1. Predicting Profitability
and Customer
Preference
Results and Recommendations

2. Data Mining
Objectives
 Objective 1: Predicting profitability of future products for
Blackwell Electronics using two different data analytics
methods
 Complete task with Similarity Analysis
 Complete task with Regression Analysis
 Objective 2: Predict which brand of computer Blackwell
Electronics customers prefer
 Use both actual and predicted data to solve the problem
 Use various methods to produce a high accuracylow risk result

3. Objective 1:
The difference
between
similarity and
regression
Similarity Analysis
We compare multiple lists of
numbers to evaluate their
similarity. To do this we
measure products and their
variables on a sliding scale.
The idea is similar objects
sell roughly the same.
Regression Analysis
Estimate the relationships
among variables to forecast
profitability of future products.
This is different than a
Similarity Analysis because it’s
a continuous prediction rather
than a classification.

4. Recommended
New Products
Method
Comparison
and Results
Similarity Analysis
$106,666.64
$0.00
$20,000.00
$40,000.00
$60,000.00
$80,000.00
$100,000.00
$120,000.00
Total Profit Gained
Product
Regression Analysis
$186,602.40
$0.00
$20,000.00
$40,000.00
$60,000.00
$80,000.00
$100,000.00
$120,000.00
$140,000.00
$160,000.00
$180,000.00
$200,000.00
Total Profit Gained
Product

5. Objective 2:
Brand
Preference
Report
How we did this
 Investigated customer responses to survey
questions
 Using data analytics methods, the goal was to
discover similarities between the data
 Used data analysis to recover 5000 missing
survey entries
 Combined 15,000 sets of data with the
outcome having an accuracy rating of almost
85%

6. Brand
Preference
Data and
Results
Customer Data
 Salary
 Age
 Education
 Car
 Zip Code
 Credit
Brand
25%
11%
41%
23%
Acer Completed Acer Predicted
Sony Completed Sony Predicted

7. Final Report
Recommendation
 Customers prefer
Sony Computers
to Acer Computers
by a margin of
nearly 2 to 1
 Pursue a deeper
strategic
relationship with
Sony

8. Summary of
Lessons
Learned for
Blackwell
Electronics
 Decided top five products to introduce into Blackwell’s
inventory
 Successfully predicted profits of said new products
 Demonstrated how Data Analytics method used nearly
doubles profits
 Predicting customer brand preference
 Increase customer satisfaction to help build loyalty
 Improve relationships with vendors based on product
marketability

9. Other Possible Uses for Data Analytics
Optimize inventory to eliminate products that don’t sell
Use click stream analysis to see what the customer is
looking at and how they arrived there
Understand how the customer uses the website to
help personalize the experience
Could use Sentiment Analysis in social media to
gather opinions and suggestions concerning Blackwell
Electronics
Use consumer data to extend credit to low risk
applicants