CASE
Designing what sells and what delights: careful research for a new business launch
Overview
Client: A car maker (Japan)
Period (total): September 2024 to May 2025 (about 8 months)
Scope: Hands-on support refining new business ideas / research design, survey system build and execution
Industry: Manufacturing (automotive)
| Phase | Period |
|---|---|
| Discussing new business ideas | September–December 2024 |
| Building the survey system / final adjustments | January–March 2025 |
| Running the survey | March–May 2025 |
The challenge
To move an internal new project forward, three problems were running in parallel.
First, the direction of the new business idea was not settled, and the team needed an outside perspective to bounce ideas off and refine them.
Second, they wanted to do research in advance so the idea would not stay on paper, but they lacked the data or grounds to justify it.
Third, there were questions within the company about the familiar framework of simply commissioning a research firm, and they were looking for a new approach.
The company had spent huge budgets commissioning major research firms, and voices inside were questioning the entire flow from request to delivery.
Our proposal and approach
As we listened to the client’s situation, we raised several questions and ideas.
“Could having your own independent research function make a big budget reduction possible?”
“Showing the maker’s name and logo is authoritative and memorable — but doesn’t that very authority bias the answers?”
“Are the survey panels that research firms hold really made up of people who drive every day and own cars?”
Based on these questions, we designed our support around three pillars.
① Hands-on support refining new business ideas (about 3 months)
We continuously bounced ideas around with the internal project team from an outside perspective, giving shape to the business around “who it is for” and “what will resonate.”
② Research design: defining the target and the questionnaire
We narrowed the research to “people who use or own a car in their daily lives” and put the target definition into words. By adopting an “anonymous design” that hides the company name and logo, we removed brand preconceptions and created a neutral environment for answers.
Questions were designed to capture both “behavioral data” and “emotions and latent needs.”
Detailed branching shows each respondent the most relevant questions for their situation, increasing the accuracy and depth of the data.
③ Building and running an in-house research system
Rather than relying on outsourcing to major research firms, we built from scratch a survey system the client can keep using permanently.
We also ran the research ourselves, handling everything from building the system to collecting responses.
Results
The framework of the new business came together, and the picture of the expected end user became clear.
The survey gathered over 1,000 responses. Beyond reactions and opinions on the new ideas, we obtained quantitative data on everyday real-life problems and situations, such as “what I like and dislike about driving day to day” and “a service like this would make my life easier.” The client told us it was “very meaningful, interesting data.”
The qualitative results were also significant, breaking away from the previous dependence on research firms in three ways.
- Access to latent needs: detailed question branching made it possible to reach problems that potential customers had not put into words
- Removing bias: the anonymous design, hiding the company name, gathered “neutral, candid opinions” beyond existing customers and fans
- A permanent research foundation: the system keeps running semi-permanently as long as the website stays open, greatly reducing the cost and effort of future research
Our perspective
Research firms have overwhelming sample sizes. On numbers alone, that looks attractive. But we believe the foundation of research is judging “whether this is someone you can ask in depth about this product or service.”
Our principle this time was simple: “Don’t ask, or even reach out to, people who have nothing to do with cars.”
By sticking to this one point, we believe we captured 1,000 “deep” answers from the right people, rather than “thin” answers gathered from a list of millions.
In research, “quantity” and “quality” are not necessarily proportional. We believe it is the design of “whom to ask what, and how” that determines the value of the data.