| Description |
An in-depth knowledge of customers and prospects is essential to stay competitive in today's marketplace. Therefore, data is now often collected at a detailed level for a large number of customers. Segmentation is the act of breaking down this large customer population into segments, in which the customers within the segment are similar to each other, and those being in different segments are dissimilar.
A "true" segmentation will be collectively exhaustive and mutually exclusive, i.e. every customer in the database must fall into exactly one segment.
Segmentations are valuable, since they allow the end user to look at the entire database from a bird's eye perspective. A common use of segmentation analysis is to segment customers by profitability and market potential, also known as "Share of Wallet" analysis.
There are several reasons to use segmentations in business. One can use it for distribution channel assignment in sales, i.e. which segment will be addressed by which channel. Or segments may be identified and used as a framework to communicate for strategic purposes, that there are differences between customers, that could be captured at a very high level. Ideally, one would like to know each customer individually. Segmenting the customer base up to the level of individual customers, lays the basis for one-to-one marketing. One-to-one marketing is the ideal marketing strategy, in which every marketing campaign or product is optimally targeted for each individual customer. For other concepts, like profitability, it does not make sense, to define in the worst case 100,000 segments or levels of profitability. Instead one would restrict oneself to e.g. four or five and assign the customers to these clusters.
There are two possible ways how segmentations can be performed. Either humans do the segmentation "by hand", a process also known as "profile analysis", or the segmentation is created data-driven by analysis of the customer data. Data-driven segmentations are performed most often by using a variety of DM and statistical techniques, which fall into two categories: predictive segmentations and clustering. Predictive segmentations create segments with some goal in mind, e.g. segments of high and low value customers, according to their buying habits of a product or service.
In contrast, clustering has no particular goal in mind but is merely a way to give more structure to the data. Clustering techniques pull interesting commonalities from the data, which help to organize the data. These segments do not present customers who e.g. bought the product or turned in the rebate, but generally have similar characteristics. For address lists it is typical, that there are some predefined clusters e.g. households with high income and no kids. The people that fall into one particular cluster do not necessarily buy products at a higher or lower rate than people in different clusters. Nevertheless, the clusters' definitions are helpful because they provide high-level data organization in a consistent way.
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