Промышленный лизинг
Методички
Commercial Data Mining Software Products Have Become Available There is always a lag between the time when new algorithms first appear in academic journals and excite discussion at conferences and the time when commercial software incorporating those algorithms becomes available. There is another lag between the initial availability of the first products and the time that they achieve wide acceptance. For data mining, the period of widespread availability and acceptance has arrived. Many of the techniques discussed in this book started out in the fields of statistics, artificial intelligence, or machine learning. After a few years in universities and government labs, a new technique starts to be used by a few early adopters in the commercial sector. At this point in the evolution of a new technique, the software is typically available in source code to the intrepid user willing to retrieve it via FTP, compile it, and figure out how to use it by reading the authors Ph.D. thesis. Only after a few pioneers become successful with a new technique, does it start to appear in real products that come with users manuals and help lines. Nowadays, new techniques are being developed; however, much work is also devoted to extending and improving existing techniques. All the techniques discussed in this book are available in commercial software products, although there is no single product that incorporates all of them. How Data Mining Is Being Used Today This whirlwind tour of a few interesting applications of data mining is intended to demonstrate the wide applicability of the data mining techniques discussed in this book. These vignettes are intended to convey something of the excitement of the field and possibly suggest ways that data mining could be profitably employed in your own work. A Supermarket Becomes an Information Broker Thanks to point-of-sale scanners that record every item purchased and loyalty card programs that link those purchases to individual customers, supermarkets are in a position to notice a lot about their customers these days. Safeway was one of the first U.S. supermarket chains to take advantage of this technology to turn itself into an information broker. Safeway purchases address and demographic data directly from its customers by offering them discounts in return for using loyalty cards when they make purchases. In order to obtain the card, shoppers voluntarily divulge personal information of the sort that makes good input for actionable customer insight. From then on, each time the shopper presents the discount card, his or her transaction history is updated in a data warehouse somewhere. With every trip to the store, shoppers teach the retailer a little more about themselves. The supermarket itself is probably more interested in aggregate patterns (what items sell well together, what should be shelved together) than in the behavior of individual customers. The information gathered on individuals is of great interest to the manufacturers of the products that line the stores aisles. Of course, the store assures the customers that the information thus collected will be kept private and it is. Rather than selling Coca-Cola a list of frequent Pepsi buyers and vice versa, the chain sells access to customers who, based on their known buying habits and the data they have supplied, are likely prospects for a particular suppliers product. Safeway charges several cents per name to suppliers who want their coupon or special promotional offer to reach just the right people. Since the coupon redemption also becomes an entry in the shoppers transaction history file, the precise response rate of the targeted group is a matter of record. Furthermore, a particular customers response or lack thereof to the offer becomes input data for future predictive models. American Express and other charge card suppliers do much the same thing, selling advertising space in and on their billing envelopes. The price they can charge for space in the envelope is directly tied to their ability to correctly identify people likely to respond to the ad. That is where data mining comes in. A Recommendation-Based Business Virgin Wines sells wine directly to consumers in the United Kingdom through its Web site, www.virginwines.com. New customers are invited to complete a survey, the wine wizard, when they first visit the site. The wine wizard asks each customer to rate various styles of wines. The ratings are used to create a profile of the customers tastes. During the course of building the profile, the wine wizard makes some trial recommendations, and the customer has a chance to agree or disagree with them in order to refine the profile. When the wine wizard has been completed, the site knows enough about the customer to start making recommendations. Over time, the site keeps track of what each customer actually buys and uses this information to update his or her customer profile. Customers can update their profiles by redoing the wine wizard at any time. They can also browse through their own past purchases by clicking on the my cellar tab. Any wine a customer has ever purchased or rated on the site is in the cellar. Customers may rate or rerate their past purchases at any time, providing still more feedback to the recommendation system. With these recommendations, the web site can offer customers new wines that they should like, emulating the way that the stores like the Wine Cask have built loyal customer relationships. Cross-Selling USAA is an insurance company that markets to active duty and retired military personnel and their families. The company attributes information-based marketing, including data mining, with a doubling of the number of products held by the average customer. USAA keeps detailed records on its customers and uses data mining to predict where they are in their life cycles and what products they are likely to need. Another company that has used data mining to improve its cross-selling ability is Fidelity Investments. Fidelity maintains a data warehouse filled with information on all of its retail customers. This information is used to build data mining models that predict what other Fidelity products are likely to interest each customer. When an existing customer calls Fidelity, the phone representatives screen shows exactly where to lead the conversation. In addition to improving the companys ability to cross-sell, Fidelitys retail marketing data warehouse has allowed the financial services powerhouse to build models of what makes a loyal customer and thereby increase customer retention. Once upon a time, these models caused Fidelity to retain a marginally profitable bill-paying service that would otherwise have been cut. It turned out that people who used the service were far less likely than the average customer to take their business to a competitor. Cutting the service would have encouraged a profitable group of loyal customers to shop around. A central tenet of customer relationship management is that it is more profitable to focus on wallet share or customer share, the amount of business you can do with each customer, than on market share. From financial services to heavy manufacturing, innovative companies are using data mining to increase the value of each customer. Holding on to Good Customers Data mining is being used to promote customer retention in any industry where customers are free to change suppliers at little cost and competitors are eager to lure them away. Banks call it attrition. Wireless phone companies call it churn. By any name, it is a big problem. By gaining an understanding of who is likely to leave and why, a retention plan can be developed that addresses the right issues and targets the right customers. In a mature market, bringing in a new customer tends to cost more than holding on to an existing one. However, the incentive offered to retain a customer is often quite expensive. Data mining is the key to figuring out which 1 2 3 4 5 6 7 8 9 10 11 12 [ 13 ] 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |