Desired%20Outcomes:%20Data%20Mining

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Desired Outcomes Data Mining

1. Explain the fundamental concepts and business
   uses of data mining
2. Describe the critical aspects of customer data
   for marketing analytics
3. Understand the role of predictive modelling in
   business
4. Build a predictive model
5. Demonstrate the ability to select appropriate
   techniques for solving business problems
6. Understand the importance of customer
   segmentation

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What this course will NOT do

• Teach you all the statistics you need to do data
  mining
• Replace real-world experience analyzing databases
• ? Turn you into an immediate data mining
  practitioner

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What this course will do

• Help you understand how and when to use data
  mining
• Assist you in talking to data miners (internally
  or externally)
• Begin your training as a data miner

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Intro to Data Mining

• MARK2039
• Spring 2005
• George Brown College

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What is Data Mining?

• The process of exploration and analysis, by
  automatic means, of large quantities of data to
  discover meaningful patterns and rules
• What does this mean from a business standpoint ?
• Capitalization of above learning to maximize ROI
  for a given business process.

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What is Data Mining? Continued...

• Data Mining is revolutionizing business today
• The old business paradigms are no longer
  acceptable
• Companies recognize their information as a
  critical asset
• The most successful companies in the coming
  millennium will be able to intelligently utilize
  this information for profit-maximization
  decisions

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Why the Growth in Data Mining?

• Marketers are no longer revenue-driven, but ROI
  driven
• Organizations have are becoming customer centric
  vs. product centric
• Too much noise and confusion in the market place
• Societal changes include
• Consumers are time conscious
• Emphasis on quality and value
• Aging population
• Emphasis on What's in it for me
  ?

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Why the Growth in Data Mining?

• Technological Changes
• Increased storage and processing capacity within
  a constantly cost-reduction environment
• Increased use of statistical tools and software
  for enhancing business decision-making
• One-to-One Marketing is becoming the norm
• Increased emphasis on developing customer loyalty
  programs
• Information represents a critical requirement in
  developing customer loyalty programs
• Mining the above information intelligently is
  the key towards successful customer loyalty
  programs.
• The Web
• Easy and timely access to large volume of data
  

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Data Mining as a Profession

• The most important asset for successful data
  mining is people.
• Successful hiring factors to look for are
• Quantitative skills
• Business and problem-solving skills
• Programming skills
• Knowledge of data structure, file structure,
  system structure and their integration
• Communication skills and ability to liase with
  marketing and systems departments

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Common Software

• SAS (Enterprise Miner, Base SAS)
• SPSS
• IBM Intelligent Miner
• Angoss Knowledge Studio

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Common applications

• Fraud detection
• Direct marketing
• Call analysis
• Customer segmentation
• Drug testing
• Quality control
• Credit scoring
• Click stream analysis

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Common Marketing Applications

• 1) Acquisition of new customers.
• 2) Developing Up-Sell strategies
• 3) Developing Cross-Sell strategies
• 4) Reducing customer defection
• 5) Creation of target customer groups for
  existing customer marketing programs
• 6) Campaign management analysis
• 7) Identifying high value and high potential
  value customers
• 8) Product affinity and bundling analysis
• 9) Retail site location analysis and product
  distribution analysis

One of the primary objectives of data mining is
to align marketing investment with customer
potential.
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Improving Business Results

• Data Mining is about identifying opportunities to
  improve business results.
• This may be achieved by identifying segments of
  customers that outperform others based on certain
  business objectives (an objective function)
• For example, the results from the predictive
  model below identifies customers more or less
  likely to respond to a particular DM offer.

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Mass marketing. Same investment for all customers

• High
• Marketing
• Investment
• /Customer
• Low
• Low Customer Value / Potential
  High

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Align marketing investment with customer potential

• High
• Marketing
• Investment
• /Customer
• Low
• Low Customer Value / Potential
  High

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Different objectives gt Different approaches

• Directed data mining
• When you know what you are looking for.
• e.g. Produce a predictive model to identify
  customers most likely to respond.

• Undirected data mining
• A process of discovery.
• e.g. What can the data tell us about customers?

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Example Which are these?

• Predicting the likelihood of response in the
  next campaign
• Analyzing call logs to determine which are
  complaints
• Determining the data mining strategy for the
  next year
• Why are sales decreasing in the last 3 years
• Assigning a likelihood of default score on a
  mortgage applicant
• Grouping customers together into segments

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Four Stages of Data Mining

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The Data Mining Process - Problem Identification
Stage

• 1)Problem Identification

Role of Marketer
Role of Data Miner
Role of Systems
Identification and Prioritization of business
strategy components which can be resolved
through data mining
Provide information regarding current data
environment
Identify overall business strategy
Example Improve retention results. What is the
data mining impact?
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The Data Mining Process Creation of the
Analytical File
Role of Marketer
Role of Data Miner
Role of Systems
Conduct preliminary datadiagnostics-source
file extractions-Data Dumps-Determination of
links and keys between files-Frequency
distributions on all fields on all files

Acts as Data Consultant toData Miner-Data
Dictionary-File Layouts-Star Schema-Data
Nuances /Interpretations
Understand sourcesof data that are used in data
mining project
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The Data Mining Process Application of Data
Mining Techniques

• Role of Data Miner/ Analyst
• Design appropriate reports to communicate final
  data mining solution and its expected performance
• Consult and advise on how data mining solution
  should be used and tracked in future campaign

Role of Marketer

• Have clear understanding of the key information
  within data mining solution
• Have clear understanding of how data mining
  solution performs from business perspective
• Have clear understanding of how to use data
  mining solution in future campaign

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The Data Mining Process Implementation
Role of Marketer
Role of Data Miner
Role of Systems
Review current results of solution vs. results of
solution achieved through development
Apply solution to database for upcoming campaign
Assist or run program to apply data mining
solution to database for upcoming campaign
Validate application of learning by checking
random dump of 10 records
Produce results
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What is the impact of data mining

• First Example Increase number of orders from
  100000 to 200000. Is this caused by data mining
• Second Example Increase the order rate per
  customer from 1 to 2 with total orders
  decreasing by 100000. Is this caused by data
  mining
• A third example to illustrate the impact of data
  mining

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Problem Identification

• How does data mining impact the business?
• Example 1 Direct Mail Campaign to 500000
  customers. Promotion cost per piece is 1.00
• Assume data mining can bring 10 improvement in
  performance for all campaigns. What is the
  potential data mining impact here?
• What other metric do we need to think of ?

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Problem Identification

• How does data mining impact the business?
• Example 1 Direct Mail Campaign to 500,000
  customers. Promotion cost per piece is 1.00

Note the calculation is an opportunity cost. It
calculates the additional promotional
cost to achieve 5500 responders without data
mining.
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Problem Identification

• Example 2 Outbound telemarketing campaign to
  300,000 customers. Promotion cost person is
  6.00

• Example 3 Email campaign to 1,000,000
  customerswith cost per promotion of .10

Of the three examples, which campaign would you
focus your data mining activities on?
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Identifying data mining opportunitieswithin your
organization

• Explore the organizations key business challenges
  
• Determine if improved customer/prospect targeting
  or segmentation would improve results
• Review the following questions
• Are the overall business results reasonable?
• Is the product or service in a stable business
  environment?
• What is the current data environment?
• What type of budgets are available?
• What type of margins does the product or service
  contribute to the organization?
• How many customers or prospects do you currently
  target?
• Will the results of your data mining exercise be
  actionable based on the results you are trying to
  improve?

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Example 1-Identifying Data Opportunities

• Company A has a 10,000 customers enrolled in a
  service that is renewed on an annual basis. Each
  year only 10 of all customers renew their
  service. Their renewal rates for other products
  and services averages 70.Should data mining be
  used to improve retention?

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Example 2-Identifying Data Opportunities

• Company B has a 1,000,000 customers and has been
  cross selling a long distance phone plan for over
  2 years. Over the last 6 months acquisition
  results have decline and the cost per new plan
  member has increased beyond target levels.
  Should data mining be used to improve results?
• Give me an example of a data mining solution?

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Example 3-Identifying Data Opportunities

• Art vs. Science
• Retail Company collect no information on its
  customers. Market research has indicated that the
  key drivers of purchase behaviour are high
  income, female immigrants.
• No individual-level information
• Information is available only at aggregate or
  postal code level
• Advantages of using advanced statistical
  techniques are minimized within this data
  environment.
• Quicker and simpler solutions will suffice.

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Example 3-Identifying Data Opportunities

• The Solution
• Using an RFM index approach, create postal code
  index based on three Statistics Canada Variables
• Median taxfiler income of postal code
• of population female within postal code
• of population landed immigrants within postal
  code

Income Female Landed Immig.
Average Postal Code 40,000 52 5
M5A 1J2 50,000 60 10
Index 1.25 1.15 2
The index for M5A 1J2 is (.33 x 1.25)(.33 x
1.15)(.33 x 2) 1.45
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Example 3-Identifying Data Opportunities
This index scheme can then be used to score each
postal code. The 800000 postal codes in Canada
are then ranked into 20 half decilesbased on
descending index score.
How would you use this above tool?
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Example 4

• An SVP of a large bank has spent thousands of
  dollars creating a credit card response model.
• The predictive model identifies those who are
  most likely to respond to the banks next offer.
• The model will allow the bank to save
  considerable money mailing only 20 of the
  prospects, they will generate 70 of all the
  responders.

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Example 4 (continued)

• But I need the maximum number of responders
• Attaining even 70 of the responders will not
  meet the campaign expectations
• What is the real problem here
• ? Data Mining is not always necessary