Market segmentations offer novel way to review market characteristics, conditions and dynamics. It is often described as "inhomogeneous" approach. Using market segmentation makes easier to find new ways to increase customer satisfaction and loyalty, or improve customer service models.
Behavioural segmentation is sophisticated way of compressing information about customers behaviour, which makes it valuable strategic decision support tool. Essentially, it is a list of relevant types of customer behaviour, manifested in some period.
Behavioural segmentation stands out when compared to other market segmentations in the following ways:
- It is the only one built by consistent observation of the whole range of aspects that define customer's interaction with the company;
- It successfully replaces a whole range of other partial decision support solutions such as "Propensity to Buy" (PtB) models, Customer Attrition models or Activation models, thus significantly reducing the costs of development, implementation and maintenance of these models;
- Behavioural segmentation, unlike LTV * segmentation, doesn't rely on any assumptions regarding future "Business to Consumer" (B2C) interaction, so it is far more robust and reliable;
Behavioural segmentation groups customers into segments according to their similarities in interaction with the company. In example taken from retail banking interaction could be observed through following aspects:
- Client's level, structure or change of: debt, savings, consumption, income, activity,...;
- Risk assessment;
- Demography (age, education, profession...);
Interaction is measured within fixed historical observation period, whose length is determined by the nature of company business as well as by predetermined role of segmentation usage. Segments are usually named by some dominant behavioural feature which is subsequently connected ("coloured") with other features in order to obtain full picture, so in previous example from retail banking segments could be high consumers, Highly Indebted Customers, High LOC Utilization Customers, debt expiration customers, etc.
After grouping customers into segments (according to their similarities in interaction with the company), assumption of similarity in customer segment behaviour in recent past and customer behaviour in the future can be applied, i.e. there is expectation that certain group (segment) will show similar behaviour dynamic as it has shown in the recent past.
Behavioural segmentation development is a very complex process, but final product is very simple to use. When it comes to data requirements, it is not necessary that the data goes far into the past, but information stored in database should allow derivation of wide range of aspects of customer behaviour. The basic data mining technique relied on is cluster analysis, supported by PCA analysis and correlation analysis.
Technical definition of segments, in terms of defining aspects is contained in a very short and simple algorithm. So, unlike the development process, technical implementation of behavioural segmentation is an easy task. The entire database can be segmented any time by applying simple segmentation rules.
The development of behavioural segmentation is more time-consuming when compared to development of individual (partial) solution (e.g. various PtB models, attrition models...). However, a well-developed behavioural segmentation can not only effectively replace all of those tools together, but also it gives a completely new and improved picture of portfolio of clients.
Beside behaviour properties, behavioural segmentation also allows other non-behavioural properties to be assigned to recognized patterns of behaviour. This is contrary to the principle of intuition based "simple segmentation". Furthermore, in intuitive approach, correlations and multi-collinearity of attributes describing clients are not tested and properly treated, leading to a false image of the client portfolio.
Through comprehensive and unbiased "data driven" approach, behavioural segmentation will discover hidden interrelations between client groups.
* LTV segmentation is based on an assessment of the total profit that the customer will generate during relationship with the company. Therefore, it is necessary to assess: (a) attrition time (even a death...), (b) development through the time of the customer basket of products and/or services, (c) development through the time of the profitability of the client's basket of products and/or services. In other words, LTV is based on prediction of the cash flow generated by each client, not in a selected fixed future period, but to (again predicted) end of the relationship between the customer and the firm. Customers are then grouped into segments according to the forecasted profitability. However, it is obvious that such a large number of complex forecasts of various aspects concerning customer interaction with the company can't work, especially not together.