EVALUATING AND COMPARING PREDICTED CUSTOMER PURCHASE BEHAVIOR FOR EDUCATIONAL TECHNOLOGY PRODUCTS

• US 20170372337A1
• Filed: 06/28/2016
• Published: 12/28/2017
• Est. Priority Date: 06/28/2016
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Han, Zhaoying, King, III, Coleman Patrick, Cheng, Yiying, Wang, Juan
• Application Number:
  US15/195,870
• Publication Number:
  US 20170372337A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/306 User profiles

Abstract

The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of overall scores representing predicted purchase behaviors of a set of customers with an educational technology product. Next, the system displays a graphical user interface (GUI) comprising a customer prioritization chart for the educational technology product. The system then displays representations of the overall scores in the customer prioritization chart. Finally, the system displays, in the GUI, the set of overall scores and a breakdown of the overall scores into a set of sub-scores that characterize different components of the overall scores.

Claims

1. A method, comprising:

obtaining a set of overall scores representing predicted purchase behaviors of a set of customers with an educational technology product;

displaying, by a computer system, a graphical user interface (GUI) comprising a customer prioritization chart for the educational technology product;

displaying representations of the overall scores in the customer prioritization chart; and

displaying, in the GUI, the set of overall scores and a breakdown of the overall scores into a set of sub-scores that characterize different components of the overall scores.

2. The method of claim 1, further comprising:

obtaining, for the set of customers, a set of values of a customer prioritization metric; and

displaying representations of the set of values in the customer prioritization chart.

3. The method of claim 2, wherein the chart comprises:

a first axis representing the overall scores; and

a second axis representing the customer prioritization metric.

4. The method of claim 2, wherein the customer prioritization metric comprises a potential spending.

5. The method of claim 1, wherein the sub-scores comprise:

a similarity score representing a similarity of a customer to existing customers of the educational technology product;

an engagement score representing a level of engagement of the customer with an online professional network; and

a learning culture score representing an amount of learning culture associated with the customer.

6. The method of claim 1, further comprising:

displaying, in the GUI, one or more attributes of the customers with the set of overall scores and the breakdown of the overall scores into the sub-scores.

7. The method of claim 6, wherein the one or more attributes comprise at least one of:

an account ID;

an account name;

an industry; and

a number of employees.

8. The method of claim 1, further comprising:

obtaining one or more filters from a user through the GUI; and

updating the representations in the customer prioritization chart based on the one or more filters.

9. The method of claim 8, wherein the one or more filters comprise at least one of:

an account owner;

a manager;

an overall score range; and

a range of values for a customer prioritization metric.

10. The method of claim 1, wherein obtaining the set of overall scores comprises:

inputting a set of features for a customer of the educational technology product into a joint model;

using the joint model to calculate multiple values of the overall score; and

combining the multiple values into a final value of the overall score.

11. The method of claim 10, wherein the one or more statistical models comprise:

a random forest; and

a gradient-boosted tree.

12. An apparatus, comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to;

obtain a set of overall scores representing predicted purchase behaviors of a set of customers with an educational technology product;

display a graphical user interface (GUI) comprising a customer prioritization chart for the educational technology product;

display representations of the overall scores in the customer prioritization chart; and

display, in the GUI, the set of overall scores and a breakdown of the overall scores into a set of sub-scores that characterize different components of the overall scores.

13. The apparatus of claim 12, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

obtain, for the set of customers, a set of values of a customer prioritization metric; and

display representations of the set of values in the customer prioritization chart.

14. The apparatus of claim 13, wherein the chart comprises:

a first axis representing the overall scores; and

a second axis representing the customer prioritization metric.

15. The apparatus of claim 13, wherein the customer prioritization metric comprises a potential spending.

16. The apparatus of claim 12, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

display, in the GUI, one or more attributes of the customers with the set of overall scores and the breakdown of the overall scores into the sub-scores.

17. The apparatus of claim 12, wherein the sub-scores comprise:

a similarity score representing a similarity of a customer to existing customers of the educational technology product;

an engagement score representing a level of engagement of the customer with an online professional network; and

a learning culture score representing an amount of learning culture associated with the customer.

18. The apparatus of claim 12, wherein obtaining the set of overall scores comprises:

inputting a set of features for a customer of the educational technology product into a joint model;

using the joint model to calculate multiple values of the overall score; and

combining the multiple values into a final value of the overall score.

19. A system, comprising:

an analysis module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to obtain a set of overall scores representing predicted purchase behaviors of a set of customers with an educational technology product; and

a management module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to;

display a graphical user interface (GUI) comprising a customer prioritization chart for the educational technology product;

display representations of the overall scores in the customer prioritization chart; and

display, in the GUI, the set of overall scores and a breakdown of the overall scores into a set of sub-scores that characterize different components of the overall scores.

20. The system of claim 19, wherein the non-transitory computer-readable medium of the management module further stores instructions that, when executed, cause the system to:

obtain, for the set of customers, a set of values of a customer prioritization metric; and

display representations of the set of values in the customer prioritization chart.