Dynamic prediction of online shopper's intent using a combination of prediction models

• US 10,373,177 B2
• Filed: 02/06/2014
• Issued: 08/06/2019
• Est. Priority Date: 02/07/2013
• Status: Active Grant

• Current Assignee: [24]7.ai, Inc.
• Original Assignee: [24]7.ai, Inc.
• Inventors: Vijayaraghavan, Ravi, Adusumilli, Kranthi Mitra, Kulkarni, Subhash Ramchandra, Prakash, Ravi
• Primary Examiner(s): Dunham, Jason B
• Assistant Examiner(s): Loharikar, Anand R
• Application Number:
  US14/174,678
• Publication Number:
  US 20140222503A1
• Time in Patent Office: 2,007 Days
• Field of Search: 705 261- 272
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Online browsing behavior is used to predict the intent of online users dynamically. The category of online user is predicted and the prediction can be used to provide assistance to the user, if required. Such prediction is based on a combination of a Naïve'"'"'s Bayes classifier and a Markov model.

Claims

1. A computer implemented method for user intent analysis, comprising:

providing a processor configured for receiving a user Web server request from a user device through at least one of a plurality of channels;

said processor collecting data traffic of the user at the Web server;

said processor analyzing Web path taken by the user in real time while the user is visiting a Web site;

said processor developing at least two predictive models based on said Web path analysis by;

applying a Naï

ve Bayes'"'"' function to predict raw intent with static information only; and

combining results of said Naï

ve Bayes'"'"' function with first-order Markov information to capture a dynamic nature of user intent with each step of a user'"'"'s navigation during a Web journey;

said processor testing said at least two predictive models with Web path data of another of a plurality of users;

based upon said path analysis, said processor using dynamic models to infer user intent from the Web path as the user visit to the Web site commences;

based on said path analysis, said processor categorizing said user as any of;

a browser comprising users who browse without purpose;

a knowledge seeker comprising users who browse to gather information;

a purchaser with assistance comprising users who want to buy but are not very clear and, therefore, need assistance; and

a purchaser with self-help comprising users who know exactly what is wanted and where to find it;

said processor updating said predictive models based upon both a user category and by how far along a search path the user has progressed;

said processor using said predictive models to predict the intent of the user before the user exits the Web site; and

in response to the models predicting that the probability of a particular intent is high on a specific page, and before the user leaves the Web site, said processor proactively offering said user any of chat and a personalized offer to help meet any of the user'"'"'s expectations and requirements on said user device.

2. The method of claim 1, further comprising:

based on said path analysis, said processor categorizing said user as any of;

a browser comprising users who browse without purpose;

a knowledge seeker comprising users who browse to gather information;

a purchaser with assistance comprising users who want to buy but are not very clear and, therefore, need assistance; and

a purchaser with self-help comprising users who know exactly what is wanted and where to find it.

3. The method of claim 1, wherein said user'"'"'s Web navigation reveals information about a user'"'"'s intent;

and

wherein said intent either changes or gets stronger as the user'"'"'s Web journey matures.

4. The method of claim 1, further comprising:

analyzing data traffic at a Web server by collecting click-stream data related to a Web path that said user takes when navigating through a Web site of a provider.

5. The method of claim 4, wherein said click-stream comprises a record of said user'"'"'s activity on the Internet including factors that comprise any of every Web site and every page of every Web site that the user visits, how long the user was on a page or site, in what order the pages were visited, any newsgroups in which the user participates, and the addresses of any email that the user sends and receives;

and

wherein the path Web taken by the user reflects the user'"'"'s intents, needs, and goals.

6. The method of claim 1, wherein said user'"'"'s online search behavior comprise any of goal-oriented search and exploratory search.

7. The method of claim 1, further comprising:

categorizing said user'"'"'s online search behavior to develop a plurality of intents comprising any of directed-purchase visits, search and deliberation visits, hedonic-browsing visits, and knowledge-building visits.

8. The method of claim 1, wherein a user may belong to one of the following categories:

knowledge seeker, browser, prospect, or customer.

9. An apparatus for user intent analysis, comprising:

a processor receiving a user Web server request from a user device through at least one of a plurality of channels;

said processor collecting data traffic of the user at the Web server;

said processor analyzing Web path taken by the user in real time while the user is visiting a Web site;

said processor developing at least two predictive models based on said Web path analysis by;

applying a Naï

ve Bayes'"'"' function to predict raw intent with static information only; and

combining results of said Naï

ve Bayes'"'"' function with first-order Markov information to capture a dynamic nature of user intent with each step of a user'"'"'s navigation during a Web journey;

said processor testing said at least two predictive models with Web path data of another of a plurality of users;

based upon said path analysis, said processor using dynamic models to infer user intent from the Web path as the user visit to the Web site commences;

based on said path analysis, said processor categorizing said user as any of;

a browser comprising users who browse without purpose;

a knowledge seeker comprising users who browse to gather information;

a purchaser with assistance comprising users who want to buy but are not very clear and, therefore, need assistance; and

a purchaser with self-help comprising users who know exactly what is wanted and where to find it;

said processor updating said predictive models based upon both a user category and by how far along a search path the user has progressed;

said processor using said predictive models to predict the intent of the user before the user exits the Web site; and

in response to the models predicting that the probability of a particular intent is high on a specific page, and before the user leaves the Web site, said processor proactively offering said user any of chat and a personalized offer to help meet any of the user'"'"'s expectations and requirements on said user device.