Data Mining Report for Blackwell’s E-commerce Team

RESULTS AND RECOMMENDATIONS

Our Mission

With this project we were tasked with using data mining methods to investigate customer buying patterns and assist in making cross-selling recommendations.

Our Data

Data from Blackwell

Age

Amount of Items bought

Amount spent

Region

Purchase made In-Store or Online

Data Processing Highlights Age separated into four target groups

Older Baby Boomers (68-85)

Younger Baby Boomers (52-67)

Generation X (35-51)

Millennials (18-34)

Region separated into East, West, South, and Central

Data Mining Objectives

Investigate the relationship between region of purchase and amount spent per transaction

Investigate the factors that predict the amount spent per transaction Understand the correlation between the age of a customer and

region Make cross-selling recommendations based off products already

purchased Decide if we can use the region of purchase to predict the customers

age

Relationship Between Region and Amount Spent

23%

8%

29%

40%

Sales Per Region

East Region

West Region

South Region

Central Region

What we learned: Central region spends most at $1284.05 West Region spends the least at $252.11

Recommendation: Compare and contrast results to

understand why Central Region spends so much more than other regions

Use marketing techniques and strategies in all regions to emulate the Central Region

Amount Customer Will Spend by Region

What we learned: In-store spending as a whole is much

higher than online spending Central Region online spends almost twice

the amount as second highest region

Recommendation: Focus on mirroring cross-selling

recommendations from in-store to online Consider adding brick and mortar stores to

the West Region to penetrate market East R

egion

Onli

ne

West R

egion O

nline

South R

egion O

nline

Centra

l Reg

ion O

nline

In-store

$-

$200.00 $400.00 $600.00 $800.00

$1,000.00 $1,200.00 $1,400.00 $1,600.00

$526.00

$253.00

$521.00

$1,023.00

$1,542.00

Regional Consumer Transaction Forecast

Predict Age Based on Shopping Method

18-34 35-51 52-67 68-850

5000

10000

15000

20000

25000

30000

35000

1283516305

82992561

9619

13418

10820

6143

In-Store Online

What we learned: 90% Success 10% Risk Key demographics (18-34,35-51) shop

over 25% more in-store

Recommendation: Offer cross-promotions for in-store

shoppers to make purchases online Focus marketing strategies towards key

demographics

Promoting Cross-Selling Recommendations

Report

Successfully recommended additional products to customers based off data from a limited product list

Conducted an attribute evaluation and included a merit metric to rank stronger correlations

Included items with direct correlation and separated irrelevant items with lower scores

Recommended other products for Blackwell Electronics to consider adding in their inventory

Report included rationale for each product given to help understand cross-selling decisions

Recommendations for Alienware AAR4-10000BK

Predict the age of a customer in region

What we learned: 40% confidence rate which is too low to consider

successful The Central Region has virtually no 18-34 and 35-

51 year olds

Recommendation: Offer more specific data sets, perhaps including

income, date, or items purchased, to help reduce error in predictions

Establish new marketing techniques in Central and South region to focus on 18-34 and 35-51 year olds18-34 35-51 52-67 68-85

0

5000

10000

15000

20000

25000

30000

4913 63633573

1151

2314

58445699

6143

4849

6479

5262

1410

10378

11037

4585

0

Age Breakdown per Region

East Region West Region South Region Central Region

Objectives Completed

Lessons learned:• Regional Customer Spending Trends

• Marketing Analysis and Direction

• Inventory Expansion

Results:Higher customer satisfaction rate and profitability for Blackwell Electronics

Successfully investigated the relationship between region of purchase and amount spent

Successfully able to predict how much a customer will spend in each region

Established age of customer based on shopping method

Successfully recommended particular products to launch

Consider this, with the proper data, here are some more questions data mining can answer: CAN WE PREDICT CUSTOMER PATTERNS AND THE DIRECTION THEY WILL

TAKE? HOW SHOULD WE DEFINE MARKETING GROUPS? HOW DO WE SEPARATE PROFITABLE CUSTOMERS FROM UNPROFITABLE

CUSTOMERS? COULD WE USE BUYING PATTERNS AS AN EARLY DETECTION SYSTEM

FOR FRAUD? CAN WE PREDICT NEW SHOPPING TRENDS BEFORE THEY HAPPEN?