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 modelingMassive data setse-Business motivation

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,

(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.

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

DATA MINING & DECISION SUPPORT

Data mining is one DM tool.More than retrieval, intelligent queriesDatabase requirementsTransaction based vs. historicalDecision Support Fusion and the VP of

Marketing = YOU!!!

SOCIETAL ISSUES

PrivacyData OwnershipAppropriate UseImplications of getting too good.

WHO ARE YOUR BEST CUSTOMERS????

The Concept is very simple (RFM):Your best customers are those who:

Have brought from you most recentlyBuy from you most frequentlySpend a lot on your products/services

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.

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.

Monetary

Total dollar value of customer purchases since they first started buying from your company.

Amount of total purchasesAmount of purchases in last 12 months.Amount of average purchase.

Creating RFM Codes

Excellent scoring/segmentation systemConvert 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.

Using RFM Cells to Predict Response

Historical Data OverlayTest Group, e.g. 40000 out of 1 million

customers, sent promotion/communication.

Gauge response by RFM GroupDemographic profiles of high response

group for projections.