METHODS AND APPARATUS TO DETERMINE EFFECTS OF PROMOTIONAL ACTIVITY ON SALES

• US 20120303414A1
• Filed: 08/10/2012
• Published: 11/29/2012
• Est. Priority Date: 05/05/2009
• Status: Active Grant

• Current Assignee: Nielsen Consumer LLC (Nielsen Holdings plc)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Dodge, James, Donmyer, John, Slavik, Frank J.
• Granted Patent: US 8,583,477 B2
• Time in Patent Office: Days
• US Class Current: 705/7.31
• 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

Abstract

Example systems, methods, processes, and apparatus for determining expected base sales for a product include obtaining sales data for a product sold at a point of sale location. The sales data can be organized in a time series according to a predetermined time period. The method further includes identifying a promotional event for at least one of the product and the point of sale location and excluding sales data corresponding to the promotional event. The remaining sales data is processed using a smoothed moving average model involving a plurality of passes through the remaining sales data. Expected base data for the product is generated based on the smoothed moving average model and output to a user.

Claims

1. A method for product sales baseline determination, comprising:

correlating product sales data from a point of sale with causal data identifying at least one promotional event to exclude sales data corresponding to the at least one promotional event identified in the causal data to generate non-promoted sales data for a product, the sales data organized in a time series for a time period;

processing the non-promoted sales data using a double smoothed moving average model employing a plurality of passes through the non-promoted sales data to generate expected base data for the product from the double smoothed moving average model; and

outputting the expected base data for the product to a user.

2. A method according to claim 1, further comprising comparing expected base sales for the product to sales data corresponding to the promotional event to determine incremental sales for the product.

3. A method according to claim 1, wherein the double smoothed moving average model is applied to the non-promoted sales data by executing a backward pass through the non-promoted sales data, executing a forward pass through the non-promoted sales data, and averaging the backward and forward passes.

4. A method according to claim 3, further comprising removing outlying data points in the non-promoted sales data time series after averaging the backward pass and the forward pass.

5. A method according to claim 3, further comprising:

receiving updated non-promoted sales data for the product based on second sales data received for the product;

executing a backward pass and a forward pass through the updated sales data; and

averaging the results of the backward pass and the forward pass through the updated sales data for the product.

6. A method according to claim 3, wherein the double smoothed moving average model includes a smoothing constant to provide relative weight to newer sales data versus older sales data.

7. A method according to claim 6, wherein the smoothing constant assigns exponentially decreasing weights to older sales data values as the sales data becomes older in time.

8. A method according to claim 6, wherein the smoothing constant is determined based on a product category for the product.

9. A method according to claim 8, wherein the smoothing constant is determined based on at least one of a rate of sale and a quantity of sale for the product.

10. A method according to claim 1, wherein the time period comprises a week.

11. A method according to claim 1, wherein outputting the expected base data for the product comprises outputting the expected base data for the product by at least one of generating a visual depiction of the expected base data for display to the user and generating a machine-readable representation of the expected base data for further processing.

12. A method according to claim 1, further comprising testing the double smoothed moving average model with the non-promoted product sales data to validate the model for use with the non-promoted product sales data.

13. An apparatus for product sales baseline determination, comprising:

a data preparation and alignment engine to receive sales data for a product and causal data identifying a promotional event from a point of sale, the sales data organized in a time series for a time period, the data preparation and alignment engine to correlate the product sales data with the causal data to exclude sales data corresponding to the promotional event identified in the causal data to generate non-promoted sales data for the product; and

a modeling engine to process the non-promoted sales data using a smoothed moving average model involving a plurality of passes through the non-promoted sales data to generate expected base data for the product from the smoothed moving average model and outputting the expected base data for the product to a user.

14. An apparatus according to claim 13, wherein the modeling engine is to compare expected base sales for the product to sales data corresponding to the promotional event to determine incremental sales for the product.

15. An apparatus according to claim 13, wherein the modeling engine is to apply the smoothed moving average model to the non-promoted sales data by executing a backward pass through the non-promoted sales data, executing a forward pass through the non-promoted sales data, and averaging the backward and forward passes.

16. An apparatus according to claim 15, wherein the modeling engine is to calculate short and long sales from the non-promoted sales data based on the averaged backward and forward passes and ignores outlying short and long sales data points in the time series in the plurality of passes through the non-promoted sales data.

17. An apparatus according to claim 15, wherein the modeling engine is to receive updated non-promoted sales data for the product from the data preparation and alignment engine based on second sales data received for the product at the data preparation and alignment engine, the modeling engine to execute a backward pass and a forward pass through the updated sales data and then average the results of the backward pass and the forward pass through the updated sales data for the product.

18. An apparatus according to claim 15, wherein the smoothed moving average model includes a smoothing constant to provide relative higher weight to newer sales data and relative lower weight to older sales data.

19. An apparatus according to claim 18, wherein the smoothed moving average model comprises an exponentially smoothed moving average model wherein the smoothing constant assigns exponentially decreasing weights as the sales data becomes older in time.

20. An apparatus according to claim 19, wherein a double exponentially smoothed moving average model or a single exponentially smoothed moving average model is selected based on a determination of trend and seasonality in the time series data.

21. An apparatus according to claim 20, wherein the modeling engine is to output the expected base data for the product to a user by at least one of generating a visual depiction of the expected base data for display to the user and generating a machine-readable representation of the expected base data for further processing.

22. An apparatus according to claim 20, wherein the modeling engine is to test the smoothed moving average model with the non-promoted product sales data to validate the model for use with the non-promoted product sales data.