METHODS AND APPARATUS TO MODEL CONSUMER AWARENESS FOR CHANGING PRODUCTS IN A CONSUMER PURCHASE MODEL

• US 20100205034A1
• Filed: 02/09/2009
• Published: 08/12/2010
• Est. Priority Date: 02/09/2009
• Status: Abandoned Application

• Current Assignee: Nielsen Company (US), LLC
• Original Assignee: Nielsen Company (US), LLC
• Inventors: Fix, John P., Noble, Steven S., Zimmerman, William Kelly, Smith, Brian Robert II
• Application Number:
  US12/368,028
• Publication Number:
  US 20100205034A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors

Abstract

Example methods and apparatus to model consumer awareness for changing products in a consumer purchase model are disclosed. A disclosed example method includes receiving utility values associated with at least one of a product or a product attribute, and identifying an agent awareness state associated with the restage product and the original product. The example method also includes calculating a choice probability for the restage product based on the estimated utility values associated with the identified awareness state, and outputting the choice probability for use in a simulation of consumer purchase.

Claims

1. A computer implemented method to model consumer purchase decisions for restage products, comprising:

receiving utility values associated with at least one of a product or a product attribute;

identifying an agent awareness state associated with the restage product and the original product;

calculating a choice probability for the restage product based on the estimated utility values associated with the identified awareness state; and

outputting the choice probability for use in a simulation of consumer purchase.

2. A method as defined in claim 1, wherein receiving the utility values further comprises retrieving respondent choice data from a panel of respondents participating in a discrete choice study.

3. A method as defined in claim 2, wherein receiving the utility values further comprises applying the respondent choice data to a hierarchical Bayes model.

4. A method as defined in claim 2, further comprising projecting the utility values of the panel of respondents to a set of agents to participate in at least one consumer purchase simulation.

5. A method as defined in claim 4, further comprising initializing each agent within the set of agents with at least one utility set of values.

6. A method as defined in claim 4, further comprising initializing each agent within the set of agents with at least one purchasing rule.

7. A method as defined in claim 6, wherein the at least one purchasing rule comprises at least one of a shopping frequency or an awareness state.

8. A method as defined in claim 4, further comprising generating at least one product consideration set available to the set of agents during the at least one purchasing simulation.

9. A method as defined in claim 4, further comprising injecting at least one of advertising attributes, price attributes, product availability attributes, or promotional attributes to the at least one purchasing simulation.

10. A method as defined in claim 4, wherein calculating the choice probability further comprises applying the received utility values to a multinomial logit model.

11. A method as defined in claim 10, further comprising simulating consumer purchase decisions based on the choice probability and the received utility values associated with the agent awareness state.

12. A method as defined in claim 11, wherein simulating consumer purchase decisions further comprises applying the received utility values and the choice probability to an agent based model.

13. A method as defined in claim 11, further comprising calculating an emergent pattern based on a plurality of product and restage product sets available to the set of agents in the at least one purchasing simulation.

14. A method as defined in claim 1, wherein identifying the agent awareness state further comprises identifying an unaware state when the agent is unaware of the original product and unaware of the restage product.

15. A method as defined in claim 1, wherein identifying the agent awareness state further comprises identifying a state indicative of agent awareness of the original product and unawareness of the restage product.

16. A method as defined in claim 15, wherein calculating the choice probability further comprises applying the received utility value associated with the original product to a pattern model when the agent is aware of the original product and unaware of the restage product.

17. A method as defined in claim 1, wherein identifying the agent awareness state further comprises identifying a state indicative of agent awareness of the restage product and the original product.

18. A method as defined in claim 17, wherein calculating the choice probability further comprises applying the received utility value associated with the original product and the restage product to a pattern model when the agent is aware of the original product and the restage product.

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24. An apparatus to model a restage product, comprising:

a utility estimator to estimate utility values associated with an original product and a restage change;

an awareness manager to identify a respondent awareness state of a plurality of agents associated with the restage product and the original product; and

a relative probability calculator to calculate a choice probability value for the restage product and the original product based on the calculated utility values associated with the respondent awareness state.

25. An apparatus as defined in claim 24, further comprising a discrete choice exercise engine to obtain a plurality of respondent choice decisions, the choice decisions provided to the utility estimator to estimate the utility values.

26. An apparatus as defined in claim 24, further comprising an agent manager to project the plurality of respondents to a plurality of agents to participate in at least one purchasing simulation.

27. An apparatus as defined in claim 26, further comprising a simulation framework manager to initialize each of the plurality of agents with at least one of a purchasing rule or an awareness state.

28. An apparatus as defined in claim 26, further comprising a consumer purchase simulator to generate at least one product consideration set available to the plurality of agents.

29. An apparatus as defined in claim 26, further comprising a simulation framework manager to simulate consumer purchase decisions based on the choice probability value and the agent awareness state.

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

receive utility values associated with at least one of a product or a product attribute;

identify an agent awareness state associated with the restage product and the original product;

calculate a choice probability for the restage product based on the estimated utility values associated with the identified awareness state; and

output the choice probability for use in a simulation of consumer purchase.

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