And there are some customers who resent any attempts

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Unformatted text preview: try to suggest a second item, the customer may get irritated and hang up without ordering anything. And there are some customers who resent any attempts at all to cross-sell them on additional products. Before trying data mining, G&R had been reluctant to cross-sell at all. Without the model, the odds of making the right recommendation were one in three. And because making any recommendation is for some customers unacceptable, they wanted to be exceptionally sure that they never made a recommendation when they should not. In a trial campaign, they had less than a 1% sales rate and had a substantial number of complaints. They were reluctant to continue for such a small gain. The situation changed dramatically with the use of data mining. Now the data mining model operates on the data. Using the customer information in the database and the new order, it tells the customer service representative what to recommend. They successfully sold 2% of the customers an additional product with virtually no complaints. Developing this capability involved a process similar to solving the credit card customer acquisition problem. As with that situation, two models were needed. The first model predicted whether someone would be offended by recommendations. G&R found out how their customers would react by conducting a very short telephone survey. To be conservative, they counted anyone who declined to participate in the survey as someone who would find recommendations intrusive. Later on, to verify this assumption, they made recommendations to a small but statistically significant subset of those who had refused to answer the survey questions. To their surprise, they found that the assumption was not warranted. This enabled them to make more recommendations and further increase profits. The second model predicted which offer would be most acceptable. In summary, data mining helped G&R better understand their customers’ needs. When the data mining models were incorporated in a typical cross-selling CRM campaign, the models helped G&R increase its profitability 2%. Increasing the value of your existing customers: personalization via data mining Big Sam’s Clothing (motto: “Rugged outdoor gear for city dwellers”) has set up a website to supplement their catalog. Whenever you go to their site they greet you with “Howdy Pardner!” but once you have ordered or registered with them they greet you by name. If you have a record of ordering from them, they will also tell you about any new products that might be of particular interest to you. When you look at a particular product, such as a waterproof down parka, they will suggest other things that might supplement such a purchase. 4 When they first put up the site, there was none of this personalization. It was just an on-line version of their catalog – nicely and efficiently done but not taking advantage of the sales opportunities presented by the Web. Data mining greatly increased the sales at their website. Catalogs frequently group products by type to simplify the user’s task of selecting prod...
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This note was uploaded on 11/25/2010 for the course CENG ceng taught by Professor Ceng during the Spring '10 term at Universidad Europea de Madrid.

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