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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.
- Spring '10