My wife and I were shopping the other day and she asked,“ Why are these people always asking us if we want to get a credit card? They have to know we have a couple already, don’t they? ” While I can’t
pretend to know all the reasons why retailers train their customer - facing folks to push store credit cards, I do know that there are some informational advantages. Stores can use credit card data to determine spending pattern behavior. This in turn reveals the different charging patterns of different segments of customers, information that can be mapped to customer profitability. With the buying behavior captured, retailers can target their most profitable customers with a personalized campaign of couponing designed to stimulate behavior. Lot of retailers in India offer loyalty programs and cards. This program will tell you about purchases that were made at your store but Co-branded credit cards may also tell you about purchases that were made at your competition's stores.
All retailers have as one of their long - term goals the desire to keep profitable customers loyal to their store. Is there — via the wonders of analytics — a way for a retailer to know when a customer is on the verge of leaving your store and to do something to circumvent that decision? Analytically here you are asking two questions: How do I know if a customer is going to leave my store? What can I do about it?
Most people would develop an ad hoc report that says, “ Tell me all the people that provide $ X amount and if that sales volume has dropped by $ Y amount. So, if they used to buy $ 100 a week and now spend only $ 50 a week, maybe they ’ re leaving my store.” That was the traditional model. The challenge with that is that by the time that situation has occurred, that customer has likely already made the decision that they are leaving their store. What you want to do is to be able to predict that a customer is going to stop shopping at your store before it is noticeable in a loss of sales. So we did that in one of proof of concept at one the retailer.
We threw a whole bunch of data at the computer for a set of consumers who did lapse the store and for a whole bunch of people who haven’t. In this example, we did find that there was one key item that was a telltale product that if a customer used to buy this item, and then stopped buying this item, that there was a high percentage likelihood that this customer would stop shopping at that store. Do you have any idea what the product was? Believe it or not, it was salt.
Business Analytics helps you understand where the customer’s head at the time of contact/purchase. Understanding this would enable much more appropriate messaging and might enhance service recovery. Such situational awareness would allow airlines, when you check in at the kiosk, to say “ Oh, sorry about your delay yesterday, here’s a free Vada Pav or Idli or Coffee coupon. ”