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# Deep Dive into Customer segmentation using Statistical / Analytical Techniques

By Sandeep Agarwal | January 7th 2012 | Views:

Today the business officials not only needs to design the strategies for retention of the customer base but also to increase the Customer base at a very fast pace. Earlier the number of players in a particular segment was very low which led to easy path for the players as most of them used to enjoy monopoly in the market. But as the level of competition is very high in the present scenario, it takes significantly lesser effort for the competitors to uproot the company. In order to sustain the business, the official continuously looks for expansion of the customer base. But to incur the expansion is not a cakewalk; it requires significant study as in which segment to target and many other issues. As companies need to incur significant expenditure in the expansion process, the officials are very much answerable to the CEO about the cost and benefit aspects of the same.

Here we would like to state few of the analytical techniques which are extensively used in the study of existing customer base to get significant insights on which segment of customer to target for maximum return. Mostly people uses analytic technique like cluster analysis in this scenario but here we would like to look at other analytical techniques such as Probit to get an idea about the existing customer base. The basic idea behind applying this statistical technique is to get an idea about the probability that a particular customer will purchase the product based on some known independent parameters or variables. This technique is more robust as compared to cluster analysis because it gives better insights about the customer and deep dive into the primary question as in how probable a particular type of customer to purchase a particular product.

Let us explain a bit about the analytical technique we are arguing about in the entire discussion. Firstly let us talk about the data which is there when we use this technique and why can’t we use the normal regression techniques in this scenario. Firstly the dependent variable in this case is either one or zero according to whether the customer purchased a product or not respectively. In this scenario you can’t use statistical technique such as linear regression as it violates an assumption that the dependent variable should be continuous in nature. So we need to divert ourselves into other estimation techniques such as maximum likelihood estimator. We would also need to take into account all the information about independent variables. After running the probit model you will get a value of probability depending upon the values of independent variables such as age, sex, income and many more. This will help you to segment the customers into high, moderate and low purchasing probability segments. Now depend on the threshold value of probability depending upon your personal discretion and business sense you can target a particular set of customers.

To run the probit model we can use any statistical software such as SAS and SPSS but SAS would be better as the size of data will be huge. This particular statistical technique is extensively used in the analytics industry to completely understand the behaviour of customers. It help to properly target your advertising as post this analysis you can do promotional activities like offers, discounts to attract that particular segment of customers.

Probit is different from cluster analysis as in cluster analysis you don’t have information about dependent variable and you directly work out on independent variables to segment the customers or anything else.

Meltdata Business Solutions is a result oriented, technology-driven full service Market Research and Marketing Solutions provider. Our specialized range of services comprise of extensive end-to-end data collection services, data processing, data analysis and reporting services.

Article Source:
http://www.articleside.com/marketing-articles/deep-dive-into-customer-segmentation-using-statistical-analytical-techniques.htm

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