Systems and methods of data mining to customize software trial demonstrations

Patent metadata

Field	Value
Publication number	US-10007956-B2
Application number	US-201715798198-A
Country	US
Kind code	B2
Filing date	Oct 30, 2017
Priority date	Jan 29, 2015
Publication date	Jun 26, 2018
Grant date	Jun 26, 2018

Abstract

The technology disclosed describes systems and methods for delivering software trial demonstrations that are customized, with features identified as interesting to a software demonstration candidate, by mining biographical and behavioral data of the candidate. The technology further discloses systems and methods for the customization of trial demonstrations with software usage stories that reflect a software demonstration candidate's interests, identified by analyzing mined biographical and behavioral data about the candidate.

First claim

What is claimed is:

1. A computer implemented system applied to customizing a demonstration of a software suite with numerous features and deployments, the computer implemented system including:
a processor, memory coupled to the processor, and program instructions stored in the memory that implement a method comprising:
obtaining from at least one of a user profile and a social profile, at least some user attributes for a demonstration candidate, including a role of the demonstration candidate within a demonstration candidate's company;
obtaining interest attributes for the demonstration candidate including known competitors of the demonstration candidate's company extracted from searching in a table of known companies for an intersection between the demonstration candidate's company and known competitors of the demonstration candidate's company;
using at least some of the user attributes for the demonstration candidate to select a subset of features of the software suite to guide the demonstration candidate through during a software demonstration, to include:
accessing and automatically applying at least one trained classifier rule set that maps the user attributes determined for the demonstration candidate to subsets of features in the software suite to select based on parameters of the trained classifier rule set;
using at least some interest attributes of the demonstration candidate to select one or more software usage stories describing successful implementation of the software suite by business entities determined to be potential customers of known competitors of the demonstration candidate's company to profile during the software demonstration; and

customizing the software demonstration for the demonstration candidate with a selected subset of features in the software suite and the software usage stories; and
at least initializing presentation across a display screen of the software demonstration to the demonstration candidate.

2. The computer implemented system of claim 1 , further including obtaining additional user attributes by presenting the demonstration candidate with a questionnaire and receiving responses.

3. The computer implemented system of claim 1 , further including receiving permission from the demonstration candidate to access the social profile of the demonstration candidate and accessing the social profile.

4. The computer implemented system of claim 1 , further including features to more efficiently market the software suite to a demonstration candidate's company's competitors' customers.

5. The computer implemented system of claim 1 , further including accessing and applying at least one rule set that maps the interest attributes to software usage stories to select.

6. The computer implemented system of claim 1 , further including applying the trained classifier rule set to the user attributes and the interest attributes to select the subset of features and the software usage stories.

7. The computer implemented system of claim 6 , further including training a classifier, the method including:
for a test set of data, accessing user attribute and interest attribute data for demonstration users of the software suite;
for the test set of data, accessing at least user dwell times during use of particular features and viewing of particular software usage stories by the demonstration users;
using at least the user attribute and interest attribute data as independent variables and at least the user dwell times as dependent variables, training the classifier to select for demonstration candidates a subset of features of the software suite and to select one or more software usage stories and producing classifier parameters that implement the classifier; and
using the classifier parameters for the customizing of the software demonstration.

8. The computer implemented system of claim 1 , further including as user attributes of the demonstration candidate at least education, past employment, and assigned territory.

9. The computer implemented system of claim 1 , further including as user attributes of the demonstration candidate at least affiliations, endorsements and skills.

10. The computer implemented system of claim 1 , further including as user attributes of the demonstration candidate at least product line responsibility.

11. The computer implemented system of claim 1 , further including as user attributes of the demonstration candidate, other companies that compete with the demonstration candidate's company.

12. The computer implemented system of claim 1 , further comprising a personal interest including admiration of the demonstration candidate for a business leader at the business entities determined to be potential customers of known competitors of the demonstration candidate's company.

13. A non-transitory computer readable storage medium that stores program instructions that implement a method of customizing a demonstration of a software suite with numerous features and deployments, which implementation includes:
obtaining from at least one of a user profile and a social profile, at least some user attributes for a demonstration candidate, including a role of the demonstration candidate within a demonstration candidate's company;
obtaining interest attributes for the demonstration candidate including known competitors of the demonstration candidate's company extracted from searching in a table of known companies for an intersection between the demonstration candidate's company and known competitors of the demonstration candidate's company;
using at least some of the user attributes for the demonstration candidate to select a subset of features of the software suite to guide the demonstration candidate through during a software demonstration, to include:
accessing and automatically applying at least one trained classifier rule set that maps the user attributes determined for the demonstration candidate to subsets of features in the software suite to select based on parameters of the trained classifier rule set;
using at least some interest attributes of the demonstration candidate to select one or more software usage stories describing successful implementation of the software suite by business entities determined to be potential customers of known competitors of the demonstration candidate's company to profile during the software demonstration; and

customizing the software demonstration for the demonstration candidate with a selected subset of features in the software suite and the software usage stories; and
at least initializing presentation across a display screen of the software demonstration to the demonstration candidate.

14. The non-transitory computer readable storage medium of claim 13 , further including obtaining additional user attributes by presenting the demonstration candidate with a questionnaire and receiving responses.

15. The non-transitory computer readable storage medium of claim 13 , further including features to more efficiently market the software suite to a demonstration candidate's company's competitors' customers.

16. The non-transitory computer readable storage medium of claim 13 , further including accessing and applying at least one rule set that maps the interest attributes to software usage stories to select.

17. The non-transitory computer readable storage medium of claim 13 , further including applying the trained classifier rule set to the user attributes and the interest attributes to select the subset of features and the software usage stories.

18. The non-transitory computer read

Assignees

• Salesforce Com Inc

Inventors

• Jemiolo Daniel Everett
• Cronin Beau David

Classifications

• G06Q10/40 Primary

  Business processes related to social networking or social networking services · CPC title

• G06Q50/01 Primary

  Physics · mapped topic

• G06Q30/0276 Primary

  Advertisement creation · CPC title

• G06Q10/42

  Determination of affinities or common interests between users · CPC title