Predicting shopper traffic at a retail store

• US 8,140,379 B2
• Filed: 07/01/2011
• Issued: 03/20/2012
• Est. Priority Date: 09/08/2006
• Status: Active Grant

• Current Assignee: The Procter & Gamble Company
• Original Assignee: The Procter & Gamble Company
• Inventors: Anand, Jayaraman, Young, Kathryn Byrd, Choudhary, Ranjit Kumar, Howell, Dina R.
• Primary Examiner(s): DIXON, THOMAS A
• Application Number:
  US13/174,806
• Publication Number:
  US 20110264492A1
• Time in Patent Office: 263 Days
• Field of Search: 705/7.31
• US Class Current: 705/7.31
• 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 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06V 20/53 Recognition of crowd images, e.g. recognition of crowd congestion
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats

Abstract

Mathematical models for predicting shopper traffic at a shopper region useful for developing inter alia a metric for measuring impressions to in-store advertising.

Claims

1. A method of predicting impressions at a target store comprising the steps:

(a) obtaining actual market data from the target store for a period of time,

wherein the target store comprises a first shopper region,

wherein the first shopper region comprises a first product category and an in-store advertisement advertising a product during the duration of the period of time,

wherein the actual market data comprises category purchase data from the first product category;

(b) inputting the actual market data from the target store in an analysis program executable on a computer,

wherein the analysis program comprises a mathematical model for predicting traffic;

wherein the mathematical model is developed from;

(i) actual shopper traffic data at a first shopper region from at least ten retail stores, wherein the first shopper region comprises at least a first product category;

(ii) actual market data from said ten retail stores, wherein the market data comprises category purchase data from the first product category; and

(c) executing the analysis program to predict impressions at the first shopper region of the target store.

2. The method of claim 1, further comprising the step of normalizing the mathematical model for:

(i) the period time from which the actual market data from the target store is obtained and;

(ii) the first product category.

3. The method of claim 2, wherein the market data from the target store further comprises at least two of the following:

store purchase data, store data, temporal data, demographic data, or combinations thereof.

4. The method of claim 3, wherein market data from the target store further comprises the following:

store purchase data, store data, temporal data, and demographic data; and

wherein the first category purchase data further comprises;

category items, category baskets, category dollars, and category percentage of store dollars; and

wherein the product which the in-store advertisement advertising is localized in the first shopper region or a second shopper region.