Business Intelligence
/Decision Models
Dr. Richard Michon
TRSM 1-040
Ext. 7454
rmichon@ryerson.ca
www.ryerson.ca/~rmichon/mkt700
Leaving Traces all over the
Place
Leaving Traces all over the
Place
Social medias, SMS, emails, Google,
Web browsing
 e-Commerce, shopping baskets,
basket abandonment, showrooming
 Payment, CC, PayPal, banking,
paying bills, AirMiles
 Delivery
 Support registration and guarantees
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Small Stores
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Recognition
Service
Friendship
Information
Small Stores
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Notice
Remember
Learn
Act
Larger Organizations
Notice: Transaction files
 Remember: Data warehousing
 Learn: Data mining
 Act: CRM
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OLTP and Warehousing
Analytical and Operational
CRM
8
CRM Architecture
9
Online Analytical Process
Data Mining vs. OLAP
OLAP
 Deliver key facts
based on hist. data:
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Data Mining
 Deliver the reasons or
drivers of those facts
KPIs
Core Business Metrics
Factual Reporting
Visualization driven
analysis – reporting
bases
See what happened in
the past
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Goal driven analysis
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Predict what’s going
to happen in the
future
Data Mining and Statistics
Approach
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Statistical Analysis
 Tests for statistical
correctness of models
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Data Mining
 Data Driven
• No assumption required
• If it works and makes some
sense, let’s use it
• Are statistical assumptions
of models correct?
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Hypothesis testing
• Is the relationship
significant?
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Tends to rely on
sampling
Techniques are not
optimized for large
amounts of data
Requires strong
statistical skills
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Focus on data exploration
Can find patterns in very
large amounts of data
Focus on Deploying
Results
Requires understanding of
data and business
problem
Analytics Usage
Analytics Challenges
Retail
Long Tail Effect
Vol. & Demand
A
C
B
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
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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
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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
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Business Intelligence and Decision
Modeling
Analytics Usage
Analytics Challenges
Multidisciplinary Domain
MKT
IT
Data
Scientists
Course Material
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Reading posted on course website
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SPSS Tutorials
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SPSS and Excel Datasets
Course Evaluation
Midterm exam
Final exam*
Labs **
Percent
33%
33%
33%
Total
100%
* Must pass
** No makeups
Grading
F D- D D+ C- C C+ B- B B+ A- A A+*
-2
-1
0
Z Score = (X – Mean) / SD
* Approximate Scale
+1
+2
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
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