As discussed in our earlier post on Retail Analytics, in this article we discuss how business analytics can help retail industry to segment and identify the most profitable customers.

As a part of any business unit that deals with a lot of customers, it is essential to identify are all the customers walking into my store equally valuable to my business. Imagine a hypothetical case, where you are an owner of a big garment store. Say you are comparing two customers, Ashok and Sachin. Ashok has been visiting your store regularly for past twelve months and on an average makes purchases worth rupees five thousand each month from your store. On the other hand, we have Sachin, who has never visited your store before, but as he’s getting married he made a purchase worth rupees hundred thousand from our store, but we are not sure if we’ll get to see him again? The question to be answered over here is, amongst Sachin and Ashok who is more valuable to our business?

Businesses today are faced with this question all the time. This becomes particularly more important for retail industry where we see thousands of customers walking into the store daily. Imagine, a situation wherein a retail firm needs to enter into some sort of customer centric marketing plan. It shouldn’t come as a surprise that marketing team doesn’t have unlimited budgets. So given the budget constraints, how does the marketing team proceed from here given that they have millions of customers to approach for?

The concept of business analytics is applied over here. Taking on from our initial discussion, what if as an analytics professional we suggest to the marketing team that they don’t necessarily have to approach each and every customers? What if say only 10% of the customers attribute to 80% of the profitability of the firm? Wouldn’t it make sense to focus only on these 10% customers rather than having a blanket strategy for all the customers of the firm? The million dollar question is, how do we identify these set of customers?

RFM metric, is one of the most widely used business analytics framework used to segment customers. Under this framework, customers are looked from the perspective of how recently did they visit our store, how frequently do they visit our store and finally how much do they spend into our store in each visit.

Looking from all the three angles ensures that customers who visit and spend onto our stores regularly are valued more than one off customers.

There is no specific rule on which factor should carry a higher weight, so analysts may try different weights for each factor or as popularly used give equal weights to Recency, Frequency and Monetary part.

Customers that rank high on all the parameters are considered to be the most valuable customers to the business while low ranking customers are the least valuable ones. Segments thus formed can be used can be used to implement different management strategies. For example, Best Buy, a retailer identified 7% of its customers attribute for 43% of the firms sales. Subsequently, the store realigned itself to meet the needs of these high-value customers. Arrow, an apparel manufacturer uses the segments to offer more perceived value to its high-value customers, in terms of opening an off-season sale to its high value customers couple of days before the general public.

RFM analysis though being so popular has its own shortcomings. Firstly, in RFM analysis customers are analyzed only on three factors whereas in reality there could be more factors that drive customer behaviour. Secondly, in RFM we form segments from our existing pool of customers, if used quite often, we may end up forming segments within segmented data, thus classifying some of our valuable customers as non-valuable.

As a part of the business analytics training course conducted by Edzyme Academy, RFM metric is covered in detail. Interested candidates may enrol into the course by visiting the link below:

http://edzyme.com/analytics-training/certified-professional-in-business-analytics-cpba-

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