Retailer optimization using market segmentation top quintile process

• US 7,885,851 B2
• Filed: 11/17/2006
• Issued: 02/08/2011
• Est. Priority Date: 11/17/2005
• Status: Active Grant

• Current Assignee: Scientific Games Corporation
• Original Assignee: Scientific Games International Incorporated (Scientific Games Corporation)
• Inventors: Hurt, John, Flinn, Michael Dax, O'Brien, Jim
• Primary Examiner(s): Abdi; Kambiz
• Assistant Examiner(s): RANKINS, WILLIAM E
• Application Number:
  US11/601,259
• Publication Number:
  US 20070112619A1
• Time in Patent Office: 1,544 Days
• Field of Search: 705/10
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A method for determining an optimal number of lottery retailers is disclosed. The optimal number of lottery retailers for a region can be determined based households of the same segmentations purchasing lottery products similarly, and there is a strong correlation between lottery agent density (population/retailers) and per capita lottery sales. The lottery agent density (LAD) necessary to produce high per capita sales can be identified and applied to all markets of that household segmentation.

Claims

1. A method for obtaining an optimal number of retailers in a geographical area, the method comprising the steps of:

obtaining demographical data for a plurality of regions, each region being identified by a region identifier, the demographical data containing population information, retailer count, sales figures, retailer density count, and per capita sales information, the demographical data being segmented into subgroups and inputting same into a computerized spreadsheet containing the region identifier and demographical data;

sorting the region identifiers in descending order based on the retailer count and omitting any region identifiers with zero retailer count;

for each subgroup;

sorting the remaining region identifiers via the electronic spreadsheet based on a population of the selected subgroup and further omitting any of the plurality of regions with a zero population for the subgroup;

sorting the remaining region identifiers based on per capita sales using the computerized spreadsheet;

determining a top percentage of regions from the resulting region identifiers based on the population information wherein the top percentage comprises selecting a percentage of the region identifiers for analysis after using the computerized spreadsheet to rank at least one of the demographical data categories of the subgroup,

determining an average sales figure for the top percentage of regions via the computerized spreadsheet,

determining an average retailer density count for the top percentage of regions using the computerized spreadsheet; and

computing via the computerized spreadsheet the optimal number of retailers based on the average sales figure and average density count.

2. The method of claim 1, further comprising the step of using the computerized spreadsheet to compute an expected sales figure for the optimal number of retailers.

3. The method of claim 1 wherein the top percentage comprises at least 20% of the subgroup.

4. The method of claim 1 wherein the percentage comprises at least between 40% and 60% of the subgroup.

5. The method of claim 1 wherein the retailer is a lottery retailer.

6. A method for obtaining an optimal number of retailers in a geographical area, the method comprising the steps of:

obtaining demographical data for a plurality of regions, each region being identified by a region identifier, the demographical data containing population information, retailer count, sales figures, retailer density count, and per capita sales information, the demographical data being segmented into subgroups and input into a computerized spreadsheet containing the region identifier and demographical data;

sorting the retailer count in descending order via the computerized spreadsheet and creating a sample by omitting any region identifier wherein the retailer count is zero;

for each subgroup in the sample;

using the computerized spreadsheet to sort population of the subgroup in descending order and further omitting any region identifiers wherein the population is zero;

ordering the per capita sales contained in the sample in descending order using the spreadsheet;

selecting a percentage of the remaining region identifiers for analysis using the computerized spreadsheet,

determining an average per capita sales figure via the computerized spreadsheet, and

determining an average retailer density count using the computerized spreadsheet; and

using the computerized spreadsheet to compute the optimal number of retailers based on the average sales figure and average density count.

7. The method of claim 6, further comprising the step of using the computerized spreadsheet to compute an expected sales figure for the optimal number of retailers.

8. The method of claim 6 wherein the percentage comprises 20% of the sample.

9. The method of claim 6 wherein the percentage comprises between 40% and 60% of the sample.