teaching data mining: the new “required competency” for marketing professionals today’s...

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

As the data grows…

What is Data Mining?

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)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2001 2006

Source: IDC. 2001

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

• Sales/marketing applications in framework of the customer lifecycle–Basis for “analytical CRM”

~75% of Data Mining applications are CRM

“Fewer than 50 percent50 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

Operational CRM isn’t enough

What analytical CRM does

More EfficientAcquisition

Longer LastingRelationship

More FrequentUp/Cross Sell

Time

Revenue

Loss

Less Loss

Profit

More EfficientAcquisition

More Profit

Longer LastingRelationship

More FrequentUp/Cross Sell

Time

Revenue

Loss

Less Loss

Profit

What analytical CRM does

More EfficientAcquisition

Longer LastingRelationship

Even More Profit

More FrequentUp/Cross Sell

Time

Revenue

Loss

Less Loss

Profit

What analytical CRM does

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 mid-

stream

• 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

Home Page

2

e-store

3

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

Education

no college college grad

Prediction & classification

Income

high incomelow income

Prediction & classification

Secured $50 Million of mortgage revenue through the use of an

accurate propensity model to target offers %

What data mining has done for…

Standard Life needed to expand its share of the increasingly competitive mortgage market

Saved 33% of targeted customers, reduced direct mail budget by 60% and increased usage and revenue

What data mining has done for…

Verizon Wireless needed to reduce customer churn and associated replacement costs

Achieved a 300% year-on-year rise in profits the first month they deployed models for personalization

What data mining has done for…

Sofmap needed to improve cross-selling to their web shoppers and…