Part IV: Customer profiling and site selection with customer data

Getting to Know ESRI Business AnalystFred 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 scenarioIn 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 betterIncrease 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 centerRedefine the company’s trade areas using customer data, which will then be used toOpen 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 high-

value 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 customersUse a spatial join to attach demographic and Tapestry Segmentation attributes to customer features based on their locationUse summary tables to calculate geodemographic and Tapestry Segmentation lifestyle profiles of high-volume customersUse Tapestry Segmentation data with Market Potential Indexes to identify customer values, media habits, product preferences, and purchasing patternsUse 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 recordsProduce trade area penetration and distance decay reportsRank available new sites using Principal Components AnalysisEstimate 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 customersUse a spatial join to attach demographic and Tapestry Segmentation attributes to customer features based on their locationUse summary tables to calculate geodemographic and Tapestry Segmentation lifestyle profiles of high volume customersUse Tapestry Segmentation data with Market Potential Indexes to identify customer values, media habits, product preferences, and purchasing patternsUse this information to make product line and merchandising decisions appropriate for Living in the Green Lane’s best customersCreate sales-derived trade areas from customer recordsProduce trade area penetration and distance decay reportsRank available new sites using Principal Components Analysis Estimate market penetration and sales using Advanced Huff Model Analysis  

Evaluation of ROI for business GIS analysisThe 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 AnalystFred L. Miller, PhD

Murray State University