Use of behavioral portraits in web site analysis
View Patent ↗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.
1. A non-transitory, computer readable medium having recorded therein a software program that contains suitable programming instructions which, when executed by a data processing apparatus, cause the data processing apparatus to perform a method for analyzing a website, the method comprising:
recording data relating to the online behavior of a plurality of users through the website;
building a behavioral portrait for each of the plurality of users based on the data;
categorizing the behavioral portraits of the plurality of users into a plurality of portrait types; and
analyzing the behavior of the plurality of users on the website as a function of portrait type;
wherein the behavioral portrait is built by applying psychological portrait scoring to the data, and wherein the data includes items selected from the group consisting of the pages a user navigates, the items the user clicks on, search terms entered by the user, check-out information, pages abandoned by the user, and the selection of “top 10” items by the user.
2. The computer readable medium of claim 1 , wherein the recorded data relates to behavior selected from the group consisting of (a) items clicked on by a user; (b) the number of times a user abandons the web site; (c) the number of return visits a user makes to the website; (d) the number of online purchases made by a user on the website; (e) the average time a user spends on the web site; and (f) the number of pages visited on the web site by a user.
3. The computer readable medium of claim 2 , further comprising:
outputting graphical information representing the recorded data as a function of portrait type.
4. The computer readable medium of claim 2 , wherein the recorded data is measured over a fixed period of time.
5. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the relative frequency with which items are clicked on by a user as a function of portrait type.
6. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the relative frequency with which users abandon the website as a function of portrait type.
7. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the relative frequency with which users make return visits to the website as a function of portrait type.
8. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the relative frequency of online purchases on the website as a function of portrait type.
9. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the average time users spend on the website as a function of portrait type.
10. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type involves determining the average number of pages users visit on the website as a function of portrait type.
11. The computer readable medium of claim 1 , wherein the non-transitory, computer readable medium is a tangible medium.
12. The computer readable medium of claim 1 , wherein the behavioral portrait is built by utilizing an analysis software package to apply a psychological behavior model scoring system to each action taken by the user.
13. The computer readable medium of claim 1 , wherein the behavioral portrait is a numerical score derived from applying meta linguistic psychological analysis to the data.
14. The computer readable medium of claim 1 , wherein the behavioral portrait is a numerical score derived from the user's progress through the website, wherein the user's actions are weighted by category and lexical analysis, and wherein the weights are based on possible actions that can be taken by a user as the user navigates through the website.
15. The computer readable medium of claim 1 , further comprising:
recommending changes to the manner in which content is presented on the website to users of a portrait type.
16. The computer readable medium of claim 1 , wherein the behavioral portraits are psychological portraits.
17. The computer readable medium of claim 1 , wherein the behavioral portrait for each user is the psychological profile of the user as it pertains to the user's preferences in a particular situation.
18. The computer readable medium of claim 1 , wherein the behavioral portrait for each user includes the user's preferred decision-making approach.
19. The computer readable medium of claim 1 , wherein the behavioral portrait for each user includes the user's motivation for making a purchase.
20. The computer readable medium of claim 1 , wherein the behavioral portrait for each user includes the manner in which the user prefers to have information presented to them.
21. The computer readable medium of claim 1 , wherein the behavioral portrait for each user includes the manner in which the user prefers to have information presented to them, the user's preferred decision-making approach, and the user's motivation for making a purchase.
22. The computer readable medium of claim 1 , wherein analyzing the behavior of the plurality of users on the website as a function of portrait type includes determining the manner in which each of the plurality of users prefers to make decisions, and ascertaining the degree to which the website accommodates the manner in which each of the plurality of users prefers to make decisions.
23. A non-transitory, computer readable medium having recorded therein a software program that contains suitable programming instructions which, when executed by a data processing apparatus, cause the data processing apparatus to perform a method for analyzing a web page, the method comprising:
categorizing the features appearing on the web page in terms of at least one behavioral trait which selection of the feature would indicate;
creating a graphical overlay which reflects the categorization of the features; and
using the categorization of features to create behavioral portraits of users accessing the web page;
wherein the behavioral portrait is built by applying psychological portrait scoring to the selection of features on the web page; and
wherein the categorization of features includes categorizing features selected from the group consisting of the pages a user navigates, the features the user clicks on, search terms entered by the user, check-out information, pages abandoned by the user, and the selection of “top 10” items by the user.
24. The computer readable medium of claim 23 , wherein the graphical overlay comprises a color-coded map.
25. The computer readable medium of claim 24 , further comprising:
superimposing the color-coded map over the web page.
26. The computer readable medium of claim 23 , wherein the non-transitory, computer readable medium is a tangible medium.
27. A non-transitory, computer readable medium having recorded therein a software program that contains suitable programming instructions which, when executed by a data processing apparatus, cause the data processing apparatus to perform a method for analyzing a website, the method comprising:
recording data relating to the online behavior of a plurality of users through the website;
building a behavioral portrait for each of the plurality of users based on the data, wherein the behavioral portrait is built by applying a psychological behavior model scoring system to each action taken by the user as the user navigates through the website, and wherein said actions include actions selected from the group consisting of the pages a user navigates, the items the user clicks on, search terms entered by the user, check-out information, pages abandoned by the user, and the selection of “top 10” items by the user;
categorizing the behavioral portraits of the plurality of users into a plurality of portrait types; and
analyzing the behavior of the plurality of users on the website as a function of portrait type to which the users belong.