Beyond the Shopping Cart

A case study of using offline data to find your best online customers

By Jesus Mena

Continued from Page 2

Web Mining Digs Deeper

Prior to this analysis, this e-commerce site had limited its analytics to log analysis using IBM's Surf Aid service. However, log analyzers like these are confined to using the Extended Log Format files for generating their limited reports, which means that they only report on the number of visitors and how they came to the site - such as the "keyword" or search engine visitors used. Another field is reserved for cookie reporting, but in most instances the only value this field can tell you is whether a visitor is new or returning. On the other hand, this Web mining analysis went much further in defining who their online customers were. It found that their online customers tended to be renters, most were single, affluent, in their mid-30s, did not own an automobile, but commuted to work, and lived primarily on the East Coast.

Reaching the Right Customer

Because the majority of their online customers did not own cars, we did not recommend radio advertising, which this e-retailer had been doing in the marketing of its Web site. Instead, we recommended that it place ads in buses and subways because the majority of their online customers took public transportation. Billboards near subway entrances would also be good places to position ads for the Web site. For the placement of their online banner ads, we recommended that they place them in regional (East Coast) sites catering to males in their mid-30s, such as renter locator sites. We also recommended bilingual ads in Spanish.

This Web mining analysis quantified the suspicions this e-commerce site had about its online sales. Furthermore, using the same data we used for this segmentation analysis, we constructed a "propensity to purchase" model using a multilayer perceptron neural network. The purpose of the model was to identify online prospects based on their neighborhood demographics. The model used a ZIP Code as an input in order to predict whether a new visitor was likely to make an online purchase. The model had an overall accuracy rate of 84 percent on a training sample size of 3,501 records, correctly predicting the outcome of 2,945. To validate the model, we tested it on a held-out data sample of 19,319 records. Of 2,555 sale accounts, the model was able to correctly classify 1,832 or 72 percent of them. In other words, the model could correctly spot seven out of 10 sales prospects as they visited this online store.

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We could generate XML or Java code from this data mining model in order to identify new visitors with similar demographics by simply passing their ZIP Code via a form in order to make dynamic offers in real time while they were still at this e-commerce site. It is absolutely critical to make such quick offers to potential prospects, because on average only about one or two out of 100 visitors to a site are buyers - with most visits lasting an average of eight to 20 seconds. Furthermore, a recent Media Metrix and McKinsey & Co. study found that 55 percent of all visitors go to fewer than 100 sites out of the millions that are out there. The message to all e-commerce and content provider sites is clear: Be quick in identifying your potential prospects because you have a very small window of opportunity to close a sale and hold on to a visitor.

Jesus Mena (jmena@webminer.com) is the author of Data Mining Your Website (Digital Press, 1999) and is the CEO of WebMiner.