Use of behavioral portraits in web site analysis

a behavioral portrait and web site technology, applied in the field of customizing web page content, can solve the problems of low success rate of approach, a significant number of sales opportunities missed, and limited utility of methods based on data obtained from web site registration

Inactive Publication Date: 2008-09-18
HERITAGE OPPORTUNITY FUND
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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.
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.
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.
Moreover, even if the previous purchases were for the consumer's personal enjoyment, those purchases may not represent the consumer's current interests.

Method used

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  • Use of behavioral portraits in web site analysis

Examples

Experimental program
Comparison scheme
Effect test

example c1

[0090]The following example illustrates the application of some of the methodologies taught herein to an e-commerce transaction.

[0091]User A decides to purchase a DVD player from a multi-category website (that is, a website which sells electronics, books, clothing, and various other goods and services). The user's pass through the website involves the steps of:[0092](a) Searching for a specific DVD player model;[0093](b) Checking the detailed specifications of the model;[0094](c) Comparing the model with other top sellers;[0095](d) Adding the selected model to the shopping cart;[0096](e) Choosing expedited shipping; and[0097](f) Checkout.

[0098]Each of these individual actions during the web session can be mapped to a set of rules or a knowledge base in the Portrait Engine. An example of such a mapping is shown in FIGS. C1-C2. In particular, FIG. C1 shows a standard e-commerce web page with a selection of electronics products, specifically DVD Players. FIG. C2 shows the same web page...

example c2

[0114]This example illustrates how user behavior may be tracked, weighted and utilized to develop a behavioral portrait for the user.

[0115]A user's behavior on a website is recorded and analyzed. That behavior may include such factors as:[0116](a) navigation selections (which navigation choices are made on the site);[0117](b) search text parsing and analysis (search used to navigate to a particular area on the site in conjunction with term analysis);[0118](c) icon selection (where the user clicks);[0119](d) marketing offer selection (the type of marketing offer a user selects);[0120](e) order of behavior (e.g., whether the user engages in seemingly random “browsing” or more focused selections);[0121](f) speed of navigation (timed clicks, e.g., speed of checkout or skipping less relevant pages); and[0122](g) omitted selections (what the user does not do or select).

[0123]After each action, the user's portrait is recalculated. This calculation is based upon a dynamic (real-time) weight...

example d-1

[0167]This example illustrates the modification of content (on the website of an on-line computer retailer) for two different types of users in accordance with a particular, non-limiting embodiment of the methodologies described herein.

[0168]With respect to FIG. D4 and FIG. D5, some of the behavioral traits of two different hypothetical users are summarized. With respect to FIG. D4, the first user (“Trish”) is a “thinker” type user. Trish is a type of user who is in control of her decisions d4-1, and will often say “I have a gut feel” d4-2. Trish typically makes her decisions based on a review of detailed specifications and trusted sources d4-3, and is motivated by how an offered product will help her to achieve her goals d4-4. She wants to see a positive result from her purchase d4-5, is heavily dependent on process and procedure d4-6, and is not likely to move to the next step in a purchase process before the current step is completed d4-7.

[0169]By contrast, the second hypothetica...

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PUM

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Abstract

A method is provided for determining a website user behavioral portrait based on navigation on the website and dynamically reconfiguring web pages based on those portraits. In accordance with the method, data relating to the progress of a user through a website is recorded, and an ongoing behavioral portrait of the user is built based on the data. The portrait is then used to dynamically reconfigure web content.

Description

FIELD OF THE DISCLOSURE[0001]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.BACKGROUND OF THE DISCLOSURE[0002]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.[0003]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 purchas...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F17/30
CPCG06F17/30867G06Q30/02G06Q30/0201G06F17/2247G06Q30/0269G06Q30/0641G06Q30/0255G06F16/9535G06Q30/0244G06F16/958H04L67/535
Inventor MINNIS, WILLIAM CHARLESNAGAITIS, MARKPOKORNEY, TREVORGUGLIEMETTI, BRIANSCHANK, ERIC
Owner HERITAGE OPPORTUNITY FUND
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