Esri Data Tapestry Segmentation Methodology Statement

Esri Data: Tapestry Segmentation Methodology Statement

1. Methodology Statement:
Esri Data—Tapestry” Segmentation

An Esri® White Paper
March 2013

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3. Methodology Statement: Esri
Data—Tapestry Segmentation

An Esri White Paper

Contents Page

Statistical Methods.

Data Used to Build Tapestry Segmentat 2
Verification Procedures 2
Database Update 3
Esris Data Development Team. 3

‘Be White Paper i

5. Statistical Methods

Methodology Statement:
Esri Data—Tapestry Segmentation

For more than 30 years, companies, agencies, and organizations have used
segmentation to divide and group their markets to more precisely target
their best customers, prospects, citizens, residents, members, and donors.
‘Segmentation systems operate on he theory that people seek others

tastes lifestyles, and behaviors similar to their own—"lke seeks like
‘These behaviors can be measured, predicted, and targeted. Segmentation
explains customer diversity, describes lifestyle and lifestages, and
incorporates a wide range of data

‘Tapestry Segmentation represents the fourth generation of geodem graphic market
segmentation systems that Bega wih the firs mas release of machine readable, small
Ara data from the 1970 Census. The avait of hundreds of variables fr thousands
‘of meighbortods was bah resisóblc and dam ing for marketers. What hey needed was
true way l rete infomation rom an overwhelming databace Mark
segments provide that structure, a system for clssiing consumers by using al the
sables that can distinguish consumer behavior om houschok characteris ike
income and family type to personal ais suchas age education, or employment and even
10 ousing ies.

‘Tapestry Segmention classifies US neighborhoods nw 65 dsinet market segments
Neighborhoods with he mnt similar characters are groupe together, while
‘neighborhoods shoving diverge characterises are epartd.Tapesty Segmentation
‘combines the “who” of lifestyle demography with he “where” of al neighborhood
‘severly to create a model of various He clsficaions o segmento actual
‘eihborhoods with addresses inc behavior market segments.

‘Cluster analysis is the generic approach used to create a marke segmentation system,
"here area number of difieren techniques or clutring methods that can be applied

10 identify and cts market tes. Each technique has is strengths and weaknesses,
Previous generations ol Tapest Segmentation have ben built using a combination of
techniques, such asthe iterative patton Kc eans again erat th inal clusters
sr mark segment, loue by aplication of War's Nierrchical minimum variane
cido group clusters. The combination has provided a complementary match al
(be srengthsof cach technique. Tapestry Segmentation combines the tradional withthe
Las data mining technique’ to provide robust and compeling segmento of US
neighborhoods. Es developed ad incorporated these data mining techniques o
“complement and srengihen radial methods o werk with age peodemopraphic
ahases Robust methods ae les susp o extreme values of ter tare
therefore crucial to smalaea analysis. The tational methodology of cluster analysis
hav ong track recordin developing marke segmentation system. Complemenay se
of daa mining techniques and implementation of robust methods enhance he
effectiveness of wana satstical methodology in developing Tapsry Segment.

‘Be White Paper

6. Methodology Statement sr Data Tapestry Segmentation

Data Used to Build
Tapestry
‘Segmentation

Verification
Procedures

Fora broaler view consumer markets se analysis was gain we to develop the
‘Tapesty Segmentation summary group, Summary grups areal when user wan 10
work with Fever than 65 segments. The 6S segments are combined into 12 LfeMode
groups based on lifestyle and sage. The 1 Urbanization groups present an leave
ay of combining the S segments ed on he segment? geographic and physical
Festrs sich as population density, sz of ey, location relaie to a mewopolan ae,
and wheter they ae par ofthe economic and social enter ofametropolan are.

‘use analysis techniques are essential eure methods that ely on exploratory
procedures 1 ave a sable and optima solo, The key 1 developing a powerful
market segmentation system is in the selection ofthe variables used classify
‘consumes, US consumer markets ae multidimensional and diverse, Using sae,
el Selected array atte capes this versity withthe most powerful data
alle, Data sources include Census 2000; Census 2010: the American Community
Survey: Ess demographic update: Experian INSOURCE™ consumer date: and
consumer surveys such a the Survey ofthe American Consume rom GIK MEU,
‘apt he sb and vibrancy ofthe US marketplace,

Selection ofthe variables used o identify consumer markets begins with data ha
incldeshowschold characteristics such as type (single person or Family), income,
‘ations (ingle or muligeneniona), and ownerreer status: personal ait such as
age sex edoaton employment, and marital stats: and housing characters ike
ome valu orret type of housing (single amily apartment or townhouse) seasonal
Sats, and over cost relive o come essence, any characteristic ha eke to
“ferent consumer spending and preferences i assessed foruse in kenlying
‘consumer markets

“The selection proces draws on Esti experience in working withthe 1980, 199, and
2000 censuses and includes range of muliarite ia methods, in ion,
factor analysis principal components analysis creation matrices, and graphic methods
Selecting the most relevant variables à eal lo defining homogeneous markt
segments: however determining the mos effective measure of each variable i equally
import income bes represented by a median, an average, or an eral? Would
Rachel or disposable ine best measure actual buying power? nthe end, selection
vas narrowed more than 60 tite 10 dent and clone US needs by
market pe

From the neighborhood or Bock group eel Tapestry Segmentation profiles cable the
comparison of consumer markets across the county By sate, metropolitan arca coun,
place census ec, ZIP code, and even congressional rich

Veriicaon procedures follow the creation ofthe segments o ensue their stability and
sally. Replicating be segment with independent samples checks subi. Vali is
checked trough characters tha ae not weed to generat segmens inking
“Tapesty Sepmenttion othe lest consumer survey da theeical est. A market
Segmentation sytem must be able to distinguish consumer behavior spending pates
{nd ie choices a expec. Ese veis the eine) of ts Tapestry Segmentation
market guns the consumer surveys rom GIK MR which inclue nsry 600
product and serie brands in 550 categories along wth readership of hunkeds o
mains and newspapers, Item sage, TV viewership by channel and program. ratio
listening, and we of Yellow Pages. The vai check provides he answer othe most,
importan question: Does work It work.

Mena

7. Methodology Statement: Er Dats—Tapesry Segmentation

Database Update

Esei's Data
Development Team

‘Tapestry datas updated annually with eurent tt from Ess demographic updates.
For more information about Tapestry Segmentation, vs comes area
1800-17978

Led by chief demogrger Lynn Wombol, Er data development tam has a 30636
story of excellence in market ieligece. The combined expertise of the teams
Sonomiss, sttsticians,demograpers, geographers and analysts tous nearly acenury
{fda and sepmentaton development experience In ation 10 Tpesuy Segmentation,
{he lama develops Uplate Demographie. Consumer Spending, Market Pote

aná Retail MarkPlce dataset, which are now industry Benchmark. For more
Information abot Es data, va os ont,

‘Be White Paper

8. @esri

Esrinspites and enables people to postivey impact thei
future though a deoper, geographie understanding of the
changing world around them

organizations ist vs to connect ther withthe analyte knowledge
rote than 40 aaa, Esihas culote colaboraineelatonshipe
pressing challenges wih geographic expertise and atonal rave.
Today we bebe that geography isa the heart of mor rent
andsstinable fue Creating esponsble products andsolutions
‘Sines cu pension forro ly fe overran.

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