Customer segmentations are a hard, but necessary evil. The descriptions can bring cross-functional teams on the same page and they are invaluable tools to create meaningful products. But, it takes a lot of POS and behavioral data to begin with and needs to be followed up with a large survey to understand attitudes. Then you might want to use an unstructured learning algorithm to identify themes. And finally, you try to humanize the results by creating a name and description.
Simplified persona for a healthy snack company
But ultimately, what you really want to get to are mindsets, attitudes, and what people actually care about to help with decision-making. For example, knowing that you are targeting a health-conscious consumer is great, but what kind of health-conscious consumer are we focused on? Do they care about sugar content, fat, protein, or just the top-line calorie number, or some combination of the above? It’s really hard, or plain expensive, to get to that level of detail using survey data alone.
We’ve spoken to clients that have done 2,000+ respondent surveys to define ~4-5 broad segments. At $20 per person, that’s a $40K survey and it’s only 400 – 500 respondents per segment. Sure, it’ll give you a directional sense of what a health-conscious consumer wants, but it’s not really useful for product development, marketing, brand management, or any other functions to use in an effective way.
$40K can easily go down the drain… all for 4-5 mildly useful personas.
At Seer, we realize that understanding nuances within a persona or consumer segment is critical. Your team probably has the broad segments in mind – you know, the $40K you just spent. We can take these high-level personas and identify sub-variations or themes within them by searching reams of qualitative data. Further, anybody, and I mean anybody, can access and use the tool to learn more about their consumer needs and preferences.
Take eco-conscious consumers of lotion products as an example. We ran our proprietary algorithm that combines Bain proprietary frameworks and data with advanced statistical and AI methods to identify more than 10 sub-variations of eco-conscious consumers of lotion products. This identified groups that care about a specific brand to sustainable packaging to using natural ingredients. Our tool allows anyone to directly interact with "synthetic personas" or characters that are built with these underlying traits and attitudes.
Example output of preferences and attitudes within the eco-conscious persona segment
Ultimately, these insights can be used to make decisions in a rapid, iterative, and affordable across your teams, whether that is during product development, while creating marketing material, or finding solutions to grow your brand.
Interested in learning more? Reach out to our team!