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Chapter16 Chapter16 Presentation Transcript

  • Chapter 16 Building the Data Mining Environment
  • The Ideal Customer-Centric Organization
    • Customer is king (not pauper)
    • For B2C (business to consumer) - Combination of point-of-sale transaction data and loyalty cards
    • For B2B (business to business) – traditional approaches (purchase orders, sales orders, etc.), Electronic Data Interchange (EDI) of same, Enterprise Resource Planning (ERP) software with intranet access for business partners
    • Customer interactions are recorded, remembered, utilized (action)
    • Corporate culture focused on rewards for how customers are treated
  • The Ideal Data Mining Environment
    • A corporate culture that appreciates the value of information
    • Committed (human and $ capital investment) to consolidate customer data from disparate data sources (ECTL – extract, clean, transform, load) which is challenging and time consuming
    • A corporate culture committed to being a Learning Organization which values progress and steady improvement
  • The Ideal Data Mining Environment
    • Recognize the importance of data analysis and its results are shared across the organization
      • Marketing
      • Sales
      • Operational system designers (IT or vendor software)
    • Willing to make data readily available for analysis even if it means some re-design of software
  • Reality (where “rubber meets the road”)
    • The ideal environments, organizations, and corporate culture rarely exist all in one organization!!!
    • Don’t be shocked…it’s hard work!!!
  • Building a Customer-Centric Organization
    • Biggest challenge to this is establishing a single view of the customer shared across the entire enterprise
    • Reverse of this is also a challenge – creating a single view of our own company to the customer
    • Consistency is needed for both the above
  • Building a Customer-Centric Organization Corp. Culture Data Mining Environment Single Customer View Customer Metrics Collecting the Right data Mining Customer data
  • Single Customer View
    • Customer Profitability Model
    • Payment Default Risk Model
    • Loyalty Model
    • Shared Definitions:
      • Customer start
      • New customer
      • Loyal customer
      • Valuable customer
    Figure 16.1 A customer-centric organization requires centralized customer data
  • Defining Customer-Centric Metrics
    • Business metrics guide managers in their decision-making
    • Selecting the right metrics is crucial because a business tends to become what it is measured by
      • New customers – tend to sign up new ones without regard to quality, tenure, profitability
      • Market share – tend to increase this at the expense of profitability
    • Easy to say customer loyalty is a goal…harder to measure the success of this
  • Collecting the Right Data
    • Data collection should map back to defined customer metrics
    • Customer metrics often stated as questions in need of answers:
      • How many times/year does customer contact our Customer Support (phone, web, etc.)?
      • What is payment status of customers (current, 30, 60, 90 days, etc.)?
      • Thousands of other questions
  • DM Environment & Mining Data
    • Data Mining group (team) is needed
    • DM Infrastructure to support is needed
  • Data Mining Group
    • Possible locations for such a group include
      • Part of I.T.
      • Outside organization – outsource this activity
      • Part of marketing, finance, customer relationship management
      • Interdisciplinary group across functional departments (e.g., marketing, finance, IT, etc.)
    • Each of the above have advantages and disadvantages
  • Data Mining Staff Characteristics
    • Database skills (SQL)
    • Data ECTL (extraction, cleaning, transformation, loading) skills
    • Hands-on with Data Mining software such as PolyAnalyst, SAS, SPSS, Salford Systems, Clementine, etc.)
    • Statistics
    • Machine learning skills
    • Industry knowledge
    • Data visualization skills
    • Interviewing and requirements gathering skills
    • Presentation, writing, and communication skills
    Cannot all be DM Rookies!
  • Data Mining Infrastructure
    • Ability to access data from many sources & consolidate
    • Ability to score customers based on existing models
    • Ability to manage lots of models over time
    • Ability to manage lots of model scores over time
    • Ability to track model score changes over time
    • Ability to reconstruct a customer “signature” on demand
    • Ability to publish scores, rules, and other data mining results
  • The Mining Platform (example)
    • Lots of architecture strategies – this is just one that includes OLAP also
  • Data Mining Software
    • Review “Questions to Ask” Side Bar in book on page 533 (2 nd edition)
  • End of Chapter 16