CLIAMS:We claim:

1. A method, implemented by a computer, for analyzing online shopping behavior, the method comprising:
receiving an event indicating shopping activities of a shopper from a shopping channel;
determining an action type associated with the event;
classifying, by a rule engine, the event into one of a plurality of shopping phases based on at least one of: classification rules, the action type, or a history of past events;
calculating, by the computer, a raw score for the shopping phase into which the event is classified base on at least one of: an existing number of events in that shopping phase or an event weight associated with the event;
calculating, by the computer, a weighted score based on the raw score and a weighting factor associated with the shopping phase into which the event is classified; and
determining a target shopping phase based on the weighted score.

2. The method of claim 1, wherein the plurality of shopping phases include two or more of: an Aware phase, a Consider phase, a Learn phase, an Evaluate phase, a Buy phase, an Experience phase, or an Advocate phase.

3. The method of claim 1, further comprising:
updating a shopper map for the shopper based on the classified event, the shopper map comprising a shopper ID, a product category ID, the target shopping phase, the raw score and the weighted score associated with each of the plurality of shopping phases, a time of update, a time of entering a particular shopping phase, and a number of action types performed in a particular shopping phase.

4. The method of claim 3, wherein the updating comprises:
generating a new shopper map upon determining that the shopper map for the shopper is not present in a context store.

5. The method of claim 1, further comprising:
retrieving the history of past events from a context store, wherein the history of past events includes a past shopper map based on history information prior to receiving the event.

6. The method of claim 1, wherein
the event includes at least one of: a clickstream event or a device activity event originating from the shopper; and
the event comprises an event type and at least one of: a shopper ID, a product category ID, a product ID, or a time stamp.

7. The method of claim 1, wherein calculating the weighted score is based on at least one of:
a saturation factor indicating a maximum number of events within a shopping phase to be used for calculating the weight score;
a decay factor for reducing the weighted score due to a time gap between two events; or
a bonus factor for increasing the weighted score due to linear shopping behavior.

8. The method of claim 1, wherein determining the target shopping phase comprises:
selecting a shopping phase having the highest weighted score as the target shopping phase.

9. The method of claim 1, wherein the classification rules, the weighing factor, and the event weight are configurable by a business user.

10. A computer system for analyzing online shopping behavior, the system comprising:
a processor operatively coupled to a memory device, wherein the processor is configured to execute instructions stored in the memory device to perform operations comprising:
receiving an event indicating shopping activities of a shopper from a shopping channel;
determining an action type associated with the event;
classifying, by a rule engine, the event into one of a plurality of shopping phases based on at least one of: classification rules, the action type, or a history of past events;
calculating, by the computer, a raw score for the shopping phase into which the event is classified base on at least one of: an existing number of events in that shopping phase or an event weight associated with the event;
calculating, by the computer, a weighted score based on the raw score and a weighting factor associated with the shopping phase into which the event is classified; and
determining a target shopping phase based on the weighted score.

11. The system of claim 10, wherein the plurality of shopping phases include two or more of: an Aware phase, a Consider phase, a Learn phase, an Evaluate phase, a Buy phase, an Experience phase, or Advocate phase.

12. The system of claim 10, wherein the operations further comprise:
updating a shopper map for the shopper based on the classified event, the shopper map comprising a shopper ID, a product category ID, the target shopping phase, the raw score and the weighted score associated with each of the plurality of shopping phases, a time of update, a time of entering a particular shopping phase, and a number of action types performed in a particular shopping phase.

13. The method of claim 12, wherein the updating comprises:
generating a new shopper map upon determining that the shopper map for the shopper is not present in a context store.

14. The system of claim 10, wherein the operations further comprises:
retrieving the history of past events from a context store, wherein the history of past events includes a past shopper map based on history information prior to receiving the event.

15. The system of claim 10, wherein
the event includes at least one of: a clickstream event or a device activity event originating from the shopper; and
the event comprises an event type and at least one of: a shopper ID, a product category ID, a product ID, or a time stamp.

16. The system of claim 10, wherein calculating the weighted score is based on at least one of:
a saturation factor indicating a maximum number of events within a shopping phase to be used for calculating the weight score;
a decay factor for reducing the weighted score due to a time gap between two events; or
a bonus factor for increasing the weighted score due to linear shopping behavior.

17. The system of claim 10, wherein determining the target shopping phase comprises:
selecting a shopping phase having the highest weighted score as the target shopping phase.

18. The system of claim 10, wherein the classification rules, the weighing factor, and the event weight are configurable by a business user.

19. A non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:
receiving an event indicating shopping activities of a shopper from a shopping channel;
determining an action type associated with the event;
classifying, by a rule engine, the event into one of a plurality of shopping phases based on at least one of: classification rules, the action type, or a history of past events;
calculating, by the computer, a raw score for the shopping phase into which the event is classified base on at least one of: an existing number of events in that shopping phase or an event weight associated with the event;
calculating, by the computer, a weighted score based on the raw score and a weighting factor associated with the shopping phase into which the event is classified; and
determining a target shopping phase based on the weighted score.

Dated this 29th day of May, 2014

Swetha S.N
Of K&S Partners
Agent for the Applicant
,TagSPECI:TECHNICAL FIELD

This disclosure relates generally to online e-commerce business. More specifically, it relates to a system and method for determining the shopping behavior of an online shopper.