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Use of behavioral portraits in the conduct of e-commerce

Use of behavioral portraits in the conduct of e-commerce description/claims

The present disclosure relates generally to methods for customizing web page content, and more specifically, to methods for generating user behavioral portraits based on web site navigation and search behavior, and for dynamically reconfiguring web page content based on such portraits to produce personalized web page content.

As e-commerce has evolved into a widespread means of doing business, online competition among merchants has increased dramatically. Much of the attention in online marketing has been directed towards placing advertisements for products or services as close to a spending decision as possible, since this is often a significant factor in an online merchant's likelihood of success.

As a specific example, a car rental company might design their website so that it is likely to turn up as a relevant hit when a consumer uses a search engine to search the term “car rental”. The company might even purchase prioritization from one or more businesses that manage popular search engines such as YAHOO!® or GOOGLE® so that their web site will appear near the top of the search results page whenever terms indicating an interest in car rentals are input into the search engine. In some cases, the company may even go the additional step of purchasing banner ads or pop-ups that are triggered by relevant search queries.

While the foregoing approach may be part of a sound online marketing strategy, it suffers from the drawback that it relies upon an overt manifestation of consumer interest to identify potential purchasers of a product or service. Consequently, such an approach may miss a significant number of sales opportunities, simply because it identifies many potential purchasers of a product or service well after a spending decision has been made. In the interim, the consumer may have been exposed to a wide variety of competing products and services.

Some of the more recent refinements in online marketing have focused on placing products or services even closer to a spending decision by looking for more subtle clues to a consumer's interests. Referring back to the previous example, the car rental company may place advertisements on web sites that help consumers to purchase airline tickets, based on the realization that a significant number of people who are purchasing airline tickets will also require a rental car. However, while this type of approach may also form part of a sound online strategy, it is founded on correlations that may be weak. Hence, this type of approach often yields a low success rate.

Other methods of online marketing have evolved which seek to match advertising content to perspective purchasers based on relevance determined from broad demographic information or consumer purchase history. For example, some websites use pop-up ads and banners whose content is selected based on the gender and age of a consumer provided during web site registration, on information gleaned from previous on-line purchases by the consumer, or on the geographic region indicated, for example, by the user's IP address.

However, methods which rely on data obtained from web site registration are of limited utility, since many consumers are hesitant to spend time on websites completing forms and profiles for what is perceived to be of little benefit. Methods based on broad demographic information frequently have a low success rate, since they are necessarily based on broad generalizations which may not apply to a given consumer. Methods based on purchase history are prone to error, since simple product relationships based on previous purchases can be misleading. Previous purchases may have no bearing on the consumer's personal interests, as may be the case if those purchases represent gifts purchased for others. Moreover, even if the previous purchases were for the consumer's personal enjoyment, those purchases may not represent the consumer's current interests. For example, the fact that a consumer's browsing history or previous purchases indicate a past interest in travel does not mean that the consumer has a current interest in travel. The consumer may have exhausted all of his vacation time, and is now interested in goods and services commensurate with a regular work schedule.

In the following figures, like reference numerals indicate like elements.

FIG. A1 is a flowchart illustrating some of the general methodologies described herein.

FIG. A2 is an illustration of a network over which behavioral portraits may be gathered in accordance with some of the methodologies described herein.

FIG. A3 is an illustration of user actions which may be analyzed in the building of a behavioral portrait in accordance with some of the methodologies described herein.

FIG. A4 is an illustration of some of the questions which can be answered with the information provided by some of the systems and methodologies described herein, as compared to the information gleaned by conventional methodologies.

FIG. B1 is an illustration of a particular, non-limiting embodiment of a network equipped with a dedicated server appliance for implementing some of the software and methodologies taught herein.

FIG. B2 is an illustration of a particular, non-limiting embodiment of a network equipped with a dedicated server appliance for implementing some of the software and methodologies taught herein.

FIG. B3 is an illustration of a particular, non-limiting embodiment of a network equipped with a dedicated server appliance for implementing some of the software and methodologies taught herein.

FIG. B4 is an illustration of a particular, non-limiting embodiment of a network architecture for implementing some of the software and methodologies taught herein.

FIG. C1 is an illustration of a web page.

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