Custom Fit

Personalization can greatly improve productivity and usability while providing key marketing advantages

By Colin White

PRIVACY MATTERS

OVERCOMING OPPOSITION TO PERSONALIZATION One issue that should not be forgotten in this discussion of personalization, is privacy. Web personalization has created a lot of heated discussion of late, and there is mounting opposition to collecting information for enabling personalization. To resolve this problem, Internet users have to be sold on the benefits of personalization and need to be reassured that the information that is being gathered will not be misused or sold to third parties. They also should be given the opportunity to opt out of the personalization process. Ideally they should be allowed to view and change their personal profiles as well. This latter feature, however, is beyond the capabilities of existing software, except for very simplistic profiles.

Easier-to-find information. Better e-business application usability. Improved productivity. More focused working client relationships. These are the benefits of personalization for consumer and business users of the Internet and intranets. In today's competitive market you cannot afford to ignore personalization applications. These applications customize the Web content consumers and business users view through their desktop and mobile online interfaces.

In this article, I will explore personalization from two perspectives - the consumer and the business user. I will first examine how consumer e-business applications employ personalization to customize Web storefronts to create a one-to-one e-marketing experience for Internet users. Then I will discuss the role of personalization in the corporate environment for providing front-office users and trading partners with a personalized view of business content that enables them to do their jobs and run business operations more effectively.

Personalization for the Consumer

In the consumer e-business marketplace, personalization can be driven by Internet users themselves or by the e-business applications that interact with users. An example of user-driven personalization is my.yahoo.com where you can create a personal profile of the information resources (news feeds, weather, stock prices, and so on) that you wish to see displayed on the home page when you first connect to the Yahoo site. Users do not have to navigate the generic Yahoo home page to find the content they require - instead, the Web server tailors the displayed content based on each user's profile. For online shopping, Yahoo also has a My Shopping profile that enables consumers to manage orders and to specify the online retail stores they like to visit.

User-driven personalization is typically used for filtering content such as news feeds, rather than for the one-to-one e-marketing of products and services. The main reason that it is not used for marketing products is that consumers are rarely motivated to create profiles detailing the types of goods they like to purchase. Nonetheless, there are some exceptions. Lands' End, for example, offers My Virtual Model, in which you enter your measurements and then try clothes on a 3D model. Over 1.5 million people have entered their measurements since this option became available on the Lands' End Web site. In general, however, if you are selling products over the Internet, your e-business applications must either learn about the user's preferences over a period of time, or place each customer in a particular market segment, and then use marketing data for that segment to personalize Web pages of product offerings.

Inference-Based Software

E-business applications employ either inference-based or rules-based techniques to personalize the products and services sold over the Internet. Inference-based software is usually used for cross-selling when a business has a wide range of different products and product lines to market on the Web. It is also useful when little is known about customers and their preferences. With an inference-based approach, the software tracks, or profiles, a Web user's behavior, identifies other people (sometimes called mentors) who have a similar behavior, and then uses these mentors in conjunction with a recommendation engine to create product suggestions that may be of interest to the current Web visitor. Amazon.com is a good example of this approach.

After purchasing a product on Amazon, the Web user is shown a list of other products that were bought by people who purchased the same product. One problem with the referrals, of course, is that a product cannot be recommended until someone else has already bought it. Products that provide inference-based recommendation engines include Macromedia LikeMinds and the Recommendation Engine in Net Perceptions' Personalization Manager. These products are often included with Web application servers, for example, IBM WebSphere (LikeMinds) and Vignette V/5 (Personalization Manager).