Customer segmentations are a hard, but necessary evil. The identified customer profiles help get cross-functional teams on the same page and are invaluable tools to create meaningful products.
Running a customer segmentation often begins with accessing point-of-sale (POS) and behavioral data that needs to be followed up with a large survey to understand attitudes and preferences in a more structured manner. This requires time, resources, and analytical expertise. Once you've finished these activities, you humanize the results by creating a persona, including a name and description that reflect your findings.
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 you focused on? Do they care about sugar content, fat, protein, just the top-line calorie number, or some combination of each? It’s really hard, and just plain expensive, to get to that level of detail using survey data alone.
We’ve spoken to companies that have run 2,000+ respondent surveys to define ~4-5 broad segments. At $20 per respondent, that’s a $40K survey and 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 CreationSpace, 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 we mean anybody, can access and use the tool to learn more about their consumers' 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, groups that care about sustainable packaging, and groups that care about using natural ingredients, to name a few. Our tool allows anyone to directly interact with "synthetic consumers" or characters that are built with these underlying traits and attitudes.
Ultimately, these insights can be used to make decisions in a rapid, iterative, and affordable manner across your teams, whether that is during product development, while creating marketing material, or finding solutions to grow your brand.
If you're interested in learning more, get in touch with us: info@creationspace.ai