Time to Evolve
The automotive industry is well over 100 years old, and vehicles today are so much more sophisticated than a century ago – even a few years ago. While some of that change is driven by technology, some of it is also motivated by consumers’ tastes, awareness, and opinions. And those are changing faster than ever.
One of the best ways for automakers’ research to keep pace is to augment their research methods, at a speed comparable to market changes. Many brands still rely almost exclusively on research that involves a long list of fixed questions fielded at a regular cadence. That’s great for trending, as long as tomorrow looks enough like yesterday. However, if automakers are hoping to reveal (and leverage) evolutions that emerge rapidly “between” the trends, a new perspective and a different approach is needed.
A great example of a more modern approach is called iterative discovery research, or IDR for short. IDR uses a sequence of short surveys to create a journey to insights, with each set of business questions building on the responses to the prior ones. There may be an overall framework (or research “destination”), but the magic is letting answers steer the questions.
IDR makes research more agile and cost-effective. It allows a business – in this case, an automaker – to determine which areas they want to probe into more deeply based on the survey results. Although IDR can be used by any business, this article will focus on the benefits it brings to the automotive industry.
How does IDR work? Let’s say you want to learn more about the features that matter most to buyers when purchasing an SUV. You decide to use the IDR approach by sending a short set of high-level questions to a specific audience. Based on the results, you learn that this group cares significantly more about safety than fuel efficiency.
So, instead of doing deep research on multiple categories, you can specifically probe deeper on what is motivating their safety perceptions. From there, you can ask about the safety features that best address those perceptions. This also gives you the opportunity to scale back or exclude questions on fuel efficiency and other less influential attributes. A non-iterative approach could have missed that discovery and also lacked the flexibility to go deeper quickly, not to mention continuing to ask unneeded questions.
Let’s Get Real
Now let’s review a real-life application of IDR. Groupe PSA, based in France, is the second-largest automaker in Europe and sells vehicles in 160 countries – but not in the US for many years. It’s planning to return to the US using its Peugeot brand. So, it’s fair to say that assessing Peugeot’s current brand equity in the States is a smart approach. As you can see in the chart below, the results are fairly neutral (chart 1), which may suggest its marketing will start from a relatively clean slate. If there was greater familiarity, Peugeot could follow up with questions on opinion of the brand (following typical purchase funnel logic).
The results of the first survey prompted us to look at US public perception of French cars, in general (chart 2) – given that there are no other French-made vehicles sold in the United States today. This iterative approach revealed a potential risk: while brand results were generally neutral, perceptions of French vehicle quality were not. These perceptions may be a hurdle for a successful PSA launch in the States. Further iterative research could delve into the drivers of these perceptions and inform tactics to overcome them.
Why is Iterative Discovery Research Now Possible?
Historically, the biggest hurdles for IDR were cost and time. Fielding several short surveys could take months, with more time required as results for each set of questions were compiled and processed. And time is money. That means research needs to match the increasing speed and flexibility of consumer changes. The Lucid Marketplace was designed for speed, efficiency and flexibility for survey research (trackers, ad hoc studies, ad effectiveness, audience validation, etc.) and, as such, is perfectly suited to empower IDC. The “iterative” element, in Peugeot’s case, can also be used to copy test candidate marketing which will be designed to address the challenges, and, ultimately, inform a tailored tracker (or update current trackers).
Next Steps: Think Bite-Sized
Now that IDC is a feasible technology, the next step to broad-scale implementation is changing industry perceptions. The changes require thinking in bite-sized increments within a broader vision and the ability to quickly analyze results. And, most importantly, it requires a directional decision based off those results for the next set of questions.
The good news is that the iterative model also means shorter burst of insights that are easier to digest – and a sample marketplace expedites getting those answers to business questions without breaking the bank, and likely saving money and time.
Contact our team if you’re interested in learning more about how Lucid empowers iterative discovery research!