Recently, it seems that developing public segmentations of your customers or citizens and then sharing it for all to see is becoming fashionable.
In part, this is to be applauded and welcomed.,/p>
The trend highlights a key tool within the customer insight toolkit, encourages greater focus on understanding people and embraces the need for greater transparency. However, there is also an inherent risk, that readers fail to understand the purpose, design and limitations of such segmentations and thus unwittingly apply them where they will not help.
This reminds me of a time many years ago when psychometric segmentations were very popular in business circles. Myers Briggs (MBTI) and many other profiles were enthusiastically applied and team members categorized into their “type.” Sadly, all too often, this perception about some important differences between team members was filed away following the team-building exercise and never used again. Screening interview candidates via psychometric segments was also “flavor of the month” at one stage, although I hear it being much more rarely used now (or only as part of a mix of “facts” to be considered).
Perhaps part of the problem can be a misunderstanding of the role of segmentation. As posted previously, segmentation is just one of a number of statistical tools available, and each segmentation will be designed to achieve a particular purpose. For this reason, more than one segmentation of customers may be entirely appropriate and insightful for a business that is able to handle such complexity (though most business leaders dislike this idea).
But let’s return to reviewing some of those recently published public segmentations. The first one I want to consider is the Consumer Spotlight segmentation published by the FCA.
While this appears a useful segmentation to help the FCA understand and focus on more vulnerable segmentation with regard to financial understanding or access, it is also important to recognize its limitations. A 10-segment model will only ever be appropriate for understand macro attitudes and behaviors. My own experience of segmenting consumers within different product markets tells me that both attitudes and behaviors can vary widely once you drill down to specific needs or products. So, it’s important to realize that this segmentation has been designed to focus on dimensions like vulnerability, detriment and financial risk. Thus it is most relevant for the FCA itself, to help target communications.
A second example is a commercial business taking such a public approach to sharing a segmentation. It is the Centre for the Modern Family segmentation funded by Scottish Widows.
This is another interesting segmentation, as it seeks to highlight and track changing social attitudes, family structures and pressures on modern families of many different types. However, once again it is important to realize the limitations of this survey. It is an attitudinal segmentation, constructed from a combination of “qual and quant” survey results, interpreted by an expert panel drawn from academia, social care and commerce. As such, this is a subjective perspective evidenced by self-reported attitudes and behaviors. Although such an understanding can be very rich, the inability to overlay this segmentation onto customer databases means that actual behavior cannot be verified or targeted actions or communications executed (often a drawback of attitudinal segments).
My final example is from the UK government. There are two I could have chosen here, as they have also recently published a segmentation on “climate change and transport choices,” but I’ve chosen to highlight the segmentation exercise published in regard to the problem of digital exclusion.
Once again, it’s encouraging to see this segmentation exercise being undertaken and the transparency regarding approach and progress. However, it does also appear to run the risk of a number of other “hybrid segmentations.” That is the risk that certain differences highlighted in various research studies or other sources are “cherry picked” to construct a patchwork quilt of apparently rich understanding that is not evidenced on a consistent basis. This can be seen in the infographic embedded in the above article. Even constructing a behavioral/demographic framework for a segmentation on that basis and then consistently surveying each segment runs the risk of masking important differences because of the averaging effect of artificially constructed segments. It will be interesting to see how government advisers and agencies avoid those risks.
I hope you found that interesting and are also engaged with the level of focus on segmentation in today’s government and media. If these are approached carefully and interpreted appropriately, they should be another driver of greater influence and seniority for customer insight leaders. That is our cause celebre.