Business Intelligence /Decision Models Dr. Richard Michon TRSM 1-040Ext

Leaving Traces all over the Place

Social medias, SMS, s, Google, Web browsing e-Commerce, shopping baskets, basket abandonment, showrooming Payment, CC, PayPal, banking, paying bills, AirMiles Delivery Support registration and guarantees

Small Stores Recognition Service Friendship Information

Small Stores Notice Remember Learn Act

Larger Organizations Notice: Transaction files Remember: Data warehousing Learn: Data mining Act: CRM

OLTP and Warehousing

8 Analytical and Operational CRM

9 CRM Architecture

Online Analytical Process

Data Mining vs. OLAP OLAP Deliver key facts based on hist. data: KPIs Core Business Metrics Factual Reporting Visualization driven analysis – reporting bases See what happened in the past Data Mining Deliver the reasons or drivers of those facts Goal driven analysis Predict what’s going to happen in the future

Data Mining and Statistics Approach Data Mining Data Driven No assumption required If it works and makes some sense, let’s use it Focus on data exploration Can find patterns in very large amounts of data Focus on Deploying Results Requires understanding of data and business problem Statistical Analysis Tests for statistical correctness of models Are statistical assumptions of models correct? Hypothesis testing Is the relationship significant? Tends to rely on sampling Techniques are not optimized for large amounts of data Requires strong statistical skills

Analytics Usage

Analytics Challenges

Retail

Long Tail Effect AB C Vol. & Demand Assortment

Services Get rid of intermediaries, keep control and reduce costs

Media Alternate business channels and reduce costs

Non-Profits Nothing like trying!

Data Mining: Set of Techniques Classification: Customer type, No fly list, Risk, Fraud, Terrorism Estimation: Household income, Lifetime Value, Life style Clustering: RFM, Typology, Segmentation Profiling: Decision Trees Prediction: Behavioral probability (0/1)

Data Mining Applied to CRM Prospecting: Prospects, Channels, Message Interactive mkt: Response models, Optimizing budgets and ROI Segmentation: Cultivation: Cross-sell, Upsell, Churn Reduction, Loyalty CLV: Credit Risk: Testing:

MKT 700 Course Specifics Business Intelligence and Decision Modeling

Analytics Usage

Analytics Challenges

IT Multidisciplinary Domain Data Scientists MKT

Course Material Reading posted on course website SPSS Tutorials SPSS and Excel Datasets

Course Evaluation Percent Midterm exam 33% Final exam* 33% Labs ** 33% Total100% * Must pass ** No makeups

Grading Z Score = (X – Mean) / SD F D- D D+ C- C C+ B- B B+ A- A A+* * Approximate Scale

Course Logic and Structure Logic 1. DBMKT, DM, CRM 2. RDBMS - SQL 3. CRISP - Data Preparation 4. CLV 5. RFM 6. Classification 7. Profiling 8. Predictive Modeling Actual 1. DBMKT, DM, CRM 2. RFM 3. RFM2, CRISP, Data Preparation 4. CLV 5. CLV2, RDBMS 6. Classification 1&2 7. Profiling 1 & 2 8. Predictive Modeling 1 & 2

Next Week RFM (Recency, Frequency, Monetary) SPSS RFM Tutorial