METHODS AND APPARATUS TO DYNAMICALLY ESTIMATE CONSUMER SEGMENT SALES WITH POINT-OF-SALE DATA

1 . A method to estimate segment purchases, comprising:

generating a dataset of observed category panelist trips for a segment of interest;

identifying a first signal variable associated with non-panelist data for a time period of interest;

calculating a trip likelihood for the segment of interest based on the first signal variable; and

estimating a decomposition of purchases by segment based on the trip likelihood and the non-panelist data.

2 . A method as defined in claim 1 , wherein the non-panelist data comprises point-of-sale (POS) data.

3 . A method as defined in claim 2 , wherein the POS data comprises retail product scanner data.

4 . A method as defined in claim 1 , further comprising calculating a posterior for a first brand of interest based on the trip likelihood, the posterior to proportionally scale the non-panelist data for the segment of interest.

5 . A method as defined in claim 1 , wherein estimating comprises applying a Bayesian analysis to calculate a posterior based on one or more prior estimates.

6 . A method as defined in claim 1 , wherein the time period of interest comprises a store-week.

7 . A method as defined in claim 1 , further comprising identifying a second signal variable associated with a matching time period of interest to generate a signature of a trading area of interest.

8 . A method as defined in claim 1 , wherein the first signal variable comprises at least one of promotion data, incremental sales data, baseline sales data, temperature data or trading area characteristic data.

9 . A method as defined in claim 1 , wherein the trip likelihood is calculated based on a Gaussian model.

10 . A method as defined in claim 1 , further comprising applying a multivariate likelihood model to calculate a trip likelihood for a plurality of signal variables of interest.

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

calculating an average signal variable value for each one of a plurality of segments of interest;

calculating a z-score for each data point based on the average signal variable value associated with each corresponding segment from the plurality of segments of interest;

calculating an average z-score for each segment of interest based on a store signal variable value; and

calculating the trip likelihood based on the store signal variable value and the average z-score for one of the plurality of segments of interest.

12 . A method as defined in claim 11 , wherein the store signal variable comprises a temperature value during a store-week of interest.

13 . An apparatus to estimate segment purchases, comprising:

a panelist data interface to generate a dataset of observed category panelist trips for a segment of interest;

a signal variable manager to identify a first signal variable associated with non-panelist data for a time period of interest;

a likelihood function engine to calculate a trip likelihood for the segment of interest based on the first signal variable; and

a decomposition engine to estimate a decomposition of purchases by segment based on the trip likelihood and the non-panelist data.

14 . An apparatus as defined in claim 13 , wherein the non-panelist data comprises point-of-sale (POS) data.

15 . An apparatus as defined in claim 14 , wherein the POS data comprises retail product scanner data.

16 . An apparatus as defined in claim 13 further comprising a probability engine to calculate a posterior for a first brand of interest based on the trip likelihood, the posterior to proportionally scale the non-panelist data for the segment of interest.

17 . An apparatus as defined in claim 13 , wherein the probability engine employs a Bayesian model to calculate a posterior based on one or more prior estimates.

18 . An apparatus as defined in claim 13 , wherein the signal variable manager identifies a second signal variable associated with a matching time period of interest to generate a signature of a trading area of interest.

19 . An apparatus as defined in claim 13 , further comprising a probability engine to apply a Gaussian model to calculate the trip likelihood.

20 . A tangible machine readable storage medium comprising instructions stored thereon that, when executed, cause a machine to, at least:

generate a dataset of observed category panelist trips for a segment of interest;

identify a first signal variable associated with non-panelist data for a time period of interest;

calculate a trip likelihood for the segment of interest based on the first signal variable; and

estimate a decomposition of purchases by segment based on the trip likelihood and the non-panelist data.

21 . A machine readable storage medium as defined in claim 20 , wherein the instructions, when executed, cause the machine to calculate a posterior for a first brand of interest based on the trip likelihood, the posterior to proportionally scale the non-panelist data for the segment of interest.

22 . A machine readable storage medium as defined in claim 20 , wherein the instructions, when executed, cause the machine to apply a Bayesian analysis to calculate a posterior based on one or more prior estimates.

23 . A machine readable storage medium as defined in claim 20 , wherein the instructions, when executed, cause the machine to identify a second signal variable associated with a matching time period of interest to generate a signature of a trading area of interest.

24 . A machine readable storage medium as defined in claim 20 , wherein the instructions, when executed, cause the machine to apply a multivariate likelihood model to calculate a trip likelihood for a plurality of signal variables of interest.

25 . A machine readable storage medium as defined in claim 24 , wherein the instructions, when executed, cause the machine to:

calculate an average signal variable value for each one of a plurality of segments of interest;

calculate a z-score for each data point based on the average signal variable value associated with each corresponding segment from the plurality of segments of interest;

calculate an average z-score for each segment of interest based on a store signal variable value; and

calculate the trip likelihood based on the store signal variable value and the average z-score for one of the plurality of segments of interest.

26 . A method to reduce likelihood calculation errors in a multivariate dataset, comprising:

transforming the multivariate dataset from a correlated space to an uncorrelated space;

identifying a plurality of segments associated with the dataset in the uncorrelated space;

calculating an average of signal variable values associated with each one of the plurality of segments;

calculating difference values for the signal variable values for each one of the plurality of segments; and

calculating a segment likelihood based on one of the signal variable values and the difference values for each segment of the plurality of segments.

27 . A method as defined in claim 26 , wherein calculating difference values comprises calculating z-scores.