This document summarizes Angie Wang's customer segmentation and predictive modeling project. It begins by describing the dataset containing over 226,000 customer records. The purposes of the project are then outlined as identifying meaningful customer segments, providing managerial implications, and developing predictive models of total profit. Customer data is segmented using a hierarchical cluster analysis in SPSS, identifying six clusters. Clusters 3 and 6 are identified as the most valuable based on frequency and monetary spending. Predictive models of total profit are developed for Clusters 1 and 2, with different variables found to impact profit for each cluster. Insights into consumer behaviors for each cluster are also provided.

1. Angie Wang
Customer Segmentation & Predictive Modeling Project

2. The Dataset
A rich data set with over 226,000 records, reflecting over 137,000 orders from 100,000 random
U.S. customers (representative of all customers) who make purchases between 12/15/2004 and
09/17/2012. Based upon every single order line, this database records a wide range of historical
sale information including customer ID number, zip code, order date, cancel date, shipping date,
price, cost, channel, payment method and etc.

3. IDENTIFY MEANINGFUL CUSTOMER SEGMENTS01
PROVIDE MANAGERIAL IMPLICATIONS02
DEVELOP PREDICTIVE MODELING OF TOTAL
PROFIT
03
PURPOSES OF THE
PROJECT

4. IDENTIFY MEANGINGFUL SEGMENTS
PART ONE
TO UNDERSTAND CUSTOMER BEHAVIORS TO HELP
BARNEYS GENERATE MORE PROFITS

5. ASSUMPTION IN CUSTOMER SEGMENTATION
The RFM Model identifies meaningful customer segments
Recency Frequency Monetary
How recently a customer
makes a purchase
How much a customer
spends
How often a customer
makes a purchase

6. METHODOLOGY
Segment a large sample of customers into distinct groups of homogeneous customers
Aggregate transactional
data to customer data
Identify critical variables
related to RFM Model
(Profit, the time between
the first and last orders,
and number of orders)
Use SPSS Hierarchical and
K-Means Cluster Analysis to
identify meaningful customer
segments
1 2 3

7. SPSS Customer Segmentation Results
100K customers are segmented into six clusters. Customers in Cluster 3 (Middle-class shoppers)
and Cluster 6 (Upper-class shoppers) are identified as the most valuable customers to Barneys
based on Frequency and Monetary in the RFM model.
Payment Method
1. Amex
2. Discover
3. MasterCard
4. Visa
Channel
1. Phone
2. In- Store
3. Website

8. Highlights of the SPSS Customer Segmentation
Results
Cluster 1,
$5,128,492.86
, 50%
Cluster 2,
$1,094,995.41
, 11%
Cluster 3,
$1,118,466.09
, 11%
Cluster 4,
$1,884,696.28
, 18%
Cluster 5,
$791,138.06 ,
8%
Cluster 6,
$176,439.06 ,
2%
TOTAL PROFIT DISTRIBUTION
CLUSTER 1,
$65.09 , 1%
CLUSTER 2,
$217.43 , 4%
CLUSTER 3,
$621.03 , 10%
CLUSTER 4,
$143.62 , 2%
CLUSTER 5,
$716.61 , 12%
CLUSTER 6,
$4,303.39 ,
71%
AVERAGE PROFIT OF EACH CUSTOMER

9. Highlights of the SPSS Customer Segmentation
Results
1
1.5
2
2.5
3
CLUSTER 1
CLUSTER 2
CLUSTER 3
CLUSTER 4
CLUSTER 5
CLUSTER 6
Most Popular Shopping Channel
0
1
2
3
4
CLUSTER 1
CLUSTER 2
CLUSTER 3
CLUSTER 4
CLUSTER 5
CLUSTER 6
Most Popular Payment Method
1. Amex
2. Discover
3. MasterCard
4. Visa
1. Phone
2. In- Store
3. Website

10. Highlights of the SPSS Customer Segmentation
Results
0.0
0.9
3.7
3.0
1.7
3.2
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
CLUSTER 1
CLUSTER 2
CLUSTER 3
CLUSTER 4
CLUSTER 5
CLUSTER 6
ANNUAL PURCHASE FREQUENCY

11. MANAGERIAL IMPLICATIONS
PART TWO
The Most Valuable Customer Segments: Cluster 3 & 6

12. Managerial Implications for Cluster 3
Characteristics of an
average customer
• ≈ 4 purchases per year
• $621 in average profit
• Visa Payment
• In-Store shoppers
• 8 months in service
Recommendations
• Referral Program
• Cash-Back rewards
• Customer Knowledge -
feedback

13. Managerial Implications for Cluster 6
Characteristics of an
average customer
• ≈ 3 purchases per year
• $4303 in average profit
• American Express
• In-Store shoppers
• 45 months in service
Recommendations
• Increase Customer Lifetime
Value
- VIP in-store services , birthday gifts
• Increase Customer Influencer
Value
- Customer satisfaction , word-of-mouth
• Partner with American Express

14. PREDICTIVE MODELING OF TOTAL
PROFIT
PART THREE
The two selected customer segments with little similarity:
Cluster 1 & 2

15. METHODOLOGY
Develop Predictive Modeling to Forecast Total Profit for the Two Selected Customer Segments
Select variables that are
relevant to total profit
Run Multiple Linear Regression in SPSS
with Calibration sample (60% of random
sample from a cluster) and then validate the
predictive modeling with Validation sample
(40%), and identify outliers.
21

16. ASSUMPTIONS IN PREDICTIVE MODELING
TOTAL
PROFIT
NUMBER OF
ORDERS
NUMBER OF
ITEMS
ONLINE
PURCHASE
RETURN
QUANTITY
VISA
PAYMENT
Significance Criteria
If the significance level of a
variable is less than 0.05 in
Coefficients Table, that variable
will have impact on Total Profit.
.
Multicollinearity Criteria
If toleration is greater than 0.1
or 0.25 and VIF is less than 10
or 4 in Coefficients Table, there
is no multicollinearity effect.
.
.

17. PREDICTIVE MODELING OF TOTAL PROFIT – CLUSTER
1
TotalProfit = 106.424 – 76.543*OrderNumber + 6.079*ReturnQuantity + 23.202*Quantity– 1.270*WEB - 2.075*VISA
REGRESSION RESULTS FROM SPSS (Note: Web and Visa are dummy coded, 1 or 0)
24.9% of the variation in total profit in cluster 1 can be estimated by the selected five variables
(Number of orders, number of items, return quantity, online purchase, and visa payment). The remaining
75.1% is unexplained by this model, due to other variables.

18. 01
02
03
• Negatively related to total profit
• The mode of OrderNumber
 one-time shopper
• Only 116 customers out of the
total 78,792 customers in
Cluster 1 shop at Barneys twice
NUMBER OF ORDERS (-)
VISA PAYMENT (-)
ONLINE PURCHASE (-
)
CONSUMER INSIGHTS – CLUSTER 1
• A customer who places orders
online creates $1.27 less in total
profit than through other
channels (in-store and by phone)
• Only customers in Cluster 1
prefer online shopping.
• No human interaction in-store or
over the phone  less profit
• A customer who places orders
by Visa creates $2.075 less in
total profit than by other
payment methods.
• Fees are charged by Visa
provider.
TotalProfit = 106.424 – 76.543*OrderNumber + 6.079*ReturnQuantity + 23.202*Quantity– 1.270*WEB - 2.075*VISA

19. PREDICTIVE MODELING OF TOTAL PROFIT – CLUSTER
2REGRESSION RESULTS FROM SPSS
The two variables, WEB and VISA, are deleted from the regression model stepwise because the significance
levels are greater than 0.05, indicating no significant relationships with total profit.

20. NEW PREDICTIVE MODEL OF TOTAL PROFIT – CLUSTER
2REGRESSION RESULTS FROM SPSS
40.9% of variation in total profit in cluster 2 can be estimated by the THREE independent variables
(Number of orders, number of items and return quantity). The remaining 59.1% is unexplained by this model,
due to other variables.
TotalProfit = 42.822+14.859*OrderNumber+23.193*Quantity+8.210*ReturnQuantity

21. 01
02 (SAME CONSUMER INSIGHTS IN CLUSTER 1)
03 (SAME IN CLUSTER
1)
• Positively related to total profit.
• An average customer in Cluster 2
shops at Barneys for 3 times over
45 months in service.
 greater than one time in Cluster 1
• For each additional order, total
profit increases by $14.859.
NUMBER OF ORDERS (+)
RETURN QUANTITY (+)
NUMBER OF ITEMS
(+)
CONSUMER INSIGHTS – CLUSTER 2
• Positively related to total profit.
• The more items a customer
purchases
 higher total profit
• For every additional item that a
customer purchases, there is an
increase of $23.193 in total
profit.
• Positively related to total profit.
• The free return policy and 100%
money back guarantee
 Customers buy more and return more
 Higher total profit
TotalProfit = 42.822+14.859*OrderNumber+23.193*Quantity+8.210*ReturnQuantity

22. THANK YOU
Angie Wang