Date post: | 14-Apr-2017 |
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Marketing |
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Retail Demographics AnalysisEeshan SrivastavaBalaji VanjinathanJia Xie
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Agenda• Background• Problem Statement• Methodology• Results and Analysis• Recommendations• Questions
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Background• Dominick's was a Chicago-area grocery store chain and subsidiary of
Safeway Inc.• Closed operations in Dec, 2013 after a series of failed strategic
initiatives and high competition
• Data from 1989 – 1994 when Dominick’s was a regional leader• 9 years of store-level data on the sales of more than 3,500 UPCs• Collected by The Kilts Center for Marketing at Chicago Booth and
Nielsen
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Problem Statement• To identify the demographic makeup of the market in terms of
store clusters, discover sales patterns and recommend a targeted positioning strategy
• What’s in it for Dominick’s? What are the characteristics of customers visiting each store? Which stores attract higher sales in which categories? Where are the store clusters located geographically?
Methodology
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Identify relevant data• Store-level sales and
traffic data ~ 300K transactions
• Store demographics data ~ 100+ stores
Cleanup data• Remove
junk• Remove
stores with very few transactions
Preliminary testing of assumption• Discover variance in
sales across stores for at least one product
K-Means Segmentation• Identify segmenting
variables• Perform clustering and
discover correct # of clusters
Generate Results• Merge cluster data with
sales data• Collect sales data per
cluster
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Preliminary Test• Assumption: there is a variance in sales across different stores in at
least one product• Test Product: Beer
2 6 12 19 32 44 48 52 56 62 68 72 76 81 88 92 97 102
106
110
114
118
123
129
133
137
142
301
305
309
313
318
$(500.00)
$-
$500.00
$1,000.00
$1,500.00
$2,000.00
$2,500.00
$3,000.00
Avg Beer Sales by Store Number
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K-Means Segmentation
Ethnicity – % of Hispanics/Blacks
Household size – average number of members in
the family
Household Value – average
house value in the area
Income – average income
of the neighborhood
Identified the following variables which could sufficiently differentiate clusters from one another
Education – % of college graduates
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K-Means Segmentation
1 2 3 4 5 6 7 80
0.05
0.1
0.15
0.2
0.25
0.3
K-Means Elbow Chart
Cluster # Income Education Ethnicit
yAvg Family
SizeMean House
ValueAvg Beer
SalesAvg Cust
Count# of
stores
1 $ 38,991 20.2% 12.6% 2.66 $ 140,090.08 $
615.27 2535 32
2 $ 58,657 44.4% 7.5% 2.52 $ 246,393.53 $
626.14 2762 6
3 $ 47,731 29.6% 7.6% 2.62 $ 181,861.19 $
613.88 2621 25
4 $ 33,438 11.9% 30.1% 2.77 $ 91,575.16 $
715.09 3048 22
We decided to pick 4 as our number of clusters to avoid having clusters with too few number of stores
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Analysis
1 2 3 4 $550
$600
$650
$700
$750
Avg $ Sales of Beer/day
1 2 3 4 $-
$200 $400 $600 $800
$1,000 $1,200
Avg $ Sales of Fish/day
1 2 3 4 $-
$1,000 $2,000 $3,000 $4,000 $5,000 $6,000
Avg $ Sales of Dairy/day
1 2 3 4 $-
$1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000
Avg $ Sales of Meat/day
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Store segments on the map
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Recommendation
Identification of market segment
Targeting right segments for maximized
profits
Product Positioning/Place
ment
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Limitations• Need to avoid the stereotyping pitfall
• Needs to be coupled with other types of segmentation – Psychographics and Purchase Behavior
• Hypothesis testing could be done for each segmenting variable before performing segmentation
• Costs and profitability data should also be analyzed across store segments
Questions?
Thank You!