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WHAT IS DATA MINING?

WHAT IS DATA MINING?. Data mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. Need for analysis & predictive modeling Massive data sets e-Business motivation. DATA MINING TASKS.

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WHAT IS DATA MINING?

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  1. WHAT IS DATA MINING? Data mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. • Need for analysis & predictive modeling • Massive data sets • e-Business motivation

  2. DATA MINING TASKS • Classification, e.g. low, med, or hi risk credit card applicant • Estimation, e.g. probability of paying off a home equity loan => convert to 0/1 • Prediction, e.g. predicting churn, who will respond,

  3. (data mining tasks – cont. • Affinity grouping or association rules, e.g.cross-selling opportunities, product placement, etc. • Clustering, e.g. grouping like customers with no pre-set categories • Description & Visualization, e.g.intuitive graphical display of data for meaningful interpretation.

  4. DATA MINING FOR MARKETING & CRM • Reduced costs, e.g. promotions targeted to relevant customers. • Increased revenue, e.g. cross-sell and spot most profitable customers, up-sell • 0ne-to-one marketing – ability to implement • Proactively anticipating customer needs and addressing them

  5. DATA MINING & DECISION SUPPORT • Data mining is one DM tool. • More than retrieval, intelligent queries • Database requirements • Transaction based vs. historical • Decision Support Fusion and the VP of Marketing = YOU!!!

  6. SOCIETAL ISSUES • Privacy • Data Ownership • Appropriate Use • Implications of getting too good.

  7. WHO ARE YOUR BEST CUSTOMERS???? • The Concept is very simple (RFM): • Your best customers are those who: • Have brought from you most recently • Buy from you most frequently • Spend a lot on your products/services

  8. Recency • Most recent date customer has made a purchase from you. • Most studies indicate that those consumers that have purchased from you most recently are more likely to respond positively to a new offer/promotion.

  9. Frequency • How many times they have purchased from you since their initial purchase or some set date. • Customers who buy from you many times are more likely to respond positively to a new offer/promotion. • Warning: those that have just recently purchase may not have a lot of frequency but represent long term potential.

  10. Monetary • Total dollar value of customer purchases since they first started buying from your company. • Amount of total purchases • Amount of purchases in last 12 months. • Amount of average purchase.

  11. Creating RFM Codes • Excellent scoring/segmentation system • Convert raw values to categorical codes, e.g. divide recency days into five equal groups (code 1-5). • Simplifies profiling process and processing speed. • Some loss of information.

  12. Using RFM Cells to Predict Response • Historical Data Overlay • Test Group, e.g. 40000 out of 1 million customers, sent promotion/communication. • Gauge response by RFM Group • Demographic profiles of high response group for projections.

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