West Marine’s Transition to a Waterlife Outfitter: The Role of Location Intelligence

Dr. Lawrence JosephResearch Manager

GIS Day – November 18, 2015 – University of Redlands

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Background• My experience• Original motivation

– Need to find PetSmart locations• Line of research

– More than 50 US retailers– Nearly 71,000 retail stores

• Application to West Marine

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Structure of Retailing• Retailers have a value platform

– Includes factors such as personnel, service, and display of goods, as well as the situational aspects of store location

• Retail is dynamic• Increasing consumer differentiation• Need to satisfy investors with growth• Development of store types• Exogenous factors

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Store Deployment

Process

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Evolution of Chain Networks• Collection of individual decisions creates a chain network

• Each individual deployment decision affects the chain

• Empirical research has led to theories on the spatial organization of stores (and markets)

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Real-Estate Maturity• All retailers have some point of real-estate maturity

• Only a limited number of locations that can be profitable for a particular type of store

• At some point, new stores increasingly cannibalize existing stores

• Constraints on organic and familiar growth through new store deployment

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Variations in Retail Stores

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Target

CEO fired

Target takes $5.4 billion hit

Opts to leave market

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Modeling Retail Chain Expansion and Maturity through Wave Analysis: Theory and Application to Walmart and Target

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Objectives• Introduce a method from medical geography used to study

epidemiological waves

• Adapt method to the study of the diffusion patterns of retail stores over time and space

• Apply these modified methods to study the spread of Walmart and Target

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Method Adapted from Cliff and Haggett (2006)

Coastal Geomorphology Epidemiological (Cliff and Haggett 2006)

Retail (Proposed here)

  Susceptible (S)No cases yet

ProspectiveNo stores yet

SwashWave moving up beach

Infected (I)Cases reported

DeployingNew store growth

BackwashWave moving back to sea

Recovered (R)No more new cases

SaturationFewer new stores than previous period

    Cannibalization (Re-swash)More new stores than previous period

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Equations

]0) > | ([1

1 1

T

t

A

iititi mqiMax

Ts

(1)

A

iit

T

tLE ft

At

11

1 (2)

)1( tiitit qqq

(3)

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Idealized Plot of a Swash-Backwash Wave

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Deployment Patterns of Walmart A Walmart

1960-1964

1965-1969

1970-1974

1975-1979

1980-1984

1985-1989

1990-1994

1995-1999

2000-2004

2005-2009

km

a14 0 0 0 0 0 0 6 13 5 4 2600-2799

a13 0 0 0 0 0 0 35 16 12 3 2400-2599

a12 0 0 0 0 0 0 45 12 17 25 2200-2399

a11 0 0 0 0 0 0 60 54 48 48 2000-2199

a10 0 0 0 0 0 0 32 31 43 61 1800-1999

a9 0 0 0 0 0 13 87 54 69 33 1600-1799

a8 0 0 0 0 18 58 88 41 59 40 1400-1599

a7 0 0 0 0 23 72 71 46 52 23 1200-1399

a6 0 0 0 0 33 97 100 48 64 52 1000-1199

a5 0 0 0 0 90 140 132 38 82 4 800-999

a4 0 0 0 14 143 146 49 18 47 6 600-799

a3 0 1 11 74 85 61 29 8 35 13 400-599

a2 0 4 40 59 45 33 6 7 15 13 200-399

a1 2 8 30 15 16 9 2 2 3 2 0-199

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10

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Deployment Patterns of Target

B Target

1960-1964

1965-1969

1970-1974

1975-1979

1980-1984

1985-1989

1990-1994

1995-1999

2000-2004

2005-2009

km

a13 0 0 0 0 16 49 19 30 47 46 2400-2599

a12 0 0 0 0 2 27 17 13 26 33 2200-2399

a11 0 0 0 0 2 8 34 18 29 33 2000-2199

a10 0 0 0 0 0 1 13 5 16 16 1800-1999

a9 0 0 1 1 4 1 11 56 103 68 1600-1799

a8 0 0 0 0 3 6 4 91 75 86 1400-1599

a7 0 2 0 2 4 3 3 16 36 34 1200-1399

a6 0 1 4 3 8 4 7 32 28 46 1000-1199

a5 0 0 0 0 6 11 19 21 31 17 800-999

a4 0 1 1 1 3 10 13 15 26 20 600-799

a3 0 0 2 2 3 1 34 20 29 23 400-599

a2 1 0 0 3 1 2 13 5 9 2 200-399

a1 1 1 0 2 2 4 16 10 17 17 0-199

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10

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Summary of DeploymentA Walmart 1962 2009

t 1t 2t 3t 4t 5t 6t 7t 8t 9t 10t

New Stores (∑q) 2 13 81 162 453 629 742 388 551 327

Store Count 2 15 96 258 711 1340 2082 2470 3021 3348

A

iitm

1

1 3 3 4 8 9 14 14 14 14

0 > | iti qiMax 1 3 3 4 8 9 14 14 14 14

B Target 1962 2009

t 1t 2t 3t 4t 5t 6t 7t 8t 9t 10t

New Stores (∑q) 2 5 8 14 54 127 203 332 472 441

Store Count 2 7 15 29 83 210 413 745 1217 1658

A

iitm

1

2 4 4 7 12 13 13 13 13 13

0 > | iti qiMax 2 7 9 9 13 13 13 13 13 13

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Space-Time Deployment

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Prospective Deploying Saturation Cannibalizing

Walmart 40.0% 22.1% 25.7% 12.1%

Target 24.6% 27.7% 21.5% 26.2%

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Swash, Backwash, and

Re-swash Waves for Walmart

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Swash, Backwash, and

Re-swash Waves for Target

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Walmart

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Walmart Drive-time Trade Area Data

In 10 Minute Drive-Time Trade Areas

TypeStore Count

Standard Distance

(mi)House-holds

House-hold Size

Median Household

IncomePop

Density

Average # of

Grocery Stores

Average # of

Walmart Stores

Average # of Neighborhood

MarketsWalmart (in 1998)

2,324 381.4 23,429 2.50 $43,890 1,059 30.6 0.05

Walmart (in 2014) 3,759 394.1 27,410 2.52 $46,856 1,330 35.5 0.39 0.17

Walmart Neighborhood Market

365 407.8 66,297 2.62 $52,287 3,272 80.7 1.81 0.67

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Walmart in 1998

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Walmart in 2014

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Walmart Neighborhood

Market

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Spatial Mean Centers

West Marine

47 Years of History

• 1968 - “West Coast Ropes” in founder’s garage

• 1975 - First store in Palo Alto, CA

• 1993 - Company goes public

• 1996 - Acquired E&B Marine store count to 152 doors

• 2003 - Acquired 63 Boat U.S. stores

• Today - Expanding the business as a Omni-Channel, Waterlife Outfitter

265 Stores including Hawaii, Alaska and Puerto Rico

Customers

• People who recreate on or around the water

– Boating participants

– Coastal lifestyle

– Outdoor activity enthusiasts

Core Products

Electronics Safety Maintenance Hardware

Waterlife

Apparel Water sports FishingFootwear

Small Store

Flagship

Old

New

Chicago – May 2015

Core Departments

Chicago, IL

ESRI Business AnalystData– Over 1,600 data variables– Demographics– Tapestry (Lifestyle Segmentation)– Consumer spending– Market potential– Shopping center/businesses– Competitors– Street data

• Drive-time analysis

Customer Profiling– Prospecting

• Admirals• Captains• Adventurers

Sales Forecasting– Modeling– Analogs– Cannibalization

Distance Decay Analysis

Lifestyle affects distance decay and varies by product

West Marine’s leading Tapestry segment

Targeted Growth Segments

Growth segments have greater friction of distance…why?

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Market Modeling

Contact: LawrenceJ@westmarine.com