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Teaching notes GETTING TO KNOW ESRI BUSINESS ANALYST 1

Getting to Know ESRI Business Analyst Fred L. Miller, PhDHutchens Distinguished Professor of Marketing and Business GISMurray State University

Teaching NotesThe purpose of this document is to help you use Getting to Know ESRI Business Analyst more effectively in a teaching environment. To supplement the Microsoft PowerPoint presentations and sample syllabus that accompany the book, this set of teaching notes offers suggestions and tips for creating a successful learning experience for students. It includes general observations about the structure of the book and its chapters as well as specific discussion of each chapter and its contents.

The structure of Getting to Know ESRI Business AnalystAs a narrative, the book tells the story of Living in the Green Lane, an entrepreneurial enterprise that has its roots in a green-home building blog and grows into a national chain of company owned and franchised green-lifestyle centers. At each stage of the growth process, the company’s owners, Janice Brown and Steven Bent, and you, the company’s business GIS manager, use the resources of the ESRI Business Analyst suite of products to perform integrated business GIS analyses to build the company’s success one step at a time.

As a software teaching tool, the book develops the reader’s skills with Business Analyst in a systematic progression. As Living in the Green Lane grows, its use of business GIS becomes increasingly sophisticated and its reliance upon Business Analyst tools more pronounced. Thus, as the reader works through each chapter, his/her understanding of the capabilities and applications of Business Analyst resources becomes deeper and his/her business GIS skills more developed.

As a learning tool for business decision making, the book merges these two streams of development to illustrate the role of business GIS in framing, exploring, and answering core spatial decisions in the development of the enterprise. Where are the most attractive opportunities? How strong is the competitive environment in an area? The retail attraction of that area? Where are my customers? What are they like? How do they behave? Which products do they buy and why? Where can I find more prospective customers like them?

Assigned the role of LITGL’s business GIS manager, readers answer all these questions and more. And they do so in a setting that portrays the value of business GIS not solely to large companies with substantial information technol-ogy systems, but also to smaller, entrepreneurial enterprises and the economic development units that nurture and support them.

As an instructional tool in a business, geography, and/or information systems class, Getting to Know ESRI Business Analyst supports a relevant, active learning approach while also providing flexibility to achieve the objectives of indi-vidual courses. Although the book’s chapters progress systematically through the suite of Business Analyst products, each chapter is operationally independent, containing its own data, mapping, and analytical resources. Thus, instructors may choose the chapters most relevant, for example, to courses in entrepreneurship, retail management, business projects geography, or spatially oriented information systems.

While the sample syllabus supports full, sequential coverage of the book in a pure integrated business GIS course, it could also be adapted to any of the settings just mentioned or other scenarios required by instructors.

GETTING TO KNOW ESRI BUSINESS ANALYST Teaching notes2

Getting to Know ESRI Business Analyst parts and chaptersGetting to Know ESRI Business Analyst is organized by parts and chapters. Each part includes an integrated set of business decisions focusing on a particular decision point or scenario in the life of the enterprise. More focused parts contain one chapter; more expansive ones contain two chapters.

The parts share a common structure which places the detailed Business Analyst procedures in each chapter in a clear organizational setting. Thus, they create the context in which business GIS analysis is conducted in the enterprise, discuss the core concepts of that analysis, and assess the relevant costs and benefits of that analysis in return on invest-ment (ROI) terms. Toward this end, each part contains:1. An Executive Summary that describes the business situation, analytical requirements, business GIS tools, and

relevant cost/benefit considerations2. A summary of the relevant scenario in the development of the Living in the Green Lane enterprise3. A discussion of the business GIS tools most appropriate for the analysis required by the company at this stage

in its life4. One or two chapters in which readers perform the required analysis using Business Analyst resources, each of which

contains:a. explanations of the Business Analyst resources most appropriate for the decision required by the scenariob. detailed, step-by-step instructions for performing the necessary Business Analyst procedures c. guidance for interpreting the results of these procedures and applying them appropriately to the relevant

business decision5. An explanation of the costs incurred by the enterprise to perform the required analysis and the benefits that it realizes

in doing so, in essence the ROI calculation for the procedures in the chapters as they apply to the decisions the company must make at this stage in its development

6. An inventory of the business GIS concepts readers have learned in this part of the book and the specific Business Analyst skills they have added to their skill set

This structure provides a systematic framework that helps students learn new Business Analyst skills and, at the same time, understand the value and appropriate application of those skills in a variety of business settings, from entrepreneur-ial start-up to national retail operation.

Teaching notes for part I: Trade-area analysis and site reporting with ESRI Business Analyst OnlineThese materials cover Business Analyst Online as it is encountered by Living in the Green Lane owners Janice Brown and Steven Bent through the services of an economic development agency in the Minneapolis-St. Paul area. The Twin Cities Redevelopment Task Force (TCRTF) uses its Business Analyst Online subscription to help local entrepreneurs understand the opportunities in the area and exploit them by renovating and revitalizing existing facilities. Armed with a description of their target market segments, Janice and Steven will explore the attractiveness of the existing TCRTF site.

The core business decision here is the suitability of the site available through TCRTF for Living in the Green Lane’s first store. To make this decision, students examine the characteristics of the Twin Cities area and try to find other sites that better fit the target market identified by Janice and Steven.

Chapter 1: Mapping the business environment: population and potential site characteristicsThe business GIS resources most appropriate for this task are:1. Thematic mapping to understand the distribution of key characteristics of the Twin Cities area2. Market area creation to find the best market area definition for the enterprise3. Market area profiling to ascertain the characteristics of alternative market areas4. Business environment analysis to understand the competitive environment of alternative market areas


Students will use Business Analyst Online resources to perform these tasks. Specifically, they will:1. Create a study area in the Minneapolis-St. Paul CBSA to focus their tasks2. Perform thematic mapping with several data attributes at different levels of geography in the Twin Cities area,

looking for concentrations of households with target segment characteristics3. Geocode the TCRTF site’s address and locate it on a map, then create simple ring and drive-time market areas

around it4. Use thematic maps to prospect for an attractive alternate site in the Twin Cities area, place it on the map and create

drive-time market areas around it5. Compare the two sites with the Compare Sites tool as well as standard reports from the Business Analyst Online

collection 6. Use the Business Summary report to assess the competitive environment around each site 7. Determine whether Janice or Steven should acquire the TCRTF site for their first store

Teaching suggestions1. Be sure students understand that they are not selecting the best site. There may be no actual site available for lease

or purchase in the area they select. They are simply charged with determining whether the TCRTF site should be purchased.

2. The TCRTF site is not well located (and we have almost a whole book to go) so the desired recommendation is NOT to purchase the site as it is disadvantaged relative to several other locations in the Twin Cities area.

3. The TCRTF site offers a good opportunity to comment on the relative advantages of simple ring and drive-time trade areas, as they differ significantly for this site.

4. The thematic mapping and report functions students perform are but a sample of the capabilities available in Business Analyst Online. Encourage them to explore more attributes for thematic mapping and more report formats to expand their understanding of the capabilities of Business Analyst Online.

5. Especially encourage students to perform Customized analysis in the Create Comparison Reports tool. Customization allows students to develop tables and graphs based on exactly the data attributes they deem most significant and save the results for future use.

Beyond the basics 1. Consider using Business Analyst Online to support course research projects. If your lab is based on the data in this

book or the Business Analyst Educational Lab Pack, the Business Analyst Online data is the broadest available to you. Encourage students to use it to explore your local business environment or to perform environmental scanning and/or market area analysis for organizational projects as part of your course.

2. To extend your access to Business Analyst Online, consider an educational subscription to the service. This will allow you continuous access to a substantial range of reports with national coverage and annually updated data. You will also have access to new tools as they become available as well as the most recent available data to support class projects.

Teaching notes for part II: Business environment analysis with ESRI Business Analyst DesktopIn this part of the book students assume the role of business GIS analyst for Living in the Green Lane. Owners Janice Brown and Steven Bent have created this role and acquired Business Analyst Desktop after experiencing the power of Business Analyst Online through the Twin Cities Redevelopment Task Force. In this new role, students face the task of helping Janice and Steven craft an effective business plan for their new enterprise. To attract investors, the plan must present a comprehensive, insightful analysis of the business environment for the new enterprise along with recommenda-tions for an attractive site for the first store. This chapter focuses on the first part of that task.

The core business process here is environmental scanning and competitive analysis in search of market opportunities which this enterprise can exploit. In chapters 2 and 3, students learn to use several tools to create thematic maps of pop-ulation characteristics, symbolize important characteristics of competitors and retail centers on a map, create customized datasets, and perform preliminary assessment of potential sites for LITGL’s store. Chapter 2 covers the basics of thematic mapping in Business Analyst Desktop, while chapter 3 helps students develop more advanced skills.


Chapter 2: Thematic mapping with Business Analyst wizards and layer propertiesThis chapter covers the basics of thematic mapping as a tool for environmental scanning in the process of developing entrepreneurial business plans.

To use thematic mapping effectively, students must understand the capabilities available in Business Analyst Desktop, the options available to them in creating thematic maps, and the impact of these decisions on the ability of the resulting maps to communicate important information accurately and effectively.

Specifically, students must decide:1. What data to include in the map and how best to represent it2. When graphs are more appropriate than color-coded maps or the reverse3. What classification scheme to use with what number of classes4. What color schemes to employ to communicate effectively and support other map layers, and what data should be

normalized and with which attribute5. How legends can be designed to communicate data types and relationships. In this chapter, they will use Business

Analyst Desktop to manipulate these tools for thematic mapping. Specifically, they will:• Create a study layer within a Business Analyst Desktop project• Design a pie chart map of home ownership patterns with a Business Analyst wizard• Design a thematic map of household income with a Business Analyst wizard • Explore alternate classification and color schemes within the wizard • Normalize data to design a map of home expenditures per household• Edit map symbology directly with the Layer Properties box

Teaching suggestions1. Thematic mapping is a very powerful environmental scanning tool, but is often neglected as a simple background

mapping exercise. Be sure students appreciate the large and varied dataset included with Business Analyst. One way of doing so is through discussion of the types of data that are best presented in chart or graph maps vs. thematic maps.

2. The classification schemes available in Business Analyst offer a range of data representation options. Students should appreciate the differences among them and the methods they use to create classes. A good place to do that is in the Classification window, which displays a sample selection of records, classification boundaries, and, at the user’s option, descriptive statistics.

3. Color choices for thematic mapping can affect the communication value of maps. Use different schemes varying in different directions to display the same data and ask students how they interpret the map. Look for schemes that enhance the visual communication of the underlying data as well as for schemes that distort it.

4. The impact of normalization can be illustrated with expenditure attributes. Use map examples to illustrate when high levels of spending are related to high populations with low per capita spending on the one hand and low populations with high per capita spending on the other. Which is more attractive in this scenario? Would that change with other businesses and/or business models?

5. Discuss the advantages and disadvantages of thematic mapping with Layer Properties rather than the Business Analyst wizard.

Beyond the basics1. Find ways for students to explore the rich data in Business Analyst. For example, they have used only a few home

related expenditure measures in the Living in the Green Lane scenario. Challenge them to revise this list by identify-ing a set of measures that would be more appropriate for another example enterprise or for a local organization. This would also be an important step in the development of a class project for a client organization.

2. For several measures, Business Analyst includes longitudinal data from 1990 and/or 2000 as well as CY estimates and FY projections. Using thematic mapping tools, map to create maps that display these values at these time frames. Although we do not discuss it in this book, the animation functions of ArcGIS 9.3.1 would be a good tool with which to display these projections.


3. Pose questions that anticipate some of the content of chapter 3. Can students calculate the differential between 1990, 2000, and 2009 values for the same attribute? (They can’t.) Can they create a single map layer representing the total number of college graduates by census tract? (They can’t without calculating a field, which is not directly possible with standard BA data.)

Concentrating on situation-specific data and overcoming some of these limitations are key topics in chapter 3.

Chapter 3: Advanced thematic mapping and symbology; creating datasets, dynamic ring analysisThis chapter covers the process of creating customized datasets to support enterprise-specific thematic mapping. It also includes techniques for symbolizing point features, such as competitors and retail centers, meaningfully as well as some very useful prospecting tools for identifying attractive locations for potential sites quickly. Finally, it discusses the process for adding satellite imagery and other ArcGIS Online content to Business Analyst Desktop maps.

Specifically, students will:1. Manage attribute visibility of a layer, both manually and with a saved attribute list2. Use Select by location to select the geographic units upon which a study will focus3. Export selected areas and data to a customized shapefile and include it in the Table of Contents 4. Design thematic maps based on new shapefile 5. Create new data fields, populate them with values using Field Calculator, and symbolize them in thematic maps6. Symbolize home and shopping center point features with symbols of different shape, size, and color 7. Use Dynamic Ring Analysis and Site Prospecting tools to identify and explore attractive locations8. Add satellite imagery to the project with ArcGIS Online

Teaching suggestions1. Based on some of the teaching points for chapter 2, illustrate the value of creating customized datasets. Applying clas-

sification schemes to a local area, instead of national data, can produce greater understanding of trends in the area. Localized datasets with a few key variables also are easier to manage and better for statistical calculations reflecting area characteristics.

2. Some of the key characteristics of interest to market analysts are not available in a single Business Analyst attribute. Thus, it is necessary to create and calculate aggregate attributes from them for use in future analysis. The educational attainment and home related expenditure measures in this project exemplify the value of this approach.

3. In addition to population characteristics and transportation infrastructure, the location and size of competitors on the one hand and retail centers which draw customer traffic on the other are key components of a business envi-ronment. As such, they are also important factors in environmental scanning. The point feature symbolization capabilities of Business Analyst Desktop allow users to capture these important characteristics with map symbology.

4. Satellite imagery can be a useful tool in exploring site characteristics that are not immediately obvious from symbol-ized maps of those characteristics. ArcGIS Online provides this and other mapping resources in a real time basis online. As these resources are constantly updated, users have access to the most recent imagery without the time and storage commitment of downloading, massaging and storing it. Students should be aware of how this service works as well as the various types of mapping resources it offers.

Beyond the basics1. Ask students to suggest other combinations of attributes and calculated fields that would be of value to Living in the

Green Lane, to other example enterprises or local organizations that might serve as class projects. How might this type of data help your university identify and recruit traditional college age students? Nontraditional older students?

2. These tools can be very useful for studying concentrations of demographically defined target customers. Several financial variables such as income and net worth are also aggregated by age group. So, for example, if a company defines its target customers as those with households with adults between 35 and 55 with household incomes between $80,000 and $120,000, the number of such households per census tract, may be determined with the same selection, export, field management, and calculation tools used in this exercise.

3. Business and shopping center data includes a number of attributes. Ask students to consider which are most relevant to a given business situation and how they should be represented on a map.


4. Ask students to explore the mapping resources available through ArcGIS Online and identify situations in which these would add value to business-related maps. Invite them to integrate those maps with Business Analyst layers to expand the communication effectiveness of the maps they create.

Teaching notes for Part III: Trade-area analysis and site selection without customer dataIn this part of the book students continue the role of business GIS analyst for Living in the Green Lane. Having assisted owners Janice Brown and Steven Bent with environmental scanning and market area analysis in the chapters 2 and 3, students’ attention turns to the task of selecting a site for the first LITGL store without any customer data.

Lacking a customer base from which to fashion a profile, this process involves systematic examination of market areas surrounding available properties in the Twin Cities area. Students begin by geocoding the list of available properties acquired from a commercial real estate agent. They then use a variety of techniques, tools, and reports to evaluate the characteristics of the market areas surrounding these sites and, at the end of chapter 5, select one of them as the site for the first store. Chapter 4 covers the geocoding process as well as several Business Analyst tools designed to identify potentially attractive locations. Chapter 5 covers the process of defining drive-time market areas, evaluating them with a series of Business Analyst reports, selecting the most favorable site for the first store, and using Layout view to design a map document supporting this decision as part of the enterprise business plan.

Chapter 4: Geocoding and evaluating alternative potential sitesThis chapter covers the geocoding process through which students place on a map the locations available for the new store. It also covers several techniques for identifying concentrations of households with favorable consumer character-istics as well as the tools for measuring the concentration of relevant consumer expenditures around each potential site. Finally, it includes Business Analyst tools which assess the competitive environment of each site in relevant reports and a traditional gravity model of retail attractiveness.

Specifically, students will:1. Geocode data on available properties and include it in a GIS project2. Use customer prospecting tools and grid-based heat maps to identify concentrations of attractive customers3. Create threshold trade-area rings around locations based on consumer expenditure data 4. Create Huff Equal Probability trade areas and Locator Reports, and use them to assess the competitive environment

of available locations

Teaching suggestions1. Students should understand the differences between site-selection approaches based on customer data and those that

do not require it. The former are used to select additional sites in chapters 6 and 7. The latter are covered here and use a variety of tools to understand the characteristics of the population and business environment surrounding each potential store location.

2. Business Analyst performs geocoding behind the scenes of the Store Setup Wizard. Though students used this proce-dure in Business Analyst Online in chapter 1, this is the first time they have applied it to a list of addresses from an external source, in this instance an Excel worksheet. This provides a good opportunity to review that process, which will be used extensively in later chapters.

3. Customer Prospecting and, to a lesser extent, Grid Maps are excellent tools for the task students face in this chapter. Both identify concentrations of attractive prospects based on population characteristics. Thus, the profile of green consumers Janice and Steven have developed can be used to identify pockets of attractive households in the Twin Cities area. Because multiple attributes are included in the process and users can specify ranges of values to include in the screening process, this tool can be tailored very exactly to the desired profile patterns.

4. This is the first version of the Huff Model that students encounter. They will use an expanded version later in the book to project sales based on competitive factors. The Huff Equal Probability Trade Area Model asserts the impor-tance of distance in consumer choice of competitive stores, but allows other factors to be included and weighted as well. This provides an opportunity to discuss the factors relevant to this choice and their importance relative to the distance factor.

5. The Locator Reports for both Home Centers and Shopping Centers facilitate discussion of how retail infrastructure can affect purchasing patterns. For this enterprise, home centers are competitors while shopping centers, which attract significant retail traffic, are market area attractors. The Locator Reports, which list nearby home and shopping centers for each store, provide comparative information on the competitive environments of each potential location.


Beyond the basics1. Encourage students to explore different combinations of factors in the Customer Prospecting Tool. What other

factors might be relevant for green consumers? How might the desired customer profile change for a traditional home center? For another type of retailer? For a direct-mail electronics retailer? For university recruiters? For an organiza-tion in your community? How can these differences be reflected in the tool? With what result in the maps?

2. The Huff Equal Probability Trade Area Model allows users to identify the factors deemed most relevant by consum-ers and to adjust their weights. What other factors might be considered here? How can they be factored into the process? With what results in the maps?

3. How might the Locator Reports and their importance be adjusted to other types of retailers? Students use straight line distance to select stores here but display their proximity in drive times. What is the impact of reversing these settings? If students wished to create as similar report for Building Materials stores, how would they do so? What steps are necessary to create this layer and develop Locator Reports from it.

Chapter 5: Defining trade areas, generating reports, selecting best siteIn this chapter, students complete the process of selecting the site for the first Living in the Green Lane store. They do so by creating trade-area polygons around each available location and assessing their comparative attractiveness with a series of reports. They then create map exhibits to support this decision.

Specifically, students will:1. Create drive-time trade areas around available locations and create reports detailing their characteristics 2. Perform comparisons of site population, purchasing, and lifestyle segmentation characteristics by using general and

detailed reports 3. Select and recommend a retail site by integrating and evaluating information from several maps and reports4. Design map documents to illustrate your analysis and support your recommendation

Teaching suggestions1. Students have addressed the relative value of ring and drive-time trade areas in chapter 1. This chapter provides an

opportunity to review that discussion and to see the differing structures of the drive-time trade areas for available sites. 2. The reporting function in this chapter follows the pattern of general reports for all sites and more detailed reports for

the sites considered the most attractive. These reports provide the information to complete the tables in this chapter and/or answer the associated questions. Correct table entries and answers to these questions are provided below. Taken together, they illustrate the application of many of the reports provided by Business Analyst and include his-torical as well as current year and projected future year data.

3. Once the recommended site is determined, it is important to communicate the rationale for this selection to readers of Living in the Green Lane’s business plan. Layout view provides the tools to integrate map, table, and chart data to create maps that achieve communication goals effectively.

4. Review the ROI discussion at the end of Part III. Be sure students understand the assumptions, judgments, and conclusions of this section. This analysis is the culmination of the value integrated business GIS contributes to the environmental scanning, marketing opportunity analysis, and site-selection decision processes. Understanding the financial benefits of the procedures employed in these decisions will help students appreciate the ROI impact of inte-grated business GIS on some of the most crucial entrepreneurial decisions.

Beyond the basics1. Review some of the alternative methods for creating trade areas without customer data, including the expenditure

threshold rings students created in chapter 3. Compare the various approaches on a map to assess their relative merits.2. Pose additional managerial questions (beyond those raised by Janice and Steven) and ask students to answer them

using information from various Business Analyst reports. This will help them understand the contents of different reports as well as their value relative to different business decision situations.

3. Experiment with other elements of Layout View to manage map elements in support of communication goals. Help students include multiple maps or other types of exhibits in map design.

4. Consider the benefits of these tools for small businesses, entrepreneurs and economic development organizations. Though Business Analyst is often viewed as the tool of large enterprises, the analyses students perform in chapters 1–5 illustrate its value for smaller companies and service organizations as well. How might these benefits be made more widely available to these types of enterprises?


Table data entries and answers to text questions from chapter 5 pages 114–122

Use maps, Market Profile Report, and Locator Reports to screen available sitesThere is no single method for integrating the range of data and maps you have generated into your site-selection decision. The approach below focuses on trade-area population characteristics, home related expenditure data, and competitive considerations to identify the most attractive sites. It then uses additional Business Analyst Desktop reports to incorpo-rate more information into the analysis to reach a conclusion and recommendation. You will begin by comparing the population characteristics of the available sites with the characteristics of the green-consumer profile.

1. Remember that green consumers tend to exhibit above-average levels of education, income, and home ownership. The Market Profile Report contains data on each of these characteristics for the drive-time polygon trade areas you created for each available site. You will use this data to identify the most attractive locations.

Use the data from the Market Profile report to record data on trade-area characteristics in the table below. Enter current year values for the 6-minute polygon for each site.

Trade area Total households

Median HH income

% owning home

% college degree *

Median home value

Garrison 72,911 $49,707 47.3% 34.8% $126,316

Reynolds 13,447 $79,406 77.5% 48.4% $187,489

Mayer 40,440 $61,218 66.6% 34.0% $150,706

Steiers 13,631 $103,013 88.1% 58.3% $218,970

Hall 11,199 $80,221 59.7% 60.6% $239,914

Carter 12,363 $99,144 80.4% 61.1% $249,126

Tucker 33,494 $49,452 57.9% 28.1% $123,413

* % college degree is the sum of the associate, bachelor, and graduate/professional degree percentages

Table 5.1 Population characteristics of available trade areas

Compare the values for the available sites. Which sites most closely match the characteristics of the green-consumer profile? Across all four measures, the Steiers and Carter sites are the best matches for the profile. The Hall and Reynolds sites are close, but fall short on at least one measure. Garrison and Tucker are the poorest matches for the profile.

2. Use the data from the Market Profile Report to record data on home-related purchases for each available site. Enter values for the 6-minute polygon for each site.

Trade areaHH furnishings expenditures Shelter expenditures

Total $Mil per HH Index Total $Mil per HH Index

Garrison $122 $1,664 75 $1,057 $14,451 82

Reynolds $38 $2,835 129 $312 $23,173 162

Mayer $80 $1,968 89 $659 $16,305 114

Steiers $46 $3,397 154 $368 $27,050 190

Hall $32 $2,891 131 $264 $23,579 165

Carter $44 $3,527 160 $355 $28,694 201

Tucker $53 $1,594 72 $448 $13,375 94

Table 5.2 Home-related expenditures in available trade areas


Total expenditures reports the total level of spending on this category in the trade area. Per HH (household) reports the average spending per household. The Index compares average household spending in each category to the national average. Thus, an index of 150 means that average spending on the category in the trade area is 50 percent higher than the national average, while a value of 75 means it is 25 percent lower.

Which available properties display the most attractive spending patterns? Steiers and Carter, with their high indexes, are the most attractive sites relative to spending patterns.

3. Toggle the Home Centers by Sales Volume, Shopping Centers, and Huff Equal Probability Trade Areas layers one at a time to compare them. Note the locations of available properties relative to these features and the transportation infrastructure of the area.

How are available sites positioned relative to shopping centers that attract retail traffic and transportation infrastructure that facilitates it? All the sites are reasonably well positioned relative to highway infrastructure. Garrison and Steiers appear to have several shopping centers nearby, while the remaining sites have relatively few.

How are available sites positioned relative to competing home centers and the boundaries of Huff Equal Probability Trade Areas? Most of the sites are located near trade-area boundaries, a good indication. The Garrison and Steiers locations appear to have the largest number of competitors.

4. Use the data from the Home Center and Shopping Center Locator Reports you generated in chapter 4 to record competitive environment data for each site.

Trade area

Attractors Shopping centers within 6 minutes

Competitors Home centers within 10 minutes

Number of centers

Number of stores

Number of competitors

Sales $US Million

Garrison 4 122 8 $398

Reynolds 0 0 2 $112

Mayer 1 95 4 $210

Steiers 3 190 6 $223

Hall 1 34 6 $295

Carter 0 0 5 $245

Tucker 1 95 6 $289

Table 5.3 Shopping center and home center drive-time distances from available sites

Based on the maps and Locator Reports, which sites have the most favorable competitive environment? The most unfavor-able? Explain. Garrison is the most active site with four shopping centers and four competitors, largely due to its location near the intersection of several major highways The Steiers site also has favorable retail attractions offset by substantial competition. The Carter, Tucker, and Mayer sites have few shopping centers and strong competition, while the Reynolds site has few of either. Overall, the Garrison and Steiers sites seem to offer the most favorable balance of retail attraction and competition.

Recall that Janice and Steven wish to purchase a retail facility with 40,000 to 60,000 square feet of floor space with four to five parking spaces per 1,000 square feet of retail space. They are willing to consider larger or smaller facilities if they serve highly desirable trade areas.

5. Right-click the Available Properties layer, then click Open Attribute Table to view the layer’s attributes. Compare the attributes of each site with Janice and Steven’s criteria.


6. In the final window, select the View reports on screen and Create single report file options. Click Finish.

Which sites meet the selection criteria?

Based on population characteristics, home-related expenditures, competitive factors, and site characteristics, which two sites are the most favorable? Explain why. Steiers and Carter are the two most attractive sites. They match the green-consumer profile well, have favorable spending patterns, convenient locations, reasonable competitive environments, and fall within the selection criteria.

The Carter site’s median age is higher than the Steiers site and its average household size is lower. Do these factors reflect differences in the age, employment, and family composition of the two sites? If so, how might these factors affect Living in the Green Lane’s sales in these trade areas?

The Market Profile Report provides the information you need to answer this question. Begin by completing the follow-ing table for the 6-minute drive-time trade areas of the two sites. The first two rows are the statistics Janice and Steven mention in this question.

Population characteristic Steiers Building 6-minute drive time

Carter Building 6-minute drive time

Median age 36.4 44.2

Average household size 2.71 2.48

Not in labor force, 2000 16.9% 27.9%

Households with related Children, 2000 45.5% 35.4%

Households with persons 65+, 2000 9.5% 24.3%

Median year moved into present house, 2000 1994 1991

Median year structure built, 2000 1984 1966

Table 5.4 Population characteristics of Steiers and Carter trade areas

Use this data to answer the first question here.

Yes they do. The population in the Carter trade area is older and less involved in the workforce. Household size is lower as well. This means that incomes are more likely to be relatively fixed and, since household size is lower, the savings in energy and environmental efficiency less pronounced. This extends payback periods, the time over which the cost savings of an energy-efficient system recovers its purchase price, a significant factor for greenback green consumers.

Are the favorable income, home ownership, and home value characteristics of these sites projected to continue through the next five years? Which site will have the greatest growth in population? In households?

The Market Profile report and the Comprehensive Trend report provide the data necessary to answer this question. Review these reports and answer the second question here.

Yes, future year projections indicate that these factors will remain favorable over the next five years. Steiers is projected to have the greatest growth in both population and households.

Both sites have high-spending indexes for general home-related expenditures. Is this also true for more detailed catego-ries of expenditures in this area? What implications do these indexes have for Living in the Green Lane’s market potential?


The Market Profile Report includes only two general expenditure categories for Household Furnishings and Equipment and Shelter. As referenced in the question, the Carter and Steiers trade areas have high-spending indexes for both catego-ries. The Retail Expenditures report includes these categories but also several subcategories within each. It also reports the spending index of each trade area for these subcategories. Review these values for the Steiers building 6-minute trade area and the Carter building 6-minute trade area. High-spending indexes across the subcategories would obviously be favorable to LITGL. Use this information to answer the third question here.

Yes, the indexes remain high across the various categories of expenditures. This means that these locations are both very attractive relative to their home-related spending patterns.

Do the age and household size differences between these two sites reflect underlying differences in lifestyle segments as well? If so, how might these differences affect Living in the Green Lane’s marketing strategy in these trade areas?

Business Analyst Desktop uses the Tapestry Segmentation Neighborhood Segmentation system to classify block groups in the United States into 65 distinct lifestyle segments. Lifestyle segments differ in demographics, values, housing char-acteristics, activities, and purchasing patterns. Those differences can be crucial in selecting appropriate sites, but also in crafting marketing strategies to serve customers well.

The variations between the Steiers and Carter sites in age, employment, household size, and housing patterns suggest that each may contain different Tapestry Segmentation segments. The Tapestry Segmentation Area Profile report provides the data to make that determination. In that report, Tapestry Segmentation Segments are organized by Life Mode and Urbanization groups. Segments within the same Life Mode or Urbanization group share some common characteris-tics. Within this structure, the report lists the number of households in each Tapestry Segmentation segment for each of the two trade areas. It also provides a segment index, which compares the percentage of trade-area households in the segment to the corresponding percentage of national households in that segment. Higher values for this index indicate greater concentrations of that segment in the trade area.

Use the data in the Tapestry Segmentation Area Profile report to identify the six most common Tapestry Segmentation segments in each trade area, recording the percentage of trade-area households and index of each.

Steiers trade area 4_2

Carter trade area 6_2

Segment number and name % of HHs Index Segment number and name 42.1 Index

13: In Style 27.1% 1,091 07: Exurbanites 42.1% 1,710

04: Boomburbs 26.1% 1,199 02: Suburban Splendor 21.1% 1,235

06: Sophisticated Squires 23.7% 877 24: Main Street USA 10.7% 409

12: Up and Coming Families 7.2% 219 30: Retirement Communities 7.9% 531

02: Suburban Splendor 5.0% 295 06: Sophisticated Squires 5.8% 215

16: Enterprising Professionals 4.1% 243 13: In Style 5.3% 215

Total 93.2% xx Total 92.9% xx

Table 5.5 Major Tapestry Segmentation segments by trade area

You will use expanded descriptions of Tapestry Segmentation segments in later chapters. To answer the current question, compare the general characteristics of the dominant segments in each trade area by using the Tapestry Segmentation Summary Table included in the Business Analyst Desktop documentation.

7. Navigate to C:\Program Files\ArcGIS\BusinessAnalyst\Documentation\USA_ESRI_Tapestry_Summary_Tables.pdf and double-click the file to open the Tapestry Segmentation Summary Table.


This table provides an overview of the core characteristics of each Tapestry Segmentation segment. Review the entries for the dominant segments in each trade area. The more closely these values reflect those of the green-consumer profile, the more attractive the segment is for Living in the Green Lane.

Using the data from this report and the two tables, answer the fourth question here.

Yes, these differences are reflected in the lifestyle segment composition of the two trade areas. Specifically, the Carter trade area includes several households that do not meet the green-consumer profile. The impact is that substantial portions of the population in this trade area would be less responsive to the product, promotion, and merchandising strategies of LITGL, creating less potential for market penetration and growth.

Based on your earlier analysis and your answers to these four questions, which location—Steiers or Carter—will you recommend as the site of Living in the Green Lane’s first store? Explain your reasoning.

On balance, Steiers is the better alternative. It has a greater percentage of households in lifestyle segments that match the profile while matching the Carter area’s high levels of spending on home-related products. In addition, it has slightly higher projected growth in terms of households and population over the next five years.

Teaching notes for part IV: Customer profiling and site selection with customer dataIn its first two years, Living in the Green Lane has enjoyed considerable success. Sales have been growing and the com-pany’s retail concept has been well received. Janice and Steven now face the challenge of building on that success and planning for future growth. They have identified two areas of potential new growth. The first is a penetration strategy. It seeks to increase sales to customers in existing stores by adding new product lines and expanding LITGL’s enterprise concept to that of a comprehensive green-lifestyle center.

The second growth strategy is expansion in the Twin Cities area, which they plan to accomplish by opening two new stores with the potential to match or exceed the sales level of the first store. Students in this part remain in the role of business GIS analyst, and have been charged with developing business GIS analyses to support these goals.

While these are challenging tasks, students have the benefit of two years of sales data at the first store to help in the process. Specifically, sales data from the 600-plus Green Living loyalty club members is available to them for use in these analyses. This allows them to base the new product line and site-selection decisions on historical data from the com-pany’s performance rather than general market area patterns as has been the case through chapter 5.

Chapter 6 focuses on the penetration strategy. Students will use customer data to identify the most attractive segment of High Purchasers. They will then develop demographic and lifestyle profiles of this customer segment and merge them with Market Potential Index data. In this way, they will identify the products and services best suited for the company’s new green-lifestyle center concept.

Chapter 7 focuses on the expansion strategy. There students will use sales data to create customer-derived trade areas for LITGL’s store and perform penetration and distance decay analyses. They will then identify attractive new sites for the second and third stores using Principal Components Analysis and estimate sales for these locations using the Advance Huff Model.

Chapter 6: Building a profile of distinctive customer characteristicsThis chapter begins with a geocoding operation in which students use an Excel table of Green Living loyalty club members to place these households on a map. Students use sales data to define the most attractive customer groups. They then perform a geodemographic overlay procedure by spatially joining customer records with demographic and lifestyle data tables. This creates a customer dataset from which profiles can be developed and integrated with Market Potential Index data to reveal the behavioral and purchasing patterns of high-value customers.


Specifically, students will:1. Geocode customer data with locator services2. Use Layer Properties to view attribute distribution and identify high volume customers3. Use a spatial join to attach demographic and Tapestry Segmentation attributes to customer features based on their

location4. Use summary tables to calculate geodemographic and Tapestry Segmentation lifestyle profiles of high volume

customers5. Use Tapestry Segmentation data with Market Potential Indexes to identify customer values, media habits, product

preferences, and purchasing patterns6. Use this information to make product line and merchandising decisions appropriate for Living in the Green Lane’s

best customers

Teaching suggestions1. This geocoding process is more extensive than the one in chapter 4 and creates an opportunity to discuss the

matching process, the various levels at which it is performed, and the ways in which Business Analyst reports this information to users.

2. The boundary decision for High Purchase customers is pretty clear-cut in this dataset. It is not always so obvious. This presents an opportunity to discuss the various ways in which this classification can be done both in general and within the Business Analyst system.

3. It is important that students understand the spatial join and summary table creation processes through which demo-graphic and lifestyle profiles are created. In their first encounter with these processes, students may overestimate the precision of the profile data. Similarly, when confronted with some of the inherent error in the estimation processes involved, they may well underestimate the value of the resulting profile. Though not precise measures of customer characteristics, the demographic and lifestyle profiles developed here produce very useful indications of the tenden-cies of the company’s customers taken as a whole. This, in turn, can be invaluable information in the product line and enterprise concept decisions involved here.

4. It is also important that students understand the Market Potential Index values fully. These measures represent trends as measured against national averages across lifestyle groups. As such, they are not direct predictors of lifestyle and/or buying behavior for each individual household within the lifestyle group. That said, the detail of the MPI data and its wide-ranging description of the values, activities, and buying behaviors of Tapestry Segmentation groups contribute greatly to the important task of understanding customers more fully and serving them more effectively.

Beyond the basics1. Explore with students alternative methods for segmenting customers based on sales levels. Use an alternative

approach and create the corresponding demographic and lifestyle profiles. How do they differ from the ones created in the chapter?

2. The attributes used to create the demographic profile summary tables are directly related to the green-consumer segment upon which Janice and Steven wish to focus. How would that selection of attributes and the resulting profile change if you wished to concentrate on an alternative set of characteristics?

3. The Market Potential Index values in appendix A cover a wide variety of items, but represent only a small set of measures in the system. Use them to explore different behaviors of the Tapestry Segmentation segments included there. What other types of products are these households interested in? What other types of enterprises would find them attractive target customers?

Chapter 6 Questions on page 149Business Analyst Desktop calculates summary values for the two segments and adds the results to the map as a data table below the LITGL Customer Demo layer. (NOTE: You will not be able to see the table unless the Sources tab at the bottom of the Table of Contents is selected. If it is not, click that tab and the table and its path name will appear under the Customer Overlay layer. Open the data table from the Table of Contents in the Source mode and compare the values for the High Purchases and Low Purchases segments. Use these values to complete the following table and answer the question below it. Notice that you must calculate the percentages of customers and purchases in the second and fourth rows.)


Population characteristic High purchases Low purchases

Customers in segment 202 394

Percent of total customers (calculate) 34% 66%

Segment total purchases $4,252,132 $1,779,497

Segment purchases as percent of total purchases (calculate) 70.5% 29.5%

Average household size in current year 2.89 2.68

Median household income in current year $117,428 $100,761

Median home value in current year $261,905 $224,565

Percent home ownership in current year 90% 83%

Projected percentage of home ownership in 5 years 90% 83%

Percent adults with college degree 60% 52%

Table 6.1 Population characteristics of high purchases and low purchases segments

How do the High Purchases and Low Purchases differ from each other? How are these differences related to the charac-teristics of green-consumer segments? Although both segments exhibit favorable values relative to the green-consumer profile, customers in the High Purchases segment tend to be better educated with a slightly larger household size. In addition, the High Purchase segment has higher values for income, home value, and home ownership than does the Low Purchases segment.

Pages 152–153Business Analyst Desktop calculates the values for the Tapestry Segmentation segments in this target group and adds the resulting table to the Table of Contents below the LITGLCustomerDemo layer in the Source Mode. Open the data table and compare the number of customers and purchasing patterns of the Tapestry Segmentation segments. Use these values to complete the following table and answer the question below it.

4. To preserve your work, save your map file as LITGLCustomers2.mxd to C:\My Output Data\Projects\LITGL Minneapolis St Paul\CustomData\ChapterFiles\Chapter6\.

Tapestry Segmentation Neighborhood Segment

# of High Purchasers

% of High Purchasers

% of U.S. population

Average purchases

Total purchases

04: Boomburbs 57 28% 6.7% $21,765 $1,240,596

06: Sophisticated Squires 43 21% 8.2% $19,861 $854,064

02: Suburban Splendor 38 18% 5.2% $24,555 $933,107

12: Up-and-Coming Families 25 12% 9.7% $18,030 $450,753

13: In Style 18 9% 6.7% $18,801 $338,424

07: Exurbanites 17 8% 6.1% $20,890 $355,144

Table 6.2 Tapestry Segmentation composition of high purchases segment

Which Tapestry Segmentation segments are the most numerous in LITGL’s High Purchases market segment? In order of size, they are CT segments 04, 06, 02, 12, 13, and 07.


How do their concentrations in this segment compare to national averages? CT segments 04, 06, and 02 are substantially above their national averages; segments 12, 13, and 07 are moderately above their national averages.

Which segments have the highest level of average household purchases in the past year? CT segments 02, 04, and 07 have the highest level of average purchases.

Which segments have the highest level of total purchases from LITGL in the past year? CT segments 04, 06, and 02 have the highest total purchases.

Profiles of the dominant Tapestry Segmentation segments among LITGL’s best customers provide significant insight into their characteristics, values, and purchase preferences. The Market Potential Indexes from Mediamarket Research provide even greater insight in its annual statistics on the values, activities, and purchasing behavior of Tapestry Segmentation segments.

Market Potential Indexes report the responses of each segment to hundreds of questions about their market behavior. The results are reported as indexes based on a value of 100. An index of 100 for a segment relative to a specific behavior means that this particular segment reports the behavior at exactly the same rate as the national average. A value of 125 means that the segment’s rate is 25 percent above the national average, while a value of 75 means that it is 25 percent below the national average.

Appendix A is a table containing a series of index questions selected for their relevance to the green-lifestyle customer Janice and Steven wish to reach as well as MPI values for the six most numerous Tapestry Segmentation groups in the High Purchase segment. Review the values for LITGL’s major Tapestry Segmentation segments and use them to answer the questions below the table.

Based on the values in appendix A, how well do these Tapestry Segmentation segments fit the following values, behaviors, and media-consumption patterns of the green-lifestyle consumers Janice and Steven wish to target?

Purchase lawn and garden maintenance services? (see Lawn and Garden) All six segments purchase these services at rates significantly above the national average. In addition, they purchase lawn and garden insecticides and organic soil addi-tives at high levels.

Purchase pest-control services? (See Lawn and Garden) All six segments purchase professional home services, including exterminator services, at rates significantly above the national rates.

Concern for environmental issues? (See Civic Activities, Lawn and Garden) All six segments recycled products at rates well above the national average. Five of the six participated in environmental groups within the past year at rates at high levels. All six segments participated in some public activity and five of the six contributed to public television more frequently than most households.

Interest in physical activities and fitness and wellness products? (See Apparel, Health, Leisure Activities and Lifestyle, Sports and Travel) All six segments participated at above-average rates in aerobics, mountain biking, road biking, jogging, swimming, snorkeling or skin diving, tennis, weight lifting, and yoga. In addition, all six exercised two or more times per week at above average rates. All six segments used vitamin or dietary supplements at high rates and five of the six bought natural or organic products more frequently than the average household.

Interest in fresh, organically produced fruits and vegetables? (See Grocery, Health) As mentioned above, five of the six segments bought natural or organic food products at high levels. All buy white bread at below average rates and one or more kinds of healthier breads at above average rates. They use seafood and fresh fruits and vegetables more frequently than the average household.

Read magazines on environmental, health, wellness, and home-related topics (See Media) All six segments are heavy magazine readers at rates moderately above national averages. This is also true for health, home service, and travel maga-zines. Most of the segments report high readership for science/technology and epicurean magazines as well. Music and gardening magazines are significantly less popular with these segments.

Responsive to direct marketing and the Internet, including purchasing products online, by mail order and phone? (See Internet, Mail and Phone Orders/Yellow Pages) All six segments report using the Internet more than once a day,


purchasing products and/or services online, and making travel plans online more frequently than most households. They also use mail and phone channels for purchasing products at high rates.

Which media are most appropriate for reaching these segments? (See Media) While these segments listen to radio at below average rates, they report high levels of listenership for classic rock, classic hits, jazz, news/talk, and—to a lesser extent—public radio formats. As noted above, all six segments are heavy magazine readers, with health, home service, and travel magazines the most popular choices. All six segments report heavy newspaper readership at high levels and similar levels of interest in the food/cooking, home and garden, sport, and travel sections of newspapers.

Chapter 7: Customer-based trade-area analysis and site selectionThis chapter begins with a geocoding operation in which students use an Excel table of Green Living loyalty club members to place these households on a map. Students use sales data to define the most attractive customer groups. They then perform a geodemographic overlay procedure by spatially joining customer records with demographic and lifestyle data tables. This creates a customer dataset from which profiles can be developed and integrated with Market Potential Index data to reveal the behavioral and purchasing patterns of high value customers.

Specifically, students will:1. Create sales-derived trade areas from customer records2. Produce trade-area penetration and distance decay reports using both customer and sales related approaches3. Identify available sites for the two new stores and rank them using Principal Components Analysis4. Estimate market penetration and sales using Advanced Huff Model Analysis with Statistical Calibration and the

owners’ judgments about comparative market factors

Teaching suggestions1. Customer-derived trade areas may be based on the percentage of customers (e.g. polygons including 60 percent and

80 percent of customers) or some volume measure (e.g. polygons including 60 percent and 80 percent of sales). The latter approach is used here, with annual sales as the volume attribute. Students should understand the differences between these approaches and their relative value in different decision situations. This distinction also applies to pen-etration and distance decay models used in this chapter.

2. Principal Components Analysis is a very useful tool for comparing multiple available sites simultaneously. Its effec-tiveness, however, is based on the relevance of the attributes selected for inclusion in the calculations. In this case, those attributes are all directly related to the green-consumer profile. Students should consider editing this list with other available attributes. The discussion of which attributes to include, which to exclude and why can increase students’ understanding of the wealth of data available to them and how they can best apply it to business decisions.

3. Similarly, the factors and weightings designated by students in the Advanced Huff Model with Statistical Calibration directly affect the outcomes projected by that model. The current factors and weightings are based on Janice and Steven’s judgments about their markets, customers, and competitors. Have students vary these weights and assess the impact on projected sales across the Mason market area. If you wish, you may also require them to anticipate competitors’ response to Living in the Green Lane’s success by varying the relevant measure for the home centers sur-rounding the Mason site. What impact do these revisions have on projected volume?

Beyond the basics1. Help students compare the customer-and sales-derived trade areas created in this chapter with the simple ring and

drive-time trade areas developed in earlier chapters. This comparison has two purposes. First it helps assess the accuracy and relative value of the earlier approaches as they relate to the areas drawn from actual sales information. Second, it allows students to determine which approach—and at what values—is most appropriate for developing prospective trade areas around potential new sites. For example, in this instance the larger polygons produced in the sales-derived trade areas justifies using larger rings or longer drive-times to create trade areas around prospective new sites.

2. The power of Principal Components Analysis lies in the flexibility that allows it to be customized to different enter-prise situations. Explore that flexibility by asking students how the attributes included in the analysis might change for a different kind of retailer—for example, a bank looking for ATM locations; a civic organization wishing to identify the best locations for service centers; organizations in your community that might be the focus of business GIS projects for the class.

3. The Advanced Huff Model with Statistical Calibration is also a very flexible analytical tool. In this application the calibration occurs with the settings on competitive factors and their importance which students enter as they run the


model. Variation of those factors and weights can change the outcome of the model, as discussed above. To build on that insight, encourage students to consider other organizational situations, perhaps those mentioned above in item 2. How might the model accommodate those situations? What changes in factors and weights would reflect the different settings of the organizations listed. Although these factors are largely judgmental here, users may also enter factors based on survey data. This capability integrates this process with more traditional methods of marketing research, extending the application of survey results within the enterprise.

Teaching notes for part V: Sales territory management and route optimizationAs part of their market penetration growth strategy, Janice and Steven have added lawn-and-garden-maintenance services and organic pest-control services to their product line. These new services require a sales force to create and maintain customer accounts as well as teams of technicians to provide the services. Janice and Steven have charged students, in their role as enterprise business GIS analysts, with using business GIS tools to design that territory system and implement a routing system for the service teams.

Students will use the Territory Design extension of Business Analyst Desktop to create a two-layer territory system using combinations of ZIP Codes. They will set parameters to design an initial system, then use the Territory Design window and manual editing tools to reassign ZIP Codes between territories to create a more balanced, geographically logical system.

When sales territories are established and sales and service operations begun, the focus of students’ attention turns to route optimization. Using the Find Route procedure within Business Analyst Desktop, they will designate the starting and ending points of the route, then add stops for each customer account on the service list. They will find a default route that services these calls in the order they were entered in the service system, then revise these setting to achieve the most efficient route possible. They will then determine the savings in miles, time, and cost associated with this analysis.

Chapter 8: Sales territory design and balancing; route optimizationThis chapter covers two vital functions for managing external sales and customer service operations: sales territory design and customer service technician routing. For the first function, students will create and balance six sales territories among Living in the Green Lane’s three stores for its sales force. They will use Territory Design and manual adjustment of territories to create a balanced territory solution and compare it with an alternative approach. For the second function, they will perform a routing procedure for the ten service calls within a pest-control technician’s daily schedule.

Specifically, students will:1. Build sales territories around seed points with the Territory Design extension2. Designate attributes to use in territory balancing schemes3. Create multilevel sales organizational schemes4. Realign sales territories to meet organizational objectives5. Designate stops on a service technician’s route6. Optimize route efficiency with Business Analyst Desktop’s Find Route tool7. Determine efficiencies and cost savings resulting from route optimization

Teaching suggestions1. As student solutions to this scenario will vary, this is a good opportunity to compare solutions and ask students to

explain the rationale that produced them. This helps students understand the flexibility of the system and their ability to adjust it to organizational goals. Encourage students to use Territory Comparison Reports to assess alternative models.

2. In this chapter, students use population as a balancing attribute in the original design, then households and expendi-ture levels to adjust the design manually. What other attributes would be appropriate balancing measures in this case? How will this process change in future analyses, when analysts will have actual sales data to include in the design?


3. Focus on the ROI for routing applications of business GIS, as this is one of the most beneficial early applications for many organizations. Although the estimates of costs and benefits for other applications can be ambiguous, any orga-nization with a dispatching operation will have actual per mile operating figures to use in these calculations. Thus, the ROI for this application is both immediately tangible and relatively accurate.

4. The dispatcher for the pest control teams uses records for existing service accounts to create stops. How will this process change for sales personnel, who may be calling on prospects for whom there is no current internal data? This question facilitates discussion of additional ways stops can be defined and provides the opportunity for students to experiment with them.

Beyond the basics1. How might the selection of balancing attributes in territory design differ for an organization in another industry? For

a business to business organization? For a not-for-profit organization wishing to define service areas?2. To use these tools with actual sales data, the sales levels for individual households or businesses must be aggregated at

the ZIP Code level if they are to be useful as balancing attributes. How would students extract this information from tables of customer sales data? Summarize the data tables on the ZIP Code field and calculate counts of customers and sums of their purchases in the summary operation.

3. Help students explore the flexibility of the Route Finder tool by posing the following scenario:

By noon of its workday, the pest-control technician has completed the first eight stops on the route you have plotted. However, during the morning, customer 12 has canceled an appointment and another customer, just south of customer 6, has called with a situation requiring quick attention.

How can the route be adjusted to factor in these events?

4. The procedure in the chapter makes no allowance for traffic conditions. Assume that a major thoroughfare on the route is closed due to road work or a weather event. Challenge students to use the Find Route tool Barrier tab to locate this type of restriction to the planned route, then observe how the system responds to these revisions. NOTE: Students may have to zoom into portions of the route and set barriers directly on roadways to create effective barriers in the system.

5. These situations reflect the type of real-time adjustments more advanced routing systems encounter every day. Encourage students to define how they might use GPS systems for vehicles, real time tracking in Business Analyst and online traffic information provided by various sources to improve the responsiveness of the system to these and other events.

Teaching Notes for part VI: Customer profiling and segmentation with the Business Analyst Desktop Segmentation ModuleHaving firmly established their business model successfully in the Twin Cities area, Janice and Steven now wish to grow the Living in the Green Lane enterprise through a combination of penetration and expansion strategies. The penetra-tion strategy seeks increased sales in the existing market while the expansion strategy seeks increased sales from entering markets in new geographic areas. In both cases, Janice and Steven will continue to ground their strategies on the insights gained from business GIS analysis. Specifically, they wish to extend the profiling and segmentation tools students have used previously to other tasks. The tool that supports these efforts is the Segmentation Module.

Chapters 9 and 10 focus on the Segmentation Module extension of Business Analyst Desktop. Students have already benefited from the Segmentation Module by having access to Tapestry Segmentation data at the block group level in earlier exercises. In addition, the Tapestry Segmentation Profile Reports they have used in several procedures also depend on this data.

The Segmentation Module automates the profiling process students have performed earlier and also extends the func-tionality of profiling and segmentation procedures considerably. Specifically, in this part of the book, students use a variety of techniques for creating customer profiles and use them to generate profiling and segmentation studies that reveal opportunities for growth in both the penetration and expansion strategies.


Chapter 9: Creating customer profilesThe Segmentation Module automates the process of creating demographic and lifestyle profiles of customers that students performed manually in chapter 6. In this chapter, students use tools to create profiles of different groups impor-tant to Living in the Green Lane. They use the Segmentation tool in Business Analyst Desktop to create a Tapestry Segmentation Profile of the Minneapolis St Paul CBSA. This profile will serve as the base against which students will compare a similar profile of LITGL Green Living club members. To obtain the latter profile, they will use Address Coder to geocode an Excel table of club members and their purchases. They will select appropriate demographic attri-butes to attach to customer records, perform the overlay function attached to those values to produce a demographic profile report on customers, a Tapestry Segmentation profile for use with the Twin Cities CBSA profile created earlier, and a map layer of geocoded customers.

Specifically, students will:1. Geocode customer addresses and attach demographic and segment data to them.2. Develop profiles from customer lists, layers, geographic areas, and survey data.3. Develop reports that summarize customer profile characteristics.4. Assign customers to target groups based on sales history

Page 226

Segment ID Percent Average Volumetric

4 16.4% $10,889

2 16.1% $13,242

6 16.0% $10,709

13 13.0% $9,755

7 13.0% $10,376

12 6.4% $10,959

16 4.3% $8,617Table 9.1 Segments accounting for four percent or more of LITGL’s customer base

Teaching suggestions1. As students create the Twin Areas CBSA profile, discuss with them the importance of this base comparative profile.

Business Analyst’s capacity to produce customized profiles of specific geographic areas means that the segmentation analysis of chapter 10 can be based on the base population of most interest to the business GIS analyst. While this base tends to be national level data by default, it is often of more interest to marketers, for example, to understand how an enterprise’s customers compare to the general population in their specific market area.

2. Address Coder is available as a standalone package outside the Business Analyst system. Draw students’ attention to the way in which it automates the more arduous task of profiling customers manually, a task they performed in chapter 6. Within the context of Business Analyst, Address Coder provides a very efficient way of analyzing tabular customer records quickly.

3. The Target Group and Game Plan Charts contain significant customer information that is displayed very efficiently. Be sure students understand the information they contain and how they may adjust the display of these variables as they wish. Specifically, call their attention to the options for defining the axes of the Game Plan Chart to include data on customer counts, purchases or a combination of both.

4. While the Segmentation Module offers reasonable default boundaries which define Core, Developmental, and Niche customer groups, it is also possible for users to define their own groups. This is, in fact, what students do at the end of chapter 9. It is important that they understand this capability, as they will be using default values in later exercises and might overlook this option.

Beyond the basics1. The profiles in this chapter are based on geographic areas or customer groups. However, the Segmentation Module

also allows users to create profiles based on survey data. That is, it can produce a profile of the customers who


register a positive response to any of the several hundred MPI items collected by Mediamark Research. Encourage students to create such profiles for a relevant MPI item such as “Purchased pest control services in past 12 months” or

“Purchased organic lawn products in past 12 months,” then compare these with the Twin Cities CBSA profile. This allows students to explore the match between consumers of specific services and the general market area.

2. Address Coder offers a powerful tool for quick profiling and segmentation data. Use lists provided by local organiza-tions and/or student groups to obtain snapshot profiles of their customers/members.

3. Similarly, profiling of customer/client/member lists can provide local enterprises or organizations with valuable infor-mation about the populations they serve. Thus, this capability can be a useful addition to class projects. It would also support projects for campus units such as admissions and alumni relations.

Chapter 10: Segmentation analysis for enterprise expansionThe procedures in chapter 10 are, in essence, the payoff of the Segmentation Module. Here, the profiles students create in chapter 9 are used to support very practical, results-oriented analyses of current and potential new customers. The focus of these efforts is both local and national. Locally, market area gap analysis identifies areas where the enterprise is performing very well and also areas where results are below expectations. This creates the opportunity to expand sales by concentrating on potential high volume customers in current market areas. Nationally, market potential maps and reports identify the best growth opportunities around the country. They also facilitate more detailed analysis of local conditions in the most attractive areas.

At both levels of analysis, the Segmentation Study produced by students in this chapter provides an excellent medium for communicating the results and implications of segmentation analysis. Students will use it to collect in a single document the results of the analyses they perform in chapters 9 and 10.

Specifically, students will:1. Assign segments to Core, Developmental, and Niche target groups based on their composition and/or sales volume2. Create reports using Market Potential Indexes to determine target group behaviors, values, lifestyles, purchasing

patterns, and media exposure3. Assess the potential sales volume of geographic areas based on their similarity with attractive customer profiles4. Assess the ability of attractive geographic areas to support new company-owned and/or franchised stores5. Create customized formal Segmentation Studies to support research conclusions and recommendations

Teaching suggestions1. It is important that students distinguish between the growth strategies Living in the Green Lane is pursuing and the

contribution of Segmentation Module tools to each. The increased understanding of customers that Segmentation Module tools facilitate supports both the objective of penetrating existing markets more fully and identifying new expansion opportunities around the country more precisely. Students address the former problem with local market area gap analysis and the latter with national level market potential scanning.

2. Relative to the local penetration strategy, market area gap analysis identifies pockets of households that match the profile of high value customers but are not yet members of LITGL’s Green Living loyalty club. These households are attractive targets in that they share characteristics of customers strongly loyal to the company. If they can be con-verted to customers, their potential for high levels of annual purchases is quite significant. Moreover, their similarity to current customers means that they can be reached with extensions of current marketing efforts and do not require specialized promotional campaigns.

3. With the Market Potential Map and Report, the focus of students’ analysis shifts dramatically from the Twin Cities CBSA to potential opportunities across the United States. Call their attention to the dual use of these tools. Initially, they are used as screening tools to distinguish the most attractive CBSAs around the country. However, they then become drill down tools, as students focus on a specific CBSA and potentially profitable locations within it.

4. Help students understand the importance of the Dynamic Ring Tool at this juncture. Specifically, it is used here to identify the potential opportunities for company-owned and/or franchise stores in the target CBSA. However, from a procedural perspective, it also represents a return to the analysis and decision-making process that began in the earliest chapters of the book. Specifically, students are repeating the approach they first used in chapter 3. Thus, with each new CBSA opportunity identified, the process of market analysis, planning, and development begins anew. This illustrates the need to extend Business Analyst tools throughout the enterprise so it can be used in these processes


wherever they occur across the company’s operations. This is, indeed, the function that Business Analyst Server plays in chapter 11.

5. Students should understand the power and potential uses of the Segmentation Study they design at the end of the chapter. The design process is simple enough that they might view it as a basic review of earlier procedures. However, its role in the organization can be quite significant, in that it records not only the results of Segmentation Module procedures, but also the logical flow of the analytical process that moves from basic customer and geographic profiles through descriptive mapping and report procedures to predictive models that identify opportunities for increased local sales as well as estimated sales volume from national expansion plans. Help students understand these larger roles by discussing the value of this study and its contents for internal enterprise decision making and strategic planning.

Beyond the basics1. The reports, charts, and maps produced by the Segmentation Module rely on the profiles and Target Groups created

in chapter 9. Thus, they may be customized to the specific groups identified by the business GIS analyst. Challenge students to define their own Target Groups based on profile data, rerun the market area gap and market potential tools, and analyze the differing results. This process provides insight into the flexibility of these tools in responding to particular enterprise situations.

2. The same external groups who might benefit from the profiling exercises in chapter 9 would also be well served by the more detailed analyses in chapter 10. Indeed, these tools offer a way of operationalizing enhanced understanding of customers by identifying opportunities for increased sales, service, and customer satisfaction.

3. The Segmentation Study provides a concise, comprehensive, well-organized presentation of Segmentation Module analyses for a client organization. Should students be involved in projects for such organizations, consider the Study a potential deliverable to accompany student reports and/or presentations.

Teaching notes for part VII: Expanding enterprise-integrated business GIS with ESRI Business Analyst ServerLiving in the Green Lane is established as a national home lifestyle center with several successful stores across the United States. That success has largely been based on the business GIS analyses students have performed in this book, from environmental scanning and market analysis, to site selection, customer profiling, segment targeting, and market poten-tial analysis.

The company now faces the challenge of institutionalizing business GIS tools across the enterprise. Students, in their role as enterprise business GIS manager, face the task of creating a business GIS system which managers can use in regular decision making. The system must match managers and their organizational roles and create the opportunities for managers to increase their business GIS skills as they advance in the organization.

Business Analyst Server is the ideal tool for achieving these objectives. It allows the enterprise to leverage its investment in demographic, lifestyle and business data across the organization while also facilitating the integration of internal data with the system. Moreover, it provides system administrators with the capability to manage the data and tools available to each manager and match those resources with the business GIS skills of managers at various levels of responsibility.

Chapter 11: Serving Business Analyst applications with Business Analyst ServerBusiness Analyst Server is a multitiered product that integrates the capabilities of Business Analyst Desktop, the Segmentation Module extension, and ArcGIS Server with its own unique interface and advanced analytical tools. Like Business Analyst Desktop, it offers a full range of business GIS functionality, but does so in way that extends these capa-bilities beyond a single desktop to reach throughout the organization. Like Business Analyst Online, it is a Web-based application available to all approved users, but it also has the capability to customize the system to enterprise needs and managerial roles.

A Business Analyst Server installation is a lengthy, complex procedure. In addition, the system requires oversight and administrative support beyond the scope of this book. For that reason, in this chapter students observe the function of Business Analyst Server, first through the role of a system administrator adding map services to the system and


configuring its security settings, then in the role of a new store manager, as she uses the system to perform customer profiling and segmentation analysis for the customers of her store.

Specifically, students will observe an administrator using the Business Analyst Server system to:1. Design maps for Web-based use and create map services from them2. Use the ArcGIS Server security system to define user names and roles3. Create and upload Business Analyst projects to Business Analyst services4. Manage user access business GIS resources with the Business Analyst Server security system5. Manage access to existing workflows and create customized workflows

In addition, students will observe a user, in her role as a new store manager, as she:1. Accesses Business Analyst Server resources in client role and upload data to the system2. Performs analytical tasks and executes a customized workflow in a Business Analyst Server environment to create

customer profiles and reports

Teaching suggestions1. On the administrator side, draw students’ attention to the direct integration of Business Analyst Desktop within the

Business Analyst Server system. The same maps, data, and projects with which they have been working throughout the book may be shared across the organization with this system. This provides a mechanism for leveraging existing business GIS analyses as well as creating new ones.

2. The security system of ArcGIS Server and Business Analyst Server has clear implications for protecting enterprise data and avoiding unauthorized access to enterprise data resources. However, it also has significant capabilities to protect the integrity of business GIS analyses, to match manager’s access to resources with their responsibilities, and to create a professional development system that allows executives to acquire new business GIS skills as their roles in the enterprise change.

3. From the user perspective, note the benefit of the proper sequencing of tasks and procedures within the workflow model. This approach allows managers with relatively modest business GIS skills to produce meaningful results using their own data. While this supports immediate decision making, it also allows these managers to experience the benefits of business GIS and motivates them to develop additional skills.

4. Note the tools available for the New Store Manager role. By defining the tools necessary for each role, system admin-istrators can shorten the time required for managers to learn and use business GIS tools. In addition, this provides users the capability to move beyond the strictly defined procedures of the workflow approach, while still working within the constraints of the proper functions for their skill sets and level of responsibility.

Beyond the basics1. As this is a demonstration chapter, students are not able to explore additional capabilities of the system. They can,

however, consider how Business Analyst’s security system might be used to match different sets of organizational capabilities. How might the security scheme differ, for example, for enterprises in different industries? For nonprofit organizations seeking to manage both donors to its organization and clients for its services?

2. In a similar vein, the Workflow model allows administrators to adjust the tools available to different roles and users. Students observe the workflow for new store managers. How might the workflow for regional managers differ from this one? How can workflows be used to create a business GIS learning path for new managers in the organization?

3. Invite students to revisit their Business Analyst Online accounts and compare the capabilities and characteristics of these systems. Although built on the same software platforms, each has a different role in the organization. When might the general capabilities of Business Analyst Online be the most appropriate tool for decision making? What types of organizations would benefit more from the flexibility, security, and customization capabilities of Business Analyst Server?

4. Consider the role of Business Analyst Server relative to class projects. How might this system allow you to make customized business GIS content and tools directly available to clients? How might an economic development or community support organization use these capabilities to facilitate business GIS implementation by client organizations?


Chapter 12: Conclusion: Growth trajectories with integrated business GISThe concluding chapter focuses on potential growth opportunities for enterprises seeking to fully leverage their invest-ment in integrated business GIS in general and Business Analyst’s suite in particular. It identifies growth opportunities within two broad strategies. They are:

Strategy 1: Increase the use of Business Analyst by expanding: A. Current tools in current operations

• geographic growth, market penetration, product line expansionB. Current tools in new operations

• Advanced Huff Model, new profiles, refine sales territoriesC. New tools in current operations

• direct marketing, profiling and prospectingD. New tools in new operations

• server technologies, e-commerce, data integration

Strategy 2: Expand beyond Business Analyst by implementing;A. New Business Analyst applications

• Business Continuity Planning• Sustainability Assessment and Reporting

B. Specialized business GIS• advanced vehicle routing systems• facilities management

C. Enterprise GIS• integration with ERP and/or CRM systems• enhanced Business Intelligence system

Teaching suggestions1. These concepts are presented in the context of the Living in the Green Lane scenario. To broaden students’ under-

standing, ask them to develop the concepts in the context of another organization. 2. If students are working on class projects for existing organizations, ask them to assess these growth strategies for these

organizations as part of their project reports.

Beyond the basicsForecasting the growth of a dynamic technology in a rapidly changing competitive environment is a difficult task. Ask students to use the resources below to evaluate the projections in this chapter. Which technologies have matured, which have not? Which have expanded with the development of Business Analyst, which have been replaced by other approaches? What new tools are available in the Business Analyst suite? How have the Web-based technologies devel-oped relative to desktop applications?


Additional resourcesBusiness Analyst Resource Center: http://resources.esri.com/businessAnalyst/ArcGIS Online: http://resources.esri.com/arcgisonlineservices/ESRI Customer Forums (including BA): http://support.esri.com/index.cfm?fa=forums.gatewayBusiness Analyst Blog: http://blogs.esri.com/Dev/blogs/businessanalyst/default.aspxDirections Magazine, look for articles, podcasts, webinars, and blogs: http://www.directionsmag.comESRITV on YouTube: Navigate to www.youtube.com and search for esritv.Business GeoInfo Newsletter: http://www.esri.com/industries/business/community/newsletter.htmlBusiness GIS case studies, best practices, podcasts: http://www.esri.com/industries/business/case _ studies.htmlESRI Business GIS site: http://www.esri.com/industries/business/index.html

Documents

Getting to Know ESRI Business Analyst - Esri Support | ArcGIS …downloads2.esri.com/ESRIpress/images/187/TeachingNotes.pdf · 2010-11-04 · GETTING TO KNOW ESRI BUSINESS ANALYST2