METHODS AND APPARATUS TO FACILITATE SALES ESTIMATES

• US 20080262900A1
• Filed: 03/14/2008
• Published: 10/23/2008
• Est. Priority Date: 04/18/2007
• Status: Abandoned Application

• Current Assignee: Nielsen Company (US), LLC
• Original Assignee: Nielsen Company (US), LLC
• Inventors: Duffy, Michael Day, Bronnenberg, Bart, Bock, Robert
• Application Number:
  US12/049,030
• Publication Number:
  US 20080262900A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

Methods and apparatus to facilitate sales estimates are disclosed. An example method includes compiling, in a market intelligence database, point of sale (POS) data collected at stores using a first data collection system, compiling, in a market intelligence database, consumer purchase data collected from panelists using a second data collection system, compiling, in a market intelligence database, geographically informed demographic data collected with a third data collection system, and compiling, in a market intelligence database, store characteristic data collected with a fourth data system in a market. The example method also includes organizing at least a subset of the POS data, the consumer purchase data, the geographically informed demographic data, or the store characteristic data into a first multi-dimensional volume of cells. Additionally, each cell corresponds to at least one store associated with at least one channel and the cells are arranged in the first volume based on their relative similarities with respect to a first characteristic of interest.

Claims

1. A method comprising:

generating a first data structure to store market data, the first data structure comprising a first plurality of cells, each of the plurality of cells being associated with a store;

identifying a second plurality of cells within the first plurality of cells that are associated with a channel of interest; and

placing a representation of the second plurality of cells in a cohort data structure, the second plurality of cells within the cohort data structure being arranged based on relative similarities between the stores in the second plurality of cells with respect to a characteristic of interest.

2. A method as defined in claim 1, further comprising populating a portion of the second plurality of cells with point of sale (POS) data.

3. A method as defined in claim 2, wherein the POS data is at least partially based on consumer panelist data.

4. A method as defined in claim 3, further comprising calculating a marginal based on the consumer panelist data.

5. A method as defined in claim 2, further comprising calculating a marginal based on the POS data.

6. A method as defined in claim 2, wherein the POS data is at least partially based on store-provided data.

7. A method as defined in claim 6, further comprising calculating a first marginal value based on consumer panelist data and a second marginal value based on data collected at stores.

8. A method as defined in claim 7, further comprising calculating a difference score between the first and second marginal values.

9. A method as defined in claim 8, further comprising estimating at least one of brand share or category mix for a subset of the first plurality of cells based on the difference score.

10. A method as defined in claim 8, further comprising:

calculating an average of the first and second marginal values; and

assigning a weight to the consumer panelist data in the second plurality of cells, the weight based on the average of the first and second marginal values.

11. A method as defined in claim 1, wherein the channel of interest comprises at least one of a store channel or a store sub-channel.

12. A method as defined in claim 11, wherein the store channel comprises at least one of a wholesale club store, a liquor store, a drug store, a cigarette outlet, a grocery store, a specialty store, a convenience store, or a mass merchandiser.

13. A method as defined in claim 1, wherein the characteristic of interest comprises at least one of a number of stores in a chain of stores, a number of employees at a store, a store geographic location, a channel service by the store, a volume of product sold at a store, or a volume of a brand sold at a store.

14-18. -18. (canceled)

19. An apparatus to determine sales estimates comprising:

a market intelligence database to store data indicative of a plurality of merchants; and

a cohort system to develop at least one spatial cohort based on the data.

20. An apparatus as defined in claim 19, further comprising a spatial modeling engine to apply at least one spatial modeling technique to a subset of the data to develop the at least one spatial cohort.

21. An apparatus as defined in claim 19, further comprising a cohort reference manager to populate the at least one spatial cohort with point of sale data.

22. An apparatus as defined in claim 19, further comprising a cohort panelist manager to populate the at least one spatial cohort with household panelist data.

23. An apparatus as defined in claim 19, further comprising a definition manager to retrieve the data indicative of the plurality of merchants from at least one market intelligence source.

24. An apparatus as defined in claim 23, wherein the at least one market intelligence source comprises at least one of a panelist-based measurement data source, a demographic indicator data source, a market segmentation data source, a merchant characteristic data source, or a point of sale data source.

25-30. -30. (canceled)

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

generate a first data structure to store market data, the first data structure comprising a first plurality of cells, each of the plurality of cells being associated with a store;

identify a second plurality of cells within the first plurality of cells that are associated with a channel of interest; and

place a representation of the second plurality of cells in a cohort data structure, the second plurality of cells within the cohort data structure being arranged based on relative similarities between the stores in the second plurality of cells with respect to a characteristic of interest.

32. An article of manufacture as defined in claim 31, wherein the machine accessible instructions further cause the machine to populate a portion of the second plurality of cells with point of sale (POS) data.

33. An article of manufacture as defined in claim 32, wherein the machine accessible instructions further cause the machine to calculate a marginal based on consumer panelist data.

34. An article of manufacture as defined in claim 32, wherein the machine accessible instructions further cause the machine to calculate a marginal based on the POS data.

35. An article of manufacture as defined in claim 32, wherein the machine accessible instructions further cause the machine to calculate a first marginal value based on consumer panelist data and a second marginal value based on data collected at stores.

36. An article of manufacture as defined in claim 35, wherein the machine accessible instructions further cause the machine to calculate a difference score between the first and second marginal values.

37. An article of manufacture as defined in claim 36, wherein the machine accessible instructions further cause the machine to estimate at least one of brand share or category mix for a subset of the first plurality of cells based on the difference score.

38. An article of manufacture as defined in claim 36, wherein the machine accessible instructions further cause the machine to:

calculate an average of the first and second marginal values; and

assign a weight to the consumer panelist data in the second plurality of cells, the weight based on the average of the first and second marginal values.

39-48. -48. (canceled)