Download Teaching Data Mining: The New “Required Competency” for

Teaching Data Mining:
The New “Required Competency”
for Marketing Professionals
Today’s Presenters:
Tom Nugent
Kenneth Elliott, Ph.D.
Industry trends
• Explosive data and information growth
• “Predict or perish!”
• Industry has higher expectations of new
graduates
• Soft economy means the most competitive
job market in years
What is Data Mining?
Discovering meaningful patterns in your
data
What is Data Mining?
As the data grows…
The relationships become
more complicated
What is Data Mining?
Data mining discovers meaningful
patterns in your complex data
Data mining is
• A user-centric, interactive process which
leverages analysis technologies and
computing power
“Computers and algorithms don’t mine
data; people do!”
Data mining is not
• Blind application of analysis/modeling
algorithms
• Brute-force crunching of bulk data
Size and Demand for
DM Software
Worldwide Data Mining Market ($M)
2000
1800
1600
1400
1200
1000
800
600
400
200
0
2001
Source: IDC. 2001
2006
Why data mining?
• Standard Life secured 50 million in
mortgage revenue
• Verizon Wireless retained 33% of targeted
customers, reduced direct mail budget by
60% and increased usage and revenue
• Softmap achieved a 300% year-on-year
rise in website profits the first month they
deployed models for personalization
Types of data mining
applications
•CRM: analytic applications designed to measure and optimize
customer relationships (e.g. customer profitability, retention,
marketing analysis)
•Financial/BPM: analytic applications designed to measure and
optimize financial performance (e.g. budgeting) and/or to
establish and evaluate an enterprise business strategy (e.g.
balanced scorecard).
•Operations/Production: analytic applications designed to
measure and optimize the production and delivery of a business’s
products and services (e.g. demand planning, workforce
optimization, inventory analysis, healthcare outcomes analysis).
Types of data mining
applications
• Student Relationship Management- change
the vocabulary
–Student Retention/Acquisition
–Enrollment Management
–Surveys
–Targeted Marketing
• Financial Aid Allocation
• Web Analysis
~75% of Data Mining applications are CRM
• Sales/marketing applications in
framework of the customer
lifecycle
–Basis for “analytical CRM”
Operational CRM isn’t enough
“Fewer than 50 percent of enterprise wide
CRM initiatives will generate payback by
2004.” Gartner Group
“Organizations that don’t embrace analytics
as a component of their CRM strategies
are ultimately going to fail at CRM.” Meta
Group
Operational CRM isn’t enough
“Data mining is a way to lift CRM projects
into a higher level of return on investment.”
Meta Group
What analytical CRM does
More Efficient
Acquisition
More Frequent
Up/Cross Sell
Profit
Revenue
Less Loss
Time
Loss
Longer Lasting
Relationship
What analytical CRM does
More Efficient
Acquisition
More Frequent
Up/Cross Sell
More
Profit
Profit
Revenue
Less Loss
Time
Loss
Longer Lasting
Relationship
What analytical CRM does
More Efficient
Acquisition
More Frequent
Up/Cross Sell
Longer Lasting
Relationship
Even More
Profit
Profit
Revenue
Less Loss
Time
Loss
Why data mining in marketing?
• How often do our best customers buy?
• What motivates customers to make
multiple purchases?
• How can we ensure long-term loyalty?
• How do we attract and retain new
customers?
• How can we personalize and align offers to
achieve maximum ROI?
CRM applications in marketing
• Understanding customers
– Quickly uncover the attributes that define customer
behaviors
– Profile customers to understand their needs and
desires
– Results in more relevant and targeted customer
communications
• For example…predict that a 31-year old single
male is likely to respond favorably to a
discounted travel offer every 6 months
CRM applications in marketing
• Develop targeted offers
– Identify propensities to
purchase certain products
– Maximize campaign
results through better
targeting
– Analyze past results to
predict future results
• For example…predict that a
22-year old woman who lives
in Chicago is very likely to
purchase a specific new
book release
CRM applications in marketing
• Match specific offers to
specific individuals
– Fine tune messages by
marketing channel
– Deliver offers based on
customer profile
– Results in increased
campaign ROI
• For example, predict that a
35-year old woman with two
children is likely to purchase
a new toaster every 2.5 years
CRM applications in marketing
• Execute real-time campaigns
– Assign scores based on
behavior
– Provide an immediate
offer based on customer
specifics
– Results in increased
response and long term
customer value
• For example, offer the money
market customer on the
phone a good rate on a
certificate of deposit, based
on their profile
CRM applications in marketing
• Monitor campaign results
–Determine how a campaign is doing
–Identify ways to improve response
–Maximize results by tweaking campaigns midstream
• For example, offer current cellular phone
customers the same offer as new
customers, based on feedback
Case studies
• Clustering
• Association
• Sequence association
• Prediction & classification
• SPSS customers
Clustering techniques
Clustering techniques
Clustering in Clementine
• Clustering is used
to find natural
groupings of cases
• The cluster results,
shown below, show
that certain groups
or “segments” have
a much higher
propensity to
respond
Association algorithms
+
=
Association algorithms
+
=
Sequence association
1
2
3
Home
Page
estore
Check-out
Page
Sample of sequence
association output

Results of sequence association indicate
which items and in what order have been
purchase.

We see here that if frozen meal and beer
were purchased on the last visit, then
frozen meal will be purchased on the
next visit with a confidence of 87.1%
Prediction & classification
Prediction & classification
Education
no college
college grad
Prediction & classification
Income
low income
high income
What data mining has done
for…
Standard Life needed to expand its
share of the increasingly
competitive mortgage market
Secured $50 Million of mortgage
revenue through the use of an
accurate propensity model to target
offers %
What data mining has done
for…
Verizon Wireless
needed to reduce
customer churn and
associated replacement
costs
Saved 33% of targeted customers,
reduced direct mail budget by 60%
and increased usage and revenue
What data mining has done
for…
Sofmap needed to improve crossselling to their web shoppers and…
Achieved a 300% year-on-year rise in
profits the first month they deployed
models for personalization