System and method for identifying and approaching browsers most likely to transact business based upon real-time data mining

• US 20060015390A1
• Filed: 11/03/2004
• Published: 01/19/2006
• Est. Priority Date: 10/26/2000
• Status: Abandoned Application

• Current Assignee: LivePerson Incorporated
• Original Assignee: LivePerson Incorporated
• Inventors: Rijsinghani, Vikas, Freishtat, Gregg
• Application Number:
  US10/980,613
• Publication Number:
  US 20060015390A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

The present invention is directed to a system and functionality that removes the guess work out of trying to determine which browsers on a web site are more likely to end up with a good disposition. One approach introduced by the present invention is to first make sure the sales server captures as much information about browsers as is possible with respect to their activity on the website/ecommerce server. Then the server enables the enterprise to use business rules to define the population of browsers that are eligible for chat invitations. Out of this population, the server, on behalf of individual agents, approaches browsers as randomly as possible. As agents are entering into engagements and recording their disposition codes, the server periodically determines if it can identify any patterns in behavior of those engagements that end up with a good disposition code. For example, the server may note that browsers who were invited to chat in the 8th minute of their session and those who had seen 2 product pages end up in good engagements four times more often than the average browser. Once a sufficient sample set of engagements is conducted to allow the server to develop a statistically valid profile/model of browsers who end up with good engagements, the server compares all new browsers against this model and provides a numeric number representing how close the new browser is to the model. This number, called a score, is then used by the system to sort the browsers in real time and used as the criteria as to who should be approached and in which order.

Claims

1. A method for identifying and approaching high value browsers on a web site, the method comprising the steps of:

a. selecting a type of high value transaction associated with the web site;

b. identifying a plurality of browsers that have performed on the web site a transaction of the high value transaction type;

c. storing a set of attributes associated with each of the identified plurality of browsers;

d. generating the most common attributes of the stored set;

e. comparing attributes of a new browser on the web site to the generated most common attributes of the stored set; and

f. approaching the new browser if the attributes of the new browser are similar to the generated most common attributes of the stored set.

2. The method of claim 1, wherein the most common attributes of the stored set are generated using a regression analysis.

3. The method of claim 1, wherein the type of high value transaction represents a purchase of a product or service from the operator of the web site.

4. The method of claim 1, wherein the approaching step is performed by a sales agent.

5. The method of claim 1, wherein the identifying step is performed by randomly approaching browsers, and recording the stored set of attributes associated with the randomly approached browsers.

6. A method for identifying and approaching high value browsers on a web site, the method comprising the steps of:

a. selecting a type of high value transaction associated with the web site;

b. randomly approaching a plurality of browsers on the web site, in order to identify a selected plurality of the browsers that have performed a transaction of the high value transaction type;

c. storing a set of attributes associated with each of the identified selected plurality of browsers;

d. performing a regression analysis on the stored set, thereby obtaining the most common attributes of the stored set;

e. comparing attributes of a new browser on the web site to the generated most common attributes of the stored set; and

f. approaching the new browser by a sales agent if the attributes of the new browser are similar to the generated most common attributes of the stored set.

7. A system for identifying and approaching high value browsers on a web site, the system comprising:

a. a database; and

b. a processor for performing the steps of;

i. selecting a type of high value transaction associated with the web site;

ii. identifying a plurality of browsers that have performed on the web site a transaction of the high value transaction type;

iii. storing in the database a set of attributes associated with each of the identified plurality of browsers;

iv. generating in the database the most common attributes of the stored set;

v. comparing attributes of a new browser on the web site to the most common attributes of the stored set; and

vi. approaching the new browser if the attributes of the new browser are similar to the generated most common attributes of the stored set.

8. The system of claim 7, wherein the most common attributes of the stored set are generated using a regression analysis.

9. The system of claim 7, wherein the type of high value transaction represents a purchase of a product or service from the operator of the web site.

10. The system of claim 7, wherein the approaching step is performed by a sales agent.

11. The system of claim 7, wherein the identifying step is performed by randomly approaching browsers, and recording the stored set of attributes associated with the randomly approached browsers.

12. A system for identifying and approaching high value browsers on a web site, the system comprising:

a. a database; and

b. a processor for performing the steps of;

i. selecting a type of high value transaction associated with the web site;

ii. randomly approaching a plurality of browsers on the web site, in order to identify a selected plurality of the browsers that have performed a transaction of the high value transaction type;

iii. storing in the database a set of attributes associated with each of the identified selected plurality of browsers;

iv. performing a regression analysis on the set stored in the database, thereby obtaining the most common attributes of the stored set;

v. comparing attributes of a new browser on the web site to the generated most common attributes of the stored set; and

vi. approaching the new browser by a sales agent if the attributes of the new browser are similar to the generated most common attributes of the stored set.

13. A computer-readable storage medium containing a set of instructions for execution by a computer, the set of instructions for performing the steps of:

a. selecting a type of high value transaction associated with the web site;

b. identifying a plurality of browsers that have performed on the web site a transaction of the high value transaction type;

c. storing a set of attributes associated with each of the identified plurality of browsers;

d. generating the most common attributes of the stored set;

e. comparing attributes of a new browser on the web site to the generated most common attributes of the stored set; and

f. approaching the new browser if the attributes of the new browser are similar to the generated most common attributes of the stored set.

14. A computer-readable storage medium containing a set of instructions for execution by a computer, the set of instructions for performing the steps of:

a. selecting a type of high value transaction associated with the web site;

b. randomly approaching a plurality of browsers on the web site, in order to identify a selected plurality of the browsers that have performed a transaction of the high value transaction type;

c. storing a set of attributes associated with each of the identified selected plurality of browsers;

d. performing a regression analysis on the stored set, thereby obtaining the most common attributes of the stored set;

e. comparing attributes of a new browser on the web site to the generated most common attributes of the stored set; and

f. approaching the new browser by a sales agent if the attributes of the new browser are similar to the generated most common attributes of the stored set.