METHODS AND APPARATUS TO DETERMINE EFFECTS OF PROMOTIONAL ACTIVITY ON SALES

• US 20100287029A1
• Filed: 05/05/2009
• Published: 11/11/2010
• 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 8,265,989 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 computer-implemented method of determining expected base sales for a product, comprising:

obtaining sales data for a product sold at a point of sale location, the sales data organized in a time series over a time period;

identifying a promotional event for at least one of the product and the point of sale location;

excluding sales data corresponding to the promotional event from the sales data to form remaining sales data;

processing the remaining sales data using a smoothed moving average model involving a plurality of passes through the remaining sales data;

generating expected base data for the product based on the smoothed moving average model; and

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

2. A method according to claim 1, wherein identifying a promotional event further comprises:

obtaining causal data relating to a promotional event at the point of sale location; and

correlating the causal data and the sales data.

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

4. A method according to claim 1, wherein processing the remaining sales data using the double smoothed moving average model further comprises:

executing a backward pass through the remaining sales data;

executing a forward pass through the remaining sales data; and

averaging the backward pass and the forward pass.

5. A method according to claim 4, further comprising calculating short and long sales from the remaining sales data based on the averaged backward and forward passes and ignoring outlying short and long sales data points in the time series in the plurality of passes through the remaining sales data.

6. A method according to claim 4, further comprising

updating the remaining sales data for the product based on second sales data received for the product to provide updated sales data for the product;

executing a backward pass through the updated sales data;

executing a forward pass through the updated sales data; and

averaging the backward pass and the forward pass.

7. A method according to claim 1, wherein processing the remaining sales data using the smoothed moving average model comprises employing a smoothing constant to provide relative higher weight to newer sales data and relative lower weight to older sales data.

8. A method according to claim 7, wherein the smoothed moving average model comprises a double exponentially smoothed moving average model wherein the smoothing constant assigns exponentially decreasing weights as the sales data becomes older in time.

9. A method according to claim 7, 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.

10. A method according to claim 1, wherein outputting the expected base data for the product to a user further comprises 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.

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

a data preparation and alignment engine receiving 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, and correlating 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 processing 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.

12. An apparatus according to claim 11, wherein the modeling engine compares expected base sales for the product to sales data corresponding to the promotional event to determine incremental sales for the product.

13. An apparatus according to claim 11, wherein the modeling engine applies 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.

14. An apparatus according to claim 13, wherein the modeling engine calculates 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.

15. An apparatus according to claim 13, wherein the modeling engine further receives 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, executes a backward pass and a forward pass through the updated sales data and then averages the results of the backward pass and the forward pass through the updated sales data for the product.

16. An apparatus according to claim 13, wherein the smoothed moving average model includes use of a smoothing constant to provide relative higher weight to newer sales data and relative lower weight to older sales data.

17. An apparatus according to claim 16, wherein the smoothed moving average model comprises a double exponentially smoothed moving average model wherein the smoothing constant assigns exponentially decreasing weights as the sales data becomes older in time.

18. An apparatus according to claim 16, 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.

19. An apparatus according to claim 18, wherein the modeling engine outputs 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.

20. An apparatus according to claim 18, wherein the modeling engine tests 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.

21. A tangible machine-readable medium including a program which, when executed, causes a machine to:

obtain sales data for a product sold at a point of sale location, the sales data organized in a time series for a time period;

identify 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;

process the remaining sales data using a smoothed moving average model involving a plurality of passes through the remaining sales data;

generate expected base data for the product based on the smoothed moving average model; and

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

22. A process for determining expected base sales for a sold product, comprising:

obtaining sales data for a product sold at a point of sale location, the sales data organized in a time series according to a time period;

obtaining causal data identifying a promotional event for at least one of the product and the point of sale location;

excluding sales data corresponding to the promotional event to determine a time series of non-promoted sales data for the product;

processing the non-promoted sales data using a double exponentially smoothed moving average model including a smoothing constant that assigns exponentially decreasing weights to older sales data values as the sales data becomes older in time, the processing comprising;

executing a preliminary backward pass through the remaining sales data;

executing a preliminary forward pass through the remaining sales data;

averaging the preliminary backward pass and the preliminary forward pass;

updating the remaining sales data for the product based on second sales data for an additional time period received for the product to provide updated sales data for the product;

executing an updated backward pass through the updated sales data;

executing an updated forward pass through the updated sales data; and

averaging the updated backward pass and the forward pass;

generating expected base data for the product based on the double exponentially smoothed moving average model; and

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

23-85. -85. (canceled)