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Fastwater Rapids vol. 1.7, 12Oct98
by Adina Levin
In order to maximize performance of a web business, it will be important to close the loop between site content, user behavior, and business goals. Closed loop operation will become increasingly critical for making your web business profitable and increasing competitiveness and profitability over time.
Closing the loop is a process that includes measuring and analyzing user activity; designing campaigns, content, and interaction based on what you know about customers; and modifying campaigns, content, and interactions to optimize business performance. Today, in order to build a closed loop system, you need to integrate many different pieces: web measurement technology, customer data, content management, targeting, personalization, and analysis tools. There are many gaps between the pieces -- it takes a lot of work to tie them together. Even the most advanced web businesses are only scratching the surface of what can be done.
At Fall Internet World, there was a flurry of announcements about partnerships and acquisitions among vendors of various peices of customer interaction technology -- and there was even more partnership activity going on behind the scenes. The vendors of web interaction technology are weaving a complex pattern of partnerships that knit together some of the separate pieces of closed-loop customer interaction systems.
net.Genesis, a company that makes technology to measure and analyze web usage data, has partnered with Engage, a provider of web user demographic data. Engage, based in Boston under the CMGI umbrella, has built a database of profiles of 30 million web users. These profiles contain information about user preferences in 800 categories including entertainment, books, and computers and are derived by analyzing the clickstream of websites including Lycos/Tripod and Geocities. The data is anonymous -- the system can identify a user based on a cookie and relate that data to the user's preferences -- but it does not contain identity information such as name, address, and phone number.
The two companies plan to integrate net.Genesis's net.Analysis software with Engage's user profiling technology, allowing net.Genesis users to compare the data from their own sites to usage patterns across the web.
Meanwhile, Engage has also partnered with RelevantKnowledge. RelevantKnowledge provides demographic data about web usage, based on a randomly selected panel of business, home and college Web users. RelevantKnowledge will use Engage's Precision Profiling technology to extend its current panel data, giving Web marketers comprehensive behavioral knowledge of their visitor base.
The consequence of these deals is that, once these vendors integrate their technologies, you will be able to integrate and compare data about users on your own site with data about users on other sites. This information will help you make better choices about advertising, marketing, sales, and other business goals of your website.
There are two paradigms for driving customer interactions with data. The first is traditional business analysis, refined over the last century. Business analysts gather and analyze customer and sales data and use their conclusions to make marketing, merchandising, advertising, and other business decisions. The second is new to the internet -- it acknowledges a digital, interactive medium that lets businesses respond to user behavior and calculate the next step automatically.
Collaborative filtering is a technique for computer-generated predictions of user preferences. The technology does not use traditional segmentation that divides an audience into categories based on demographic criteria, such as age and gender, or psychographic categories such as "technology early adopters." Instead, collaborative filtering technology compares a user's behavior with the behavior of other users and provides recommendations or promotion based on similarities between different peoples' preferences. But computer-generated recommendations are only as good as the data going in. Some early implementations of collaborative filtering got a bad reputation because they required users to fill out a lengthy form before receiving recommendations. By starting with more data, a personalization system can generate better recommendations, promotions, or ads. Several recently announced partnerships will help websites drive automatic recommendations with a richer set of data.
At Internet World, Andromedia, a provider of web site usage measurement technology, announced that it had merged with LikeMinds, a smaller vendor of collaborative filtering technology. The combined company will integrate the two product lines, enabling the LikeMinds collaborative filtering engine to make recommendations based on the user profile data collected by the Andromedia Aria web site monitoring server.
Andromedia also announced an alliance with Engage. Under terms of the agreement, customers who subscribe to the Engage's database of user profiles will be able to use those profiles to drive recommendations made by LikeMinds's personalization technology. Advertisers will be able to use the LikeMinds engine with Engage profiles to drive recommendations to the Engage/Accipiter AdManager to serve more personalized and targeted advertisements in real time.
At Internet World, Engage announced a similar agreement with NetPerceptions, a vendor of collaborative filtering and other personalization solutions. The two companies announced an agreement to integrate Engage's Accipiter ad server technology with NetPerceptions's newly announced Ad Targeting software. Users will be able to use the NetPerceptions technology to drive customized ads through the Accipter server.
The partnership between Engage and NetPerceptions was first announced back in August. According to terms of the partnership announced then, users of NetPerceptions's technology will be able to enrich their recommendations with user data from the Engage database. NetPerceptions's products can integrate Engage's profiling technology, enabling users to build persistent user profiles based on visitor surfing patterns, registration, and survey responses.
This interlocking set of partnerships enhances the ability of websites to automatically serve recommendations, marketing messages, or ads, based on a richer set of user data.
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