The Model Customer

With Web site complexity increasing, direct marketing principles can suggest a more robust approach to clickstream analysis

By Thomas F. Richebacher

Continued from Page 1

Web Site Communication

1. Users create text strings either by entering data directly in a text box, or by selecting options from HTML forms.
2. The text string is transmitted via the GET or POST methods of the HTTP protocol to the Web server.
3. The Web server accepts the incoming text and processes it. In its simplest form, a page request, the server finds the requested file and returns it to the browser.
4. The browser displays the results.

However, communication is not always that simple. For example, Web sites that accept orders over the Internet have to accomplish a variety of jobs such as approve credit, check inventory, and collect order information. To accomplish these tasks, the server has to communicate with back-end resources; it needs a communication method and language.

Communication Method and Language

Essentially, a CGI transmits a program that parses incoming HTTP GET and POST requests, processes the content, if a back-end system like a databases is accessed, instructions are submitted; then it waits for a response, develops a response, and sends information back to the browser. (See Figure 2.)

In Web server logs, CGI requests replace the entries for predefined HTML pages. The log entry for a specific page such as GET Content/WhitePaper/widget.html is replaced by an entry for a CGI, GET /cgi-bin/widget.cgi.

The first log entry, a page request, clearly shows what the user asked for and received. You can go back to the HTML page and see what's in it. The second log entry, the CGI request, reveals neither. All you know is that the user had a dialog with your Web site, but you have no idea what the user requested, the application it was requested from, or what the user actually saw.

SUMMARIZING BY USER SEGMENT

FOUR WAYS TO CATALOG BY CUSTOMER STATUS For actual customers -- users who purchased something -- multiple records representing customer activities or visits are kept at the transaction level. Individuals that have bought nothing, but identified themselves by name and can be traced through cookies, are also maintained at the transaction level. Data from individuals that can be traced through cookies, but who have neither purchased anything nor identified themselves, is kept in single records summarizing number of visits, visit duration, and page categories viewed. All other users are treated as one group and summarized at predefined time intervals to understand overall behavioral and navigational patterns.

Making Visible the Invisible

The beauty of this process is that you are collecting data at the point of interaction. You can go back to Steps 1, 2, and 3 of your conceptual design and apply the criteria developed there to help you create the instruction to extract only the data you want. The disadvantage of this method is that by not keeping the raw data, you are prevented from verifying or restructuring the data at a later point, and the additional processing might slow down a Web site. The more data that you collect and manipulate, the greater the potential negative impact on Web site performance. Thus, decisions about Web site architecture and data development must go hand in hand.

Figure 3 illustrates how a very basic database configuration for sites that accept orders and collect user interaction might look:

• Database A accepts and returns inventory-related information.
• Database B identifies customers and based on historical information, returns customized content.
• Database C acts primarily as a container. It accepts clickstream and content summarization from nonidentifiable customers.

From a technical and analytic perspective, having separate databases perform different types of work makes sense. Each database can be tuned and optimized for its specific purpose. But you have to be careful when you design such a separation of duties that the data distribution over many databases still makes sense. It is important to maintain a coherent segmentation.

The Next Step

When data integrity is established, the next step is investing in analysis. Usually the first two things companies want to know are how they can convert prospects into customers and how to create customer loyalty. Both objectives require analysis whose outcome enables a firm to develop gradual customer qualifications resulting in specific actions. In a traditional direct marketing environment, these actions are primarily related to contact strategies; in a Web site context, they also include personalized content presentation.

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This is where you start reaping the benefit of Web site data definition efforts. Developing personalized Web content or personalized messages requires the integration of financial, marketing, operational, and statistical information. It means knowing what customers have seen and done, not just during the current visit but in a historical context, online and offline. Personalization is based on segmentation, pattern recognition, and the detection of behavioral shifts. Without a complete customer view, you are left guessing instead of personalizing -- a shaky approach at best.

See Clearly

Thomas Richebacher (thomas.richebacher@eds.com) is an analyst for the customer relationship management service line at Electronic Data Systems (EDS), a global services company. He specializes in the development of customer intelligence through data analysis.