Angie Wang

Customer Segmentation & Predictive Modeling Project

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.

IDENTIFY MEANINGFUL CUSTOMER SEGMENTS01

PROVIDE MANAGERIAL IMPLICATIONS02

DEVELOP PREDICTIVE MODELING OF TOTAL PROFIT03

PURPOSES OF THE PROJECT

1 IDENTIFY MEANGINGFUL SEGMENTS

PART ONETO UNDERSTAND CUSTOMER BEHAVIORS TO HELP

BARNEYS GENERATE MORE PROFITS

ASSUMPTION IN CUSTOMER SEGMENTATIONThe 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

METHODOLOGYSegment 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

SPSS Customer Segmentation Results100K 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 Method1. Amex2. Discover3. MasterCard4. Visa

Channel1. Phone2. In- Store3. Website

Highlights of the SPSS Customer Segmentation Results

Cluster 1; 5128492.86000009;

50%

Cluster 2; 1094995.41; 11%

Cluster 3; 1118466.09; 11%

Cluster 4; 1884696.28; 18%

Cluster 5; 791138.060000002;

8% Cluster 6; 176439.06; 2%

TOTAL PROFIT DISTRIBUTION

CLUSTER 1; 65.0890047212926;

1%

CLUSTER 2; 217.433560365369;

4%

CLUSTER 3; 621.02503609106;

10%

CLUSTER 4; 143.617791663492;

2%

CLUSTER 5; 716.610561594204;

12%CLUSTER 6; 4303.39170731707;

71%

AVERAGE PROFIT OF EACH CUSTOMER

Highlights of the SPSS Customer Segmentation Results

CLUSTER 1

CLUSTER 2

CLUSTER 3

CLUSTER 4

CLUSTER 5

CLUSTER 6

1

2

3

Most Popular Shopping ChannelCLUSTER 1

CLUSTER 2

CLUSTER 3

CLUSTER 4

CLUSTER 5

CLUSTER 6

0

2

4

Most Popular Payment Method

1. Amex2. Discover3. MasterCard4. Visa

1. Phone2. In- Store3. Website

Highlights of the SPSS Customer Segmentation Results

CLUSTER 1

CLUSTER 2

CLUSTER 3

CLUSTER 4

CLUSTER 5

CLUSTER 6

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.0

0.9

3.7

3.0

1.7

3.2

ANNUAL PURCHASE FREQUENCY

2 MANAGERIAL IMPLICATIONS PART TWO

The Most Valuable Customer Segments: Cluster 3 & 6

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

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

3PREDICTIVE MODELING OF TOTAL PROFIT

PART THREEThe two selected customer segments with little similarity:

Cluster 1 & 2

METHODOLOGYDevelop 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

ASSUMPTIONS IN PREDICTIVE MODELING

TOTAL PROFIT

NUMBER OF ORDERS

NUMBER OF ITEMS

ONLINEPURCHASE

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.

. .

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.

0102

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

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.

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

0102 (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

THANK YOUAngie Wang