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