most people buy their car online. If the ‘pair of trainers’ story above was enough to make you think that apparently flawless data capture doesn’t give you the whole story it’s much more the case for cars. We are talking months (if not years) of purchase consideration, so many more opportunities for non-online events to have their influence. Measurement possibilities improving doesn’t simplify the real-world problem.

So why bother if it’s so hard? There’s a quote attributed to Henry Ford (rather aptly):

“If you always do what you’ve always done, you’ll always get what you’ve always got.”

Yes, analytical consultancy in this industry is not a walk in the park, but once the problems are identified it’s straightforward to break it down into manageable chunks and create a roadmap. Most of the engagements I’ve worked on that fully transformed an internal business process like budget setting were 3-5-year roadmaps. The other crucial factor in the success of these kinds of projects, and another of the reasons why I enjoyed them so much, is the necessity to work so closely with the business to be able to design and implement a process that was workable for everyone.

It would be remiss of me not to also admit that the automotive industry has produced some of my most beloved TV advertising. I think that my top 3 would have to be:

  • Honda (“Cog”) – 2003, Wieden + Kennedy
  • Ford (“Dive) - 2012, Blue Hive
  • Renault (“Papa, Nicole”) – 1991-1998, Publicis

Wasted on me. I can’t drive. But I appreciate spectacular marketing when I see it.

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