COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR COMBINING KEYWORDS INTO LOGICAL CLUSTERS THAT SHARE SIMILAR BEHAVIOR WITH RESPECT TO A CONSIDERED DIMENSION

Abstract

A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:

FIG. 1 is a block diagram of a network system on which an embodiment may operate.

FIGS. 2 a and 2b are a block diagram of a computer system on which an embodiment may operate.

FIG. 3 illustrates the structure and components of the Keyword Testing Complex of one embodiment.

FIG. 4 illustrates the structure and flow of the keyword scrubbing module of one embodiment.

FIG. 5 illustrates an example of the metrics processed by the keyword aggregator of one embodiment.

FIG. 6 illustrates an example of keyword partitioning.

FIG. 7 illustrates an example of allocations for each head keyword and each tail keyword.

FIG. 8 illustrates an example of a table of the data generated for each keyword.

Claims

1. A method comprising: ordering a list of keywords from high activity to low activity,partitioning the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list;modeling the keywords in the head partition based on a set of variables;scoring the keywords in the head partition based on the modeling; andclustering head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

2. The method as claimed in claim 1 wherein the set of variables include seasonality variables.

3. The method as claimed in claim 1 wherein the set of variables include geo-targeting variables.

4. The method as claimed in claim 1 wherein the set of variables include demonstrated user behavior variables.

5. The method as claimed in claim 1 wherein the set of variables include pop culture variables.

6. The method as claimed in claim 1 further including generating a revenue per click (RPC) value for the at least one keyword cluster.

7. The method as claimed in claim 6 further including incorporating value for confirmed registered user (VCRU) data into the generated RPC value.

8. The method as claimed in claim 6 further including incorporating paid search bidding revenue data into the generated RPC value.

9. The method as claimed in claim 1 further including creating the at least one keyword cluster based in part on value for confirmed registered user (VCRU) data.

10. The method as claimed in claim 1 further including generating a revenue per click (RPC) value for at least one keyword in the list of keywords.

11. An article of manufacture comprising at least one machine readable storage medium having one or more computer programs stored thereon and operable on one or more computing systems to: order a list of keywords from high activity to low activity,partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list,model the keywords in the head partition based on a set of variables,score the keywords in the head partition based on the modeling, andcluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

12. The article of manufacture as claimed in claim 11 wherein the set of variables include seasonality variables.

13. The article of manufacture as claimed in claim 11 wherein the set of variables include geo-targeting variables.

14. The article of manufacture as claimed in claim 11 wherein the set of variables include demonstrated user behavior variables.

15. The article of manufacture as claimed in claim 11 wherein the set of variables include pop culture variables.

16. The article of manufacture as claimed in claim 11 being further operable to generate a revenue per click (RPC) value for the at least one keyword cluster.

17. The article of manufacture as claimed in claim 16 being further operable to incorporate value for confirmed registered user (VCRU) data into the generated RPC value.

18. The article of manufacture as claimed in claim 16 being further operable to incorporate paid search bidding revenue data into the generated RPC value.

19. The article of manufacture as claimed in claim 11 being further operable to create the at least one keyword cluster based in part on value for confirmed registered user (VCRU) data.

20. The article of manufacture as claimed in claim 11 being further operable to generate a revenue per click (RPC) value for at least one keyword in the list of keywords.

21. A system comprising: a processor;a memory coupled to the processor to store information related to keywords; anda keyword cluster generator, operably coupled with the processor and the memory, operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

22. The system as claimed in claim 21 wherein the set of variables include seasonality variables.

23. The system as claimed in claim 21 wherein the set of variables include geo-targeting variables.

24. The system as claimed in claim 21 wherein the set of variables include demonstrated user behavior variables.

25. The system as claimed in claim 21 wherein the set of variables include pop culture variables.

26. The system as claimed in claim 21 being further operable to generate a revenue per click (RPC) value for the at least one keyword cluster.

27. The system as claimed in claim 26 being further operable to incorporate value for confirmed registered user (VCRU) data into the generated RPC value.

28. The system as claimed in claim 26 being further operable to incorporate paid search bidding revenue data into the generated RPC value.

29. The system as claimed in claim 21 being further operable to create the at least one keyword cluster based in part on value for confirmed registered user (VCRU) data.

30. The system as claimed in claim 21 being further operable to generate a revenue per click (RPC) value for at least one keyword in the list of keywords.