Part IV: Customer profiling and site selection
with customer data
Getting to Know ESRI Business Analyst
Fred L. Miller, PhD
Murray State University
Presentation topics
This presentation will cover:
 The decision scenario for Living in the Green Lane
 Relevant business GIS tools and tasks
 Chapter 6: Building a profile of distinctive customer
characteristics
 Geocode customer data with locator services
 Use Layer Properties to view attribute distribution and identify high-volume customers
 Use a spatial join to attach demographic and Tapestry Segmentation attributes to customer
features based on their location
 Use summary tables to calculate geodemographic and Tapestry Segmentation lifestyle
profiles of high volume customers
 Use Tapestry Segmentation data with Market Potential Indexes to identify customer
values, media habits, product preferences, and purchasing patterns
 Use this information to make product line and merchandising decisions appropriate for
Living in the Green Lane’s best customers
Presentation topics (cont.)
 Chapter 7: Customer-based trade area analysis and site
selection




Create sales-derived trade areas from customer records
Produce trade area penetration and distance decay reports
Rank available new sites using Principal Components Analysis a
Estimate market penetration and sales using Advanced Huff Model Analysis
 Evaluation of ROI for business GIS analysis
 Business GIS learning goals and skills
LITGL decision scenario
In its first two years, Living in the Green Lane has been
very successful. Now, Janice and Steven wish to:
 Study the purchasing patterns of Living Green loyalty
club members to serve them better
 Increase sales at the existing store by expanding their
product line,
 Broaden LITGL’s concept to become a green lifestyle
center, not simply a green home center
 Redefine the company’s trade areas using customer
data, which will then be used to
 Open two additional stores at attractive locations in
the Twin Cities area
Relevant business GIS tools and tasks
 Mapping customer location with geocoding
 Identifying and mapping customer purchase segments
 Attaching demographic and lifestyle attributes to
customer records based on location
 Creating demographic and lifestyle profiles of highvalue customers and using them to improve product line
 Deriving trade areas from customer purchase data and
calculate penetration and distance decay measures
 Evaluating locations for new stores based on trade area
characteristics and projected sales volume
Chapter 6: Building a profile of
distinctive customer characteristics
In this chapter, you will perform the following Business
Analyst Desktop tasks:
 Geocode customer data with locator services and symbolize it on a map
 Use Layer Properties to view attribute distribution and identify high volume




customers
Use a spatial join to attach demographic and Tapestry Segmentation
attributes to customer features based on their location
Use summary tables to calculate geodemographic and Tapestry
Segmentation lifestyle profiles of high-volume customers
Use Tapestry Segmentation data with Market Potential Indexes to identify
customer values, media habits, product preferences, and purchasing patterns
Use this information to make product line and merchandising decisions
appropriate for Living in the Green Lane’s best customers
Geocode customer data and define
segments by purchase volume
Spatially join demographic and tapestry values to
customer records, create summary tables
Create demographic and lifestyle profiles of High Purchase Segment,
Use profile information to craft marketing strategies
Chapter 7: Customer-based trade area
analysis and site selection
In this chapter, you will perform the following Business
Analyst Desktop tasks:
 Create sales-derived trade areas from customer records
 Produce trade area penetration and distance decay reports
 Rank available new sites using Principal Components Analysis
 Estimate market penetration and sales using Advanced Huff
Model Analysis
Create Customer-Derived Trade Areas,
Penetration and Distance Decay Reports
Rank potential sites with Principal Components Analysis
Project site sales volume with Advanced Huff Model Analysis
Business GIS learning goals and skills
In Part IV, you will learn to use Business Analyst Desktop
to:
 Geocode customer data with locator services
 Use Layer Properties to view attribute distribution and identify high volume customers
 Use a spatial join to attach demographic and Tapestry Segmentation attributes to







customer features based on their location
Use summary tables to calculate geodemographic and Tapestry Segmentation lifestyle
profiles of high volume customers
Use Tapestry Segmentation data with Market Potential Indexes to identify customer
values, media habits, product preferences, and purchasing patterns
Use this information to make product line and merchandising decisions appropriate for
Living in the Green Lane’s best customers
Create sales-derived trade areas from customer records
Produce trade area penetration and distance decay reports
Rank available new sites using Principal Components Analysis
Estimate market penetration and sales using Advanced Huff Model Analysis
Evaluation of ROI for business GIS analysis
The costs of this Business Analyst application are:
 A Business Analyst Desktop and Segmentation Module
 The time of managers and business GIS analyst
The benefits of this Business Analyst application are:
 Increased revenue from higher sales at existing stores
 Optimized projected sales from second and third
locations
The estimated incremental revenues are:
 About $80,000 in increased purchases from High
Segment customers in existing store
 Financial benefits for second and third stores
approximating those for first store
Part IV: Customer profiling and site selection
with customer data
Getting to Know ESRI Business Analyst
Fred L. Miller, PhD
Murray State University