Do you segment your customers mainly based on their age, gender, location or education without actually involving your customers? Well, you might get more accurate results by tossing a dice and assigning your customers into random segments. Let’s see why.
What are the challenges of traditional customer segmentation?
If your customer segments are based on demographic and socio-economic factors then you might get lucky and happen to find correlations that overlap with your actual customer segments. However, you are gambling on getting, at best, non-optimal results. You assume that people who look similar from outside behave in similar ways. However, we have a lot of contrasting evidence from many projects where these people think differently. For example, educated older women in Madrid have different values, life stories, childhoods and interests. Your business decisions based on potentially non-existing customer segments can have a negative return on investment and effort. You have to look people from inside to identify useful customer segments. This is where Hellon’s Human-Centric Segmentation steps in.
Hellon’s Human-Centric Segmentation consists of the following three phases:
Phase 1: Qualitative research
Everything starts with qualitative research, which is the foundation of successful Human-Centric Segmentation. The qualitative research identifies segmentation factors, which are not purely invented by the organisation itself. Therefore, the factors are based on the customers’ actual motives, values and thoughts.
Phase 2. Data acquisition
Segmentation data is obtained by letting customers rank the segmentation factors (e.g. values and motives) relative to each other. The resulting data is of high quality because customers have to prioritise their values by making compromises. Otherwise you might end up with useless data where customers assess everything to be equally important.
Phase 3. Mathematical and qualitative data-analysis
At Hellon, we have developed a specific mathematical algorithm to segment the factor data. The algorithm outputs the outlines of Human-Centric Segments, which are defined by customer values and behaviour. Finally, service designers take these outlines and colour and flesh them with qualitative insights into fully featured Human-Centric Segments.
The key takeaways:
- It is not possible to reliably identify the values, motivations and behaviour of customers using demographic data. You have to look at customers from inside to understand how they think. In our experience so far, the demographic data within our Human-Centric Segments sometimes resemble random noise.
- Start with qualitative research to identify the factors for segmentation.
- Customers have to prioritize their values by making compromises to obtain high-quality segmentation data.
Are you interested in developing your customer segmentation to find your competitive position in the market?
Do you want to figure out what type of value and offering speaks to your different customer segments? We would love to help you on this journey. Hellon has experience of carrying out Human-Centric Segmentation in various business domains in multiple projects. Just contact us or leave a reply, and we’ll get back to you.