Driving Innovation: Unveiling Customer Acceptance Index™

Intended Future’s Customer Acceptance Index™ (CAI) is a strategic planning tool for automotive Design VPs that helps brands increase word of mouth and product sales in key markets while reducing design and marketing risk through a scientific approach to vehicle concept development and validation.

Imagine you had an idea similar to a rear camera solution of Polestar 4. How can you convince others that this will be the next success? Can you hope for worldwide success or in one country? What if one country celebrating this design solution is China?

Can your design success be intentional?

Maybe there are more "simple" questions, but still, you need to make a decision.

Which design looks as most premium in the USA?

How about door handles design? Do we need them?

Probably yes, for some conservative rich customers, if we want to sell our design. But how about their shape? Positive or negative? What did Universal Design principles tell us? Oh, Principle #3 says it must be Simple and Intuitive Use. Will this design be intuitive for people in Japan?

Intended Future can answer all these questions with statistical significance in a crazy time of two weeks (from the idea to the data-informed design report). So you can manage to make slides until the next design review.

How? What's the Revolution?

The spectrum of concept design is vast and expansive, so let's take a step back and start with the basics. We live in times where almost everything is already invented and exists. The problem is that it is present in different domains, quite often in pieces.

The future is already here but it's not evenly distributed

Consider traditional car designers; while they can craft a sleek vehicle design, the intricacies of a multinomial logit, a statistical model used for predicting outcomes, may elude them.

A table book for every car designer. H-Point: The Fundamentals of Car Design & Packaging

On the other hand, a statistician, who can work with numbers, may be left baffled by the notion of a dash-to-axle ratio, a crucial element defining design language. But should they all grasp these diverse concepts? While no one needs to be a jack of all trades, a holistic understanding of the bigger picture can undoubtedly pave the way for better, more informed decisions.

Our new CAI tool blends these worlds, changing the way we dream up and create vehicle designs.

Today, designers rely on studies and data from marketing departments. But the creation and collection of this data is often a black box. More so, this data is frequently detached from the tangible context of car design. Classic marketing teams assess specific customer attitudes, conduct surveys, and condense results, but the quality and relevance can be questionable. For instance, designers often work off the opinions of survey-takers who may have little to no driving experience.

While qualitative research methods like focus groups and panels are often well-executed, they can be statistically insignificant and inadvertently add bias rather than clarity. These research methods tend to underperform as standalone strategies, offering data but lacking the deep insight needed for genuine innovation.

Customer Acceptance Index™ combines several unorthodox solutions to get the best of both worlds - qualitative and quantitative studies.

Let me explain how. 

In the vibrant world of marketing research, understanding consumer preferences is key. Old-school methods like the Likert scale, where people rate options on a scale of 1 to 5, don't always reflect real-world decision-making: it doesn't always reflect how people make choices in real life, where not all options are available at the same time, or where choices often have to be made between less-than-ideal options.

This is where the Best-Worst Scaling (BWS) methodology steps into the spotlight. Best-Worst Scaling (BWS), or MaxDiff (Maximum Difference Scaling) as it is also known, is a statistical method crafted by Jordan Louviere in 1991. It flips the script on traditional rating systems, instead presenting participants with a selection of options and asking them to identify both their favorite and least favorite. For instance, when assessing ice cream flavors such as chocolate, strawberry, and mint, participants would indicate which they prefer and which they like the least.

The beauty of BWS lies in its ability to generate more in-depth data

The beauty of BWS lies in its ability to generate more in-depth, nuanced data. Rather than a simple binary like/dislike decision, participants engage in a decision-making process that mirrors real-life shopping situations. This leads to a detailed understanding of consumer preferences and supports the creation of more precise predictive models.

Data can be beautiful and accurate.

Furthermore, BWS sidesteps the common pitfall of scale-use bias, a notable issue with Likert scale ratings where individuals may interpret the same rating differently. BWS focuses on relative preferences, avoiding this potential confusion. BWS pushes participants to make clear-cut choices, delineating the good from the great and offering a sharper understanding of consumer preference hierarchies.

However, to fully harness the power of BWS, it needs context. It can be linked to sensory perception, a Perceived Quality Framework, or as in the case of CAI, to design features.

At this point Intended Future is able to predict a trend

In this process, the designer steps into the role of a co-creator. At Intended Future, we require a question or hypothesis from a designer to verify. Together, we brainstorm on the best ways to present design features to potential customers, with an aim to minimize any potential bias.

After defining the targets and visuals, we initiate the study. With a carefully selected pool of respondents, triple-checked quality controls, real-time feedback, and time monitoring, the stage is set for an engaging task that, at last, brings the entire process to innovation!

Once the data is gathered, it's time for our team of data scientists to step in and begin their intricate work. Their role is akin to detectives in a complex mystery, connecting the disparate dots scattered across the information landscape.

They use sophisticated analytical tools and their deep understanding of statistical models to examine the data from various angles. They look for patterns, correlations, and trends, meticulously sifting through the multitude of data points collected during the study.

Their analysis is not confined to the obvious; they delve deep into the granular details, striving to uncover the data's subtle nuances and hidden insights. They also cross-reference findings, ensuring the derived insights are robust and dependable.

The ultimate aim is to transform the raw data into meaningful insights that can guide decision-making.

We distill the complex data into actionable intelligence that can help the designers understand customer preferences better, identify potential areas of improvement, and ultimately, shape the design in a way that resonates with the end-users.

At last, I can say: "Stay curious, stay informed, and traverse the exciting road of revolutionizing car design with us!"