Finding your new product’s ideal customer

Mindlab

Challenge

How do you pinpoint the perfect customer for your product?

Using our implicit association testing, we identified four clusters of people who would be interested in buying a fictitious new chewing gum brand. We worked out the cluster most likely to buy the product and what they would pay for a pack. We then painted a vivid picture of what makes each cluster tick and how to trigger a purchase.

Picture of potential customers

Research

500 nationally representative participants completed an online research survey about their thoughts on Nata chewing gum. The gum was presented as having a vibrant, minty taste with the same texture and feel as other brands with the additional attributes of being fully biodegradable, sustainably sourced, vegan, low carb, and sugar-free.

The study was structured to:

  • Introduce the chewing gum concept then ask explicit questions and test name recall.
  • Carry out post-concept implicit tests including preference, product association and price tests.
  • Ask segmentation questions about lifestyle and personality.
  • Offer a text hotspot to see which elements of the product presentation resonated.

Findings

We used a statistical technique known as a Gaussian mixture model to uncover clusters that existed in the data. While customer clustering is usually done on the basis of demographic data like age or gender, we took a unique approach by clustering on the basis of participant’s implicit associations with Nata.

By grouping participants according to how similar their associations are, we identified four clusters: Local Innovators, Educated Optimists, Determined Environmentalists and Hardworking Netizens.

Using demographic, personality data and lifestyle measures, we constructed prototypical members of each group, revealing their: age; employment status; preferred information sources; likelihood of remembering the name; and how much they would likely pay for the product. Educated Optimists, for instance, were prepared to pay £1.29 where Hardworking Netizens would only part with 89p.

Cluster analysis images

Four clusters, shown here, were identified by grouping similar patterns of responses to the competitor and product association test. We then developed pen portraits of each market segment.

Cluster 1: Local innovators Annie is open to trying new products like Nata. She is in her late twenties, and lives in a working-class suburban neighbourhood with her partner, Tim. She buys chewing gum at her local newsagent and decided to try Nata when she saw the product display there. She is often too busy to think much about the environment. It was more the novelty of Nata that attracted her than its environmental creds.

Cluster 2: Educated optimists Ben likes most brands of chewing gum, but doesn’t see Nata as particularly different. He is in his sixties and has a positive outlook on life. He is well educated and lives in the country. He is divorced but has a wide circle of friends and dotes on his grandchildren; he might give Nata a try if his friends or grandchildren recommend it.

Cluster 3: Determined environmentalists Chloe doesn’t like most brands of chewing gum, and doesn’t see much to distinguish Nata. She is not very likely to give Nata a try, even though she does care a lot about the environment and is a vegetarian. This may be because she suspects the claims made for Nata are greenwashing. She is affluent, in her late forties and lives in a leafy suburb with her husband Nick and her three teenage children. She is quite fussy and nothing quite lives up to her high standards.

Cluster 4: Hardworking Netizens David is not generally a fan of chewing gum but he thinks Nata is different and is willing to give it a try. He is in his early twenties and shares a flat in the city centre with a couple of friends. He works from home a lot, and loves technology. He is an avid user of social media and is likely to try Nata if it has good online reviews.

"This study provides an example of how Mindlab’s clustering analysis can help to add extra detail about customers beyond sample-wide, aggregate demographic data. We can identify who has the most positive and negative associations with a given product, and this can serve as the basis for detailed personas and segmentation."

Further reading

Subscribe to the Academy

Sign up for emails from the Mindlab Academy.