SYSTEMS AND METHODS FOR BEHAVIORAL MODELING TO OPTIMIZE SHOPPING CART CONVERSION

• US 20120323682A1
• Filed: 06/15/2012
• Published: 12/20/2012
• Est. Priority Date: 06/15/2011
• Status: Abandoned Application

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Shanbhag, Amit Umesh, Shen, Zeqian, Cholavaram, Nausher Ahmed, Pandurangan, Senthil Kumar, Decoste, Dennis
• Application Number:
  US13/524,187
• Publication Number:
  US 20120323682A1
• Time in Patent Office: Days
• US Class Current: 705/14.51
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

Systems and methods for behavioral modeling to optimize shopping cart conversion are discussed. For example, a method can include identifying a user interacting with a networked system, accessing user profile data associated with the user, tracking user activity associated with the user, accessing a behavioral model, applying the behavioral model, and determining a shopping cart optimization. The behavioral model can be generated from historical data detailing interactions with the networked system. The behavioral model can be applied to the user profiled data and the user activity data to assist in selection of a shopping cart optimization.

Claims

1. A method comprising:

identifying, within a network-based system using one or more processors, a user interacting with content published by the network-based system;

accessing profile data associated with the user;

tracking, on the network-based system using the one or more processors, user activity associated with the user interacting with the content published by the network-based system;

accessing a behavioral model generated from historical data detailing interactions of a plurality of users with the network-based system.

applying, using the one or more processors, the behavioral model to the profile data and the tracked user activity; and

determining, based at least in part on the applying the behavioral model to the profile data and the tracked user activity, a shopping cart optimization to optimize shopping cart conversion.

2. The method of claim 1, wherein the applying the behavioral model includes determining, based at least in part on the tracked user activity, a phase of interaction with the network-based system associated with the user.

3. The method of claim 2, wherein the determining the phase of interaction includes selecting the phase of interaction from a group of phases including:

exploration;

active shopping;

checkout; and

abandonment.

4. The method of claim 2, wherein the determining the phase of interaction includes determining, based on the tracked user activity, a matching node within the behavioral model, wherein the behavioral model includes a hierarchy of nodes mapped to various phases of interaction within the network-based system.

5. The method of claim 1, wherein the determining the shopping cart optimization includes selecting a specific call to action to be triggered within the network-based system targeted at the user activity associated with the user.

6. The method of claim 5, wherein the selecting the specific call to action includes selecting a call to action from a group of calls to action including:

increased merchandising;

decreased merchandising;

real-time messaging; and

coupon presentment.

7. The method of claim 5, wherein the determining the shopping cart optimization includes determining whether the user activity indicates that the user intends to perform an action from a group of actions including:

checkout;

add items to a cart;

abandon a shopping cart with items; and

return to an abandoned cart.

8. The method of claim 7, wherein the determining the shopping cart optimization includes determining a predicted day of return to an abandoned cart.

9. A network-based system comprising:

one or more processors to execute a plurality of data processing modules, the data processing modules including;

a targeting module to;

identify a user interacting with the network-based system,

access profile data associated with the user, and

track user activity associated with the user;

a behavioral modeling engine to generate behavioral models based on historical data detailing interactions of a plurality of users with the network-based system; and

a rules engine to;

access a behavioral model generated by the behavioral modeling engine,

apply the behavioral model to the profile data and the tracked user activity associated with the user, and

select, from a call to action repository based on application of the behavioral model, a shopping cart optimization to optimize shopping cart conversion.

10. The network-based system of claim 9, wherein the rules engine is to determine, based at least in part on the tracked user activity, a phase of interaction with the network-based system associated with the user.

11. The network-based system of claim 10, wherein the rules engine is to select the phase of interaction from a group of phases including:

exploration;

active shopping;

checkout; and

abandonment.

12. The network-based system of claim 10, wherein the rules engine is to determine, based on the tracked user activity, a matching node within the behavioral model;

wherein the behavioral modeling engine generates a hierarchy of nodes mapped to various phases of interaction within the network-based system.

13. The network-based system of claim 9, wherein the rules engine is to select the shopping cart optimization at least in part by selecting a specific call to action to be implemented within a checkout module executed within the network-based system.

14. The network-based system of claim 13, wherein the rules engine selects a specific call to action from a group of calls to action including:

increased merchandising;

decreased merchandising;

real-time messaging; and

coupon presentment.

15. The network-based system of claim 13, wherein the rules engine determines, as part of selecting the shopping cart optimization, whether the user activity indicates that the user intends to perform an action from a group of actions including:

checkout;

add items to a cart;

abandon a shopping cart with items; and

return to an abandoned cart.

16. The network-based system of claim 15, wherein the rules engine determines a predicted day of return to an abandoned cart when the rules engine determines that the user intends to return to an abandoned cart.

17. A machine-readable storage medium including instructions that, when executed within a network-based system, cause the network-based system to:

identify a user interacting with content published by the network-based system;

access profile data associated with the user;

track user activity associated with the user interacting with the content published by the network-based system;

access a behavioral model, the behavioral model generated from historical data detailing interactions of a plurality of users with the network-based system.

apply the behavioral model to the profile data and the tracked user activity; and

determine, based at least in part on the applying the behavioral model to the profile data and the tracked user activity, a shopping cart optimization to optimize shopping cart conversion.

18. The machine-readable storage medium of claim 17, wherein the instructions that cause the network-based system to apply the behavioral model further include instructions that cause the network-based system to determine, based at least in part on the tracked user activity, a phase of interaction with the network-based system associated with the user.

19. The machine-readable storage medium of claim 18, wherein the instructions that cause the network-based system to determine the phase of interaction include instructions that cause the network-based system to determine, based on the tracked user activity, a matching node within the behavioral model, wherein the behavioral model includes a hierarchy of nodes mapped to various phases of interaction within the network-based system.

20. The machine-readable storage medium of claim 17, wherein the instructions that cause the network-based system to determine the shopping cart optimization include instructions that cause the network-based system to select a specific call to action to be triggered within the network-based system targeted at the user activity associated with the user, the call to action selected from a group of calls to action including:

checkout;

add items to a cart;

abandon a shopping cart with items; and

return to an abandoned cart.