Responding to the different needs of customers begins with identifying different behavioral groups--but all database segmentation means to most companies is more targeted selling.
When asked about her company's approach to database segmentation, a Market Research Manager was emphatic in her response:
I'm not a fan of behavior-based segmentation. I don't want to say it's a waste of time, but I think people waste a lot of time on it. I think it really makes for good bed-time reading, but there's not a whole lot you can do to action it. You can't just put a label on someone, append it to their database record, and assume they'll always behave a certain way.
Considering her company had a data-rich transaction database of over one million customers, it was a perplexing comment. And she was not alone in her opinion--even the various product managers at the company viewed the database as simply a convenient list of names to target. According to them, the database was made up of prospective buyers, not customers with a need for multiple products. No one even could even guess the average cross-sell ratio – because no one had ever cared to know.
Segmentation is supposed to be the cornerstone of CRM. By grouping customers into discrete sub-groups based on how different (or similar) they are in their characteristics and behavior--or so the theory goes--new products and offers can be dreamed up that will appeal to their distinct needs. In practice, most companies simply partition their customer bases into target groups for direct marketing purposes. A segment, in that narrow context, is merely a group of customers with a strong likelihood to purchase.
Even when a company does try a more strategic approach, statistically segmenting customers into unique behavioral groups, it does not automatically lead to a switch in thinking. Take one major department store retailer which some years ago hired an external agency at great expense to conduct a cluster analysis of its multimillion-name database. The store made some intriguing discoveries--such as realizing that one-fifth of its customer file habitually returned products, while an avaricious 2% bought anything and everything--but the results were greeted by management with a shrug of the shoulders. More interested in what was being sold than who was doing the buying, the retailer had no clue how to apply the insights it had gained--or much interest in doing so. The cluster findings soon faded into memory while the company carried on with its mass promotional mailings.
A similar case involves a financial services provider that recently completed a cluster analysis of its customer file. The results were not particularly startling: 10% of the database consisted of older long-tenure customers who preferred to make their deposits and withdrawals in-branch, while 35% were single-account holders who generated hardly any profit at all. But they did provide some clue as to the unrealized potential that existed within certain segments.
Nevertheless, the institution is now bogged down figuring out what to do next, uncertain how to shape its marketing plans and programs around its key segments. For now, the segment profiles remain simply “good bed-time reading.”
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