This is the final project presentation for the Geographical information systems (GIS) class at Clark University on Spring 2014 completed by Dushyanthi Pieris. The project analyzes best site to locate a new H&M in Massachusetts.

1. H&M Retail Site
Selection
Introduction to GIS
Final Project
Dushyanthi Pieris

2. • H & M Hennes & Mauritz AB (H&M) is a Swedish
multinational retail-clothing company, known for its fast-
fashion clothing for men, women, teenagers and children
• The company has 3,100 stores in 53 countries and just over
116,000 employees
Store Background

3. H&M US SWOT Analysis
Strengths
•Reputation as a brand that offers
fashionable clothing at a reasonable price –
knowledge of the mainstream customer
•Fast and well established distribution
system
Weaknesses
•New to U.S. online markets compared to
competitors GAP, Zara, Forever 21
•Business model complicates entry to the
online market (clothing sizes)
Opportunities
•Expansion of retail brick-and-mortar offer
an opportunity to introduce the online
shopping to new customers
Threats
•International economic instability effecting
the supply chains
•Slow economy effecting the 10%-15%
expansion rate
H&M - U.S.

4. Goal & Analytical Process
Who is the H&M
customer
Select the ZIP codes
best matching the
above characteristics
GOAL
Select a new site
for H&M store in
Massachusetts
with maximum
market potential
RecognizingH&MCustomer
- Analyze current
H&M locations to
recognize how H&M
identify their
customers
- Compare results of
the above process
with ESRI’s H&M
spending
- Recognize what
characteristics isolate
H&M customers
SelectingtheSite
- Filter the ZIP codes
best matching the
above characteristics
- Recognize sites in
the filtered ZIP codes
- Compare the
geographical details
and physical
characteristics of the
sites to select the
best candidate

5. Where are the H&M Customers?
Data Source: ESRI Gfk MRI via ESRI Business Analyst Online and current locations via H&M website
Geographical comparison of current H&M locations and H&M spending

6. ZIP Code Demographic Details
Attribute Current H&M Locations
*ZIP codes with highest
H&M Spending
Total Population 4,200 – 35,000 21,769 – 34,211
ESRI Common Tapestry
Segment
10 and 27 10
Per-Captia Income $31,500 - $55,000 $26,877 - $42,702
Percentage of
Population enrolled in
College & Grade 9-12
8% -10% 9% - 12%
* 124 ZIP codes (3rd quartile) that produce the highest amount of spending at H&M
using ESRI’s H&M spending data from ESRI Business Analyst Online
Analyzing twenty four different characteristics, following best signified an H&M customer

7. Four Best ZIP Codes for H&M

8. The Best Location
The best location is Woburn Mall
• Woburn is the closest match for
ZIP codes with highest H&M
sales
• The shopping center is
conveniently located in a
highway intersections, 95 and 93.
• The shopping mall matches the
general characteristics of other
shopping malls with H&M stores
• Located loser to two educational
institutions

9. Q&A

10. Appendix

11. ZIP Name Per Capita Income Total Population % of Population in College or Grade 9 -12
BILLERICA $ 36,421 30,486 11%
FRAMINGHAM $ 43,492 31,217 8%
WOBURN $ 34,555 38,078 9%
TEWKSBURY $ 36,378 28,838 11%
Demographic Details for Final
4 ZIP Codes

12. List of 24 Characteristics
Compared
Apparel Buying Habits
1. 2013 Dept./cloth/ shoe/spec store/ 3 Months: H&M
2. 2013 Dept./cloth/ shoe/spec store/ 3 Months: Forever21
3. 2013 Dept./cloth/ shoe/spec store/ 3 Months: GAP
4. Apparel and services index
5. Bought clothing online in last 6 months
Population
1. Number of Household units
2. Households
3. Families
4. Total population
5. Total population in ages 20-24
6. Total population in ages 25-29
7. Total population in ages 15-29
8. Median age
9. 15+: never married
Other household characteristics
1. % without vehicle ownership
2. % rent occupied houses: (2010 owner-occupied houses/ (2010 owner-occupied houses + 2010 renter-occupied houses)%
3. Dominant tapestry segment
Education
1. Population enrolled in Grade 9-12
2. Population enrolled in college
3. Population enrolled in grad and professional school
4. Population 3+ not in school
Income
1. Median household income
2. Average Household income
3. Per-capita income

13. Limitations and Further
Recommendations
• Limitations
• Most of the detailed analysis was limited by the data integrity
and availability.
• Further recommendations for analysis
• Compare how each H&M store sales differ based on the
demographical data by location and physical characteristics.
• Is there seasonal differences in sales by location (ex. How having
a store in a college town may affect sales through the year)
• It was apparent high H&M spending was correlated to physical
location though there were several ZIP codes father away from
the H&M physical stores. Why the anomaly?
• Use GIS to produce a detailed analysis on shopping center
locations; demographic data, gross leasable areas, types of stores
in the center etc.

14. Data sources and Tools
Data Sources
• ESRI Business Analyst Online
• Mass GIS
• H&M website
Tools
• ArcMap
• ArcCatalog
• ESRI Business Analyst Online
• MS Excel
• MS Access
• GeoCoder.US