You might know a lot about your prototypical customer. For example, you might know that he’s male, that he belongs to the Baby Boomer generation, that he’s a family person, that he went to a private school, and that he owns property and that he is comparatively wealthy. You might then be tempted to conclude: people who share these attributes have similar needs. But is this really true? Are personal attributes like age, gender, income, or educational level really the causing factors to determine differences and similarities in customer needs?
The persona description above applies to both Prince Charles and Ozzy Osborne alike. It is hard to imagine that their expectations and needs towards any kind of product or service are similar (with the one exception of their taste for music, of course 😉).
The example illustrates the principal flaw of an ‘a-priori’ or prescriptive segmentation approach. Most of the traditional segmentation methods like attitude segmentation, segmentation around solutions, regions, demographics, etc. pursue this approach.
An a-priori segmentation has marketers and researchers determine market segments by asking their customers “Which of these [demographics, psychographics, attitudes, …] describe you best?” Once the data is there, they start dividing their customers into segments that contain customers who are as similar to one another as possible (homogeneous) but who differ from customers in the other segments as much as possible (heterogeneous). Then they try to create offerings tailored to any of these segments.
This approach is simple, as data are readily available, making them easy to identify, collect, track and analyze – but that is its only advantage. The great danger of an a-priori segmentation is that it often results in what Anthony W. Ulwick calls ‘phantom segments’ (see his white paper Outcome-Based Segmentation). Such phantom segments are classifications that are artificially imposed on customers, but that do not exist in the market. According to Ulwick, this is one of the main reasons that makes innovation appear random and unpredictable.
Needs-based segmentation (in ODI-terms “outcome-based segmentation”) goes the opposite way: in a first step, customer needs are being identified with in-depth customer interviews alongside a conceptual framework called ‘job map’. In a typical Outcome-Driven Innovation® (ODI) project, around 150 customer needs or ‘outcomes’ are uncovered. These outcomes are then evaluated by a large and representative sample of customers who give their ratings for importance and satisfaction. The obtained data are the foundation for the ODI data model – a comprehensive database that forms the basis for subsequent innovation decisions.
Using advanced statistical methods such as factor analysis and cluster analysis, the importance and satisfaction data in the ODI data model are used to extract segments that are naturally embedded in the market. These so-called needs-based segments are homogeneous within themselves and heterogeneous to one another with regard to the only criteria that matters to innovation: the individual pattern of customer needs.
Let’s illustrate the approach with an example:
To sum up, what are the main advantages of needs-based segmentation?
1) Needs-based segmentation is a valid way of segmenting. Instead of creating phantom segments that do not exist naturally, needs-based segmentation draws a more realistic picture of how customer needs structure your market.
2) Needs-based segmentation automatically delivers strategic options: it clarifies how to create value in a market by showing where the unmet and overserved needs are. So, there’s not a lot of translation effort required.
3) It uses large-scale quantitative data, which enables evidence-based decision making. This takes out a lot of the guesswork other approaches are using at the front end of the innovation process.
4) Even in seemingly saturated markets, needs-based segmentation enables you to identify groups of customers with innovation opportunities for your company.
5) Since demographic, psychological, behavioral, contextual and other complexity factors are also collected in the data model, you know who your customers in the defined segments are. This information is also important, for instance, to identify the right channels to approach them.
All in all, creating market segments around specific customer needs helps you to develop and position offerings that hit the nerve. Once implemented, the ODI data model enables you to create more effective strategies for decades to come.
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