Our team’s objective in this study was to identify the demographic makeup of the market for Dominick’s Finer Foods in terms of store clusters, discover sales patterns and recommend a targeted positioning strategy. In this report, we present information on our data source and methodology, along with a detailed analysis of results and a recommendation. We performed K-Means segmentation on store demographic data to discover clusters of stores which could be uniquely identified through variables such as income, average household value, average household size, education and ethnicity. We then compare sales in a variety of product categories in these clusters to find patterns. We conclude by citing limitations of this method and how it can be further enhanced.

1. Retail Demographics Analysis
Eeshan Srivastava
Balaji Vanjinathan
Jia Xie

2. Agenda
• Background
• Problem Statement
• Methodology
• Results and Analysis
• Recommendations
• Questions
2

3. 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
3

4. Problem Statement
4
• 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?

5. Methodology
5
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

6. Preliminary Test
• Assumption: there is a variance in sales across different stores in at least one
product
• Test Product: Beer
6$(500.00)
$-
$500.00
$1,000.00
$1,500.00
$2,000.00
$2,500.00
$3,000.00
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
Avg Beer Sales by Store Number

7. K-Means Segmentation
7
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

8. K-Means Segmentation
8
0
0.05
0.1
0.15
0.2
0.25
0.3
0 2 4 6 8
K-Means Elbow Chart
Cluster # Income Education Ethnicity Avg Family Size Mean House Value Avg Beer Sales Avg 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

9. Analysis
9
$550
$600
$650
$700
$750
1 2 3 4
Avg $ Sales of Beer/day
$-
$200
$400
$600
$800
$1,000
$1,200
1 2 3 4
Avg $ Sales of Fish/day
$-
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
1 2 3 4
Avg $ Sales of Dairy/day
$-
$2,000
$4,000
$6,000
$8,000
1 2 3 4
Avg $ Sales of Meat/day

10. Store segments on the map
10

11. Recommendation
11
Identification of
market segment
Targeting right
segments for
maximized profits
Product
Positioning/Placem
ent

12. Limitations
12
• 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

13. Questions?

14. Thank You!