west marine’s transition to a waterlife outfitter: the role of location intelligence dr. lawrence...
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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
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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
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265 Stores including Hawaii, Alaska and Puerto Rico
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Customers
• People who recreate on or around the water
– Boating participants
– Coastal lifestyle
– Outdoor activity enthusiasts
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Core Products
Electronics Safety Maintenance Hardware
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Waterlife
Apparel Water sports FishingFootwear
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Small Store
Flagship
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Old
New
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Chicago – May 2015
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Core Departments
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Chicago, IL
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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
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Distance Decay Analysis
Lifestyle affects distance decay and varies by product
West Marine’s leading Tapestry segment
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Targeted Growth Segments
Growth segments have greater friction of distance…why?
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Market Modeling
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Contact: [email protected]