SALES PREDICATION FOR A NEW STORE BASED ON ON-SITE MARKET SURVEY DATA AND HIGH RESOLUTION GEOGRAPHICAL INFORMATION

• US 20120084118A1
• Filed: 09/30/2010
• Published: 04/05/2012
• Est. Priority Date: 09/30/2010
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bai, Xin Xin, Dong, Jin, Li, Ta-Hsin, Lv, Hai Rong, Yin, Wen Jun
• Application Number:
  US12/894,880
• Publication Number:
  US 20120084118A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A method for predicting sales for a new store in a certain geographical area is disclosed, the method comprising geographic and non-geographic information and customer segmentation in the area to estimate sales and optionally the impact on existing competitor stores.

Claims

1. A method of predicting sales for a new retail store to be located in a certain geographical area comprising:

a) identifying at least one customer segment in the certain geographic area associated with the new retail store;

b) generating a Consumer Demand Estimation Module for the new retail store comprising

(i) an Accessibility Model;

(ii) an Attractiveness Model;

(iii) a Customer Preference Model; and

(iv) a Demand Adjustment Factor; and

c) obtaining a Unit Demand for each customer segment from the Consumer Demand Estimation Module; and

d) providing the Unit Demand to a Sales Prediction Model that generates a prediction of sales for the new retail store.

2. The method of claim 1 wherein the customer segment includes any or all of the following in the certain geographical area:

residents, workers, shoppers.

3. The method of claim 1 wherein the Accessibility Model generates an accessibility score for the new retail store in the certain geographical area, the accessibility score based on information comprising road connectivity, topology of geographic road segments, cross roads, over passes, bridges, road direction, means of transportation, cost of transportation, the accessibility score being used to select the most probable route to the new store from a given customer segment.

4. The method of claim 1 wherein the Attractiveness Model generates an attractiveness score for the new retail store in the certain geographical area, the Attractiveness Model comprising a quantitative closed-loop feedback mechanism to adjust the attractiveness score, the attractiveness score based on information comprising store sales, store attribute data, and on-site shopper survey data.

5. The method of claim 4 wherein the store attribute data comprises store visibility, store size, store service level, store environment.

6. The method of claim 4 wherein the on-site shopper survey data comprises shopper feedback on store attractiveness.

7. The method of claim 1 wherein the Customer Preference Model comprises estimating the probability of selection by a particular customer segment to select a competing store over the new retail store in the certain geographical area based on the difference between the attractiveness and accessibility of the competing store and the new store.

8. The method of claim 1 wherein the Demand Adjustment Factor adjusts the final sales contribution to a store by discounting the store'"'"'s attractiveness, accessibility, store clustering effect, and probability of selection.

9. A computer-based method to predict sales for a new convenience retail outlet in a certain geographic area, comprising:

a) segmenting customers in the certain geographic area into Geographically Distributed Customer Segments (GDCS), the GDCS being selected from any or all of the following;

(i) residents in said geographic area

(ii) workers in said geographic area

(iii) shoppers in said geographic area

b) storing the GDCS in a Geographic Information System (GIS) platform;

c) dividing the certain geographical area into a grid system;

d) identifying at least one existing store in the grid system and obtaining customer information for the store, the customer information comprising sales attributable to a given customer in the existing store and the identification of the GDCS to which the given customer belongs;

e) providing an accessibility score from each GDCS in the certain geographical area to the new store and to at least one competing store in the certain geographical area, the accessibility score comprising information on road connectivity from each GDCS to the new store and to the at least one competing store, condition of the road connectivity, means of transportation from each GDCS to the new store and the at least one competing store, and cost of the means of transportation;

f) providing an attractiveness score for the new store and for at least one competing store in the geographical area, the attractiveness score comprising attractiveness information on the new store and the at least one competing store in the certain geographical area, the attractiveness information comprising;

visibility of the new store and the at least one competing store, size of the new store and the at least one competing store, service level at the new store and the at least one competing store, environment of the new store and the at least one competing store, and on-site shopper survey data on attractiveness at the new store and the at least one competing store;

g) generating a customer preference estimate, the customer preference estimate comprising the probability that a particular GDCS will select the new store and the at least one competing store;

h) generating a demand adjustment factor based on the accessibility score, the attractiveness score and the customer preference estimate; and

i) predicting the sales of the new store using the demand adjustment factor.