METHODS AND APPARATUS TO MODEL WITH GHOST GROUPS

• US 20100306028A1
• Filed: 06/02/2009
• Published: 12/02/2010
• Est. Priority Date: 06/02/2009
• Status: Abandoned Application

• Current Assignee: Nielsen Company (US) LLC (RR Donnelley & Sons Company)
• Original Assignee: Nielsen Company (US) LLC (RR Donnelley & Sons Company)
• Inventors: Wagner, John G.
• Application Number:
  US12/476,806
• Publication Number:
  US 20100306028A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls

Abstract

Example methods and apparatus to model with ghost respondents are disclosed. A disclosed example method includes estimating discrete choice utility values for a plurality of respondents based on a plurality of market-available products, and dividing the plurality of respondents into groups based on an ownership status of the plurality of market-available products. The example method also includes identifying a test starter product from the plurality of market-available products based on test criteria indicative of a degree of similarity with the new product, generating a ghost group associated with the new product, and assigning utility values of the test starter product to the new product in the ghost group. Additionally, the example method includes tailoring the utility values assigned to the new product with a ghost group utility adjustment rule, and generating a ghost group model to represent consumers of the new product.

Claims

1. A computer implemented method to model a new product, comprising:

estimating discrete choice utility values for a plurality of respondents based on a plurality of market-available products;

dividing the plurality of respondents into groups based on an ownership status of the plurality of market-available products;

identifying a test starter product from the plurality of market-available products based on test criteria indicative of a degree of similarity with the new product;

generating a ghost group associated with the new product;

assigning utility values of the test starter product to the new product in the ghost group;

tailoring the utility values assigned to the new product with a ghost group utility adjustment rule; and

generating a ghost group model to represent consumers of the new product.

2. A method as defined in claim 1, wherein estimating the discrete choice utility values further comprise performing a hierarchical Bayes estimation on discrete choice responses received from a discrete choice exercise virtual shopping trip.

3. A method as defined in claim 1, wherein the market-available product comprises a holder product and a corresponding refill product.

4. A method as defined in claim 1, wherein the test criteria comprise at least one of a product category threshold, a product size threshold, a product price point threshold, or a product promotion threshold.

5. A method as defined in claim 1, further comprising assigning a first one of the plurality of respondents a unity weight when the first one of the plurality of respondents owns a single one of the plurality of market-available products.

6. A method as defined in claim 1, further comprising assigning a relative partitioned weight to a first one of the plurality of respondents when the first one of the plurality of respondents owns more than one of the plurality of market-available products.

7. A method as defined in claim 1, wherein tailoring the new product utility values further comprises altering a sub-utility value within a threshold value to preserve a choice probability value.

8. A method as defined in claim 1, wherein the ghost group utility adjustment rule is selected based on invocation criteria comprising a threshold sub-utility value associated with the test starter product.

9. A method as defined in claim 1, further comprising combining the tailored product utility values with the discrete choice utility values to create the ghost group model.

10. A method as defined in claim 9, further comprising receiving a simulation scenario to apply to the ghost group model.

11. A method as defined in claim 10, wherein the simulation scenario comprises at least one of an available product, a price of the available product, or a promotion for the available product.

12. A method as defined in claim 10, further comprising calculating choice probability values for each of the plurality of respondents and for the ghost group associated with the new product based on the simulation scenario and the combined utility values in the ghost group model.

13. A method as defined in claim 12, wherein calculating the choice probability values comprises employing a multinomial logit model.

14. A method as defined in claim 12, further comprising performing a respondent group weight adjustment for a number of iterations identified by the simulation scenario.

15. A method as defined in claim 14, further comprising calculating at least one of a respondent weight decrease or a respondent weight increase based on the calculated choice probability values associated with each of the plurality of market-available products and the new product.

16. A method as defined in claim 15, further comprising distributing the weight decrease or weight increase to each of the plurality of respondents and the plurality of ghost respondents.

17. A method as defined in claim 15, further comprising calculating choice shares based on the calculated choice probability values, the combined utility values, and the at least one of the respondent weight decrease or weight increase.

18. A method as defined in claim 1, wherein modeling the new product further comprises employing a choice modeling exercise.

19. A method as defined in claim 1, wherein modeling the new product further comprises modeling a product category.

20. An apparatus to model new products, comprising:

a utility estimator to estimate discrete choice utility values for a plurality of market-available products;

a product matcher to identify a match between the new product and a test starter product from the plurality of market-available products, the product matcher identifying a degree of similarity between the new product and the test starter product;

a ghost group rule manager to generate tailored utility values for the new product based on the test starter product; and

a choice share manager to combine the tailored utility values with the discrete choice utility values to create a ghost group model.

21. An apparatus as defined in claim 20, wherein the utility estimator further comprises a hierarchical Bayes estimation model to calculate utility values from discrete choice responses received from a discrete choice exercise.

22. An apparatus as defined in claim 20, further comprising a ghost group generator to generate starter product groups based on a respondent ownership status of each of the plurality of market-available products.

23. An apparatus as defined in claim 22, further comprising a weight manager to assign one of a plurality of respondents a unity weight when one of the plurality of respondents owns a single one of the plurality of market-available products.

24. An apparatus as defined in claim 22, further comprising a weight manager to assign one of a plurality of respondents a relative partitioned weight when one of the plurality of respondents owns more than one of the plurality of market-available products.

25. An article of manufacture storing machine accessible instructions that, when executed, cause a machine to:

estimate discrete choice utility values for a plurality of respondents based on a plurality of market-available products;

divide the plurality of respondents into groups based on an ownership status of the plurality of market-available products;

identify a test starter product from the plurality of market-available products based on test criteria indicative of a degree of similarity with the new product;

generate a ghost group associated with the new product;

assign utility values of the test starter product to the new product in the ghost group;

tailor the utility values assigned to the new product with a ghost group utility adjustment rule; and

generate a ghost group model to represent consumers of the new product.

26. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to perform a hierarchical Bayes estimation on discrete choice responses received from a discrete choice exercise virtual shopping trip.

27. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to assign a first one of the plurality of respondents a unity weight when the first one of the plurality of respondents owns a single one of the plurality of market-available products.

28. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to assigning a relative partitioned weight to a first one of the plurality of respondents when the first one of the plurality of respondents owns more than one of the plurality of market-available products.

29. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to alter a sub-utility value within a threshold value to preserve a choice probability value.

30. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to select the ghost group utility adjustment rule based on invocation criteria comprising a threshold sub-utility value associated with the test starter product.

31. An article of manufacture as defined in claim 25, wherein the machine readable instructions, when executed, cause the machine to combine the tailored product utility values with the discrete choice utility values to create the ghost group model.

32. An article of manufacture as defined in claim 31, wherein the machine readable instructions, when executed, cause the machine to receive a simulation scenario to apply to the ghost group model.

33. An article of manufacture as defined in claim 32, wherein the machine readable instructions, when executed, cause the machine to calculate choice probability values for each of the plurality of respondents and for the ghost group associated with the new product based on the simulation scenario and the combined utility values in the ghost group model.

34. An article of manufacture as defined in claim 33, wherein the machine readable instructions, when executed, cause the machine to employ a multinomial logit model to calculate the choice probability values.

35. An article of manufacture as defined in claim 33, wherein the machine readable instructions, when executed, cause the machine to perform a respondent group weight adjustment for a number of iterations identified by the simulation scenario.

36. An article of manufacture as defined in claim 35, wherein the machine readable instructions, when executed, cause the machine to calculate at least one of a respondent weight decrease or a respondent weight increase based on the calculated choice probability values associated with each of the plurality of market-available products and the new product.

37. An article of manufacture as defined in claim 36, wherein the machine readable instructions, when executed, cause the machine to distribute the weight decrease or weight increase to each of the plurality of respondents and the plurality of ghost respondents.

38. An article of manufacture as defined in claim 36, wherein the machine readable instructions, when executed, cause the machine to calculate choice shares based on the calculated choice probability values, the combined utility values, and the at least one of the respondent weight decrease or weight increase.