1. MGS 4020 Business Intelligence Data Mining and Data Visualization Jul 17, 2018

2. Agenda
Data Mining
Marketing Analytics
Example

3. What is Data Mining?
A set of activities used to find new, hidden, or unexpected patterns in data
Verification versus Discovery
Accuracy in predicting consumer behavior

4. OLAP – Online Analytical Processing
MOLAP – Multidimensional OLAP
Data Warehouse
/ Data Mart
RDBMS
ROLAP – Relational OLAP

5. Techniques and Technologies
Techniques Used to Mine the Data
Classification
Association
Sequence
Cluster
Data Mining Technologies
Statistical Analysis
Neural Networks, Genetic Algorithms and Fuzzy Logic
Decision Trees

6. Market Basket Analysis
Most common and useful in Marketing
What products customers purchase together
Diapers and Beer sell well on Thursday nights
Benefits
Better target marketing
Product positioning with stores (virtual stores)
Inventory management
Limitations
Large volume of real transactions needed
Difficult to correlate frequently purchased items with infrequently purchased items
Results of previous transactions could have been affected by other marketing promotions

7. Market Basket Analysis
Association Rules for Market Basket Analysis
All associations are unidirectional and take on the following form:
Left-hand side rule IMPLIES Right-hand side rule
Left and Right hand side can both contain multiple items (Multi-dimensional Market Analysis)
Examples:
Steak IMPLIES Red Wine
Hunting Magazines IMPLIES Smokeless Tobacco

8. Market Basket Analysis
3 Measures of Market Basket Analysis
Support – the percentage of baskets in the analysis where the rule is true
Of 100 baskets 11 contained both steaks and red wine.
11% support
Confidence – the percentage of Left-hand side items that also have right-side items
Of the 17 baskets that contained steak, 11 contained red wine.
65% confidence
Lift – compares the likelihood of finding the right-hand item in any random basket
Also referred to as Improvement
Lift of less than 1 means it is less predictive than random choice
If Confidence is 35%, but the right-hand side items is in 40% of the baskets, the rule offers no Improvement of random selection.

9. Market Basket Analysis
Market Basket Analysis results can be:
Trivial
Hot Dogs IMPLIES Hot Dog Buns
TV IMPLIES TV Warranty
Inexplicable
Virtual Items – Associating non-items or other attributes into the correlation study
“New Customer”

10. Limitations of Data Mining
All relevant data items / attributes may not be collected by the operational systems
Data noise or missing values (data quality)
Large database requirements and multi-dimensionality

11. Agenda
Data Mining
Marketing Analytics
Example

12. Why use Analytics? 15% to 51%+ increase in net sales
Some Benefits Are Quantifiable
15% to 51%+ increase in net sales
ROI of over 2500%
Annual increm revenue of > $178mm
For one product over a 3 yr period, $650mm in cost savings & over $350mm in increm contribution
>50% more accurate targeting of likely residential movers
24% reduction in churn rate from modeling/targeting likely churners
Other Benefits Not So Easily Quantified
Decisions based on exhibited behaviors
Makes data actionable
Easier to measure results
Validate instincts and opinions
Enhanced what-if analysis & planning
Less guesswork, more facts
Built-in process improvement

13. Advanced analytics can help to answer the following questions …
How do I determine which offers to make to my customers?
What do my best customers look like, and where can I find more of them?
What is the return on my marketing investment? How might my marketing plans be tweaked to optimize investment?
Who are my most valuable customers? What are my key value drivers?
Which of my customers have the greatest potential for growth – and which have little or no potential?
Which of my customers are most vulnerable? What are the triggers causing them to leave or churn?
Where should I employ my assets to meet customer demand?

14. Marketing Analytics Landscape
Where can I find new customers?
Where can I find more revenue & profit from my current customers?
Which of my customers are at risk and how can I keep them?
Which customers do I want to win back?
Strategy & Tactics: Guiding the business & helping to make numbers
Business Planning, Forecasting, Corp Strategy, Financial Metrics, Profitability Analysis
Customer Knowledge – Who are my customers?
Segmentation & Profiles, External Data, Mkt Share/Wallet Share, Channel Preference Modeling
Customer Acquisition
Prospect profiling
Event driven marketing
Propensity to buy & response modeling
Marketing Optimization
Market Basket Analysis
Online and Retail Channels
Customer and product churn modeling
Retentive stickiness of key products
Prediction of key events (eg, residential movers)
Customer reacquisition
Customer profitability analysis
Acquisition
Growth
Reacquisition
Retention

15. Direct Marketing Campaign Platform

16. General Data Mining Methods
Predicting which customers will purchase, based on demographics, psychographics, firmographics, service history, transactions, credit history, etc. Statistical algorithms and decision trees are used for these problems with much success.
Market Basket Analysis: which customers who purchase an additional telephone line are also likely to purchase dialup internet service? Pattern matching works well: associative rules, fuzzy logic, neural networks.
Which types of activities precede each other; eg, do customer hospitality and gaming activities show patterns or sequences? We use a combination of statistical modeling and simulations to identify these trigger points for action, and to estimate the marginal value of each.
Clustering is useful for determining similar groups based on how closely they resemble each other. Multitude of clustering techniques exist, with the primary difference being in how they define what is “close”. Clustering can be very useful for marketing messaging and advertising, strategy development and implementation, and channel development.
Classification:
Association:
Sequencing:
Clustering:

17. Analytics Process DISCOVERY DATA KNOWLEDGE LEVERAGING POST PREPARATION
DEVELOPMENT
ANALYTICS
ANALYSIS
FEEDBACK
IDENTIFYING
HYPOTHESIS
CUSTOMER
OPPORTUNITIES
DATA WAREHOUSE
TESTING
BEHAVIOR
RESULTS
SCORING
SCOPING
EXTERNAL DATA
STATISTICAL
DECOMPOSITION
APPEND
EFFORT
MODELING
DIRECT MAIL
TELEMARKETING
OBJECTIVE
DATA EXTRACTION
SEGMENTATION
FEEDBACK FOR
SETTING
REFINING
LOYALTY
ANALYTICS
DEVELOPING
DATA VALIDATION
OFFER
CAMPAIGN
HYPOTHESES
OPTIMIZATION

18. Summary Analytics allow quantifiable, intelligent decision making
Analytics can be leveraged across all areas of a business
Different analytical methods apply to different situations
Modeling enables you to combine potential hundreds of factors into a single decision metric (or a few key scores/clusters)
Analytics are more powerful when tied to bottom line profitability

19. Agenda
Data Mining
Marketing Analytics
Example

20. InterContinental Brand Reactivation Promotion
Frequent travelers (points collectors) who had 1+ stays at InterContinental hotels in the US between Jan 1, 2001 and Jun 30, 2002.
Frequent travelers (points collectors) who had 0 stays at InterContinental hotels in the US between Jul 1, 2002 and Dec 31, 2003.
A set of activities used to find new, hidden, or unexpected patterns in data
Accuracy in predicting and reactivating these consumers behavior

21. SQL SELECT MBR.MEMBERSHIP_ID, MBR.FIRST_NAME, MBR.LAST_NAME,
MBR.ADDR_LINE_1,
MBR.ADDR_LINE_2,
MBR.ADDR_LINE_3,
MBR.ADDR_LINE_4,
MBR.ADDR_LINE_5,
MBR.CITY,
MBR.STATE_DESTINATION,
MBR.ZIP_CODE,
MBR.TYPE,
SUM (CASE WHEN EVENT.CHECK_OUT_DATE BETWEEN ' ' AND ' '
THEN 1 ELSE 0 END) AS ONE_PLUS_STAYS,
SUM (CASE WHEN EVENT.CHECK_OUT_DATE BETWEEN ' ' AND ' '
THEN 1 ELSE 0 END) AS ZERO_STAYS

22. SQL FROM MBR, EVENT, PROPERTY, XREF WHERE
( MBR.MEMBERSHIP_ID=XREF.MEMBERSHIP_ID )
AND ( PROPERTY.PROPERTY_ID=EVENT.PROPERTY_ID )
AND ( EVENT.MEMBERSHIP_ID=XREF.MEMBERSHIP_ID )
AND (
MBR.MARKET_REGION_CODE = '05388'
AND MBR.TYPE IN ('BASE','GOLD','PLTNM')
AND MBR.PREF_ALLIANCE_CODE = 'POINT'
AND PROPERTY.BRAND_MAJOR_CODE = ‘INTERCONTINENTAL'
AND PROPERTY.MARKET_REGION = 'US' )

23. SQL GROUP BY MBR.MEMBERSHIP_ID, MBR.FIRST_NAME, MBR.LAST_NAME,
MBR.ADDR_LINE_1,
MBR.ADDR_LINE_2,
MBR.ADDR_LINE_3,
MBR.ADDR_LINE_4,
MBR.ADDR_LINE_5,
MBR.CITY,
MBR.STATE_DESTINATION,
MBR.ZIP_CODE,
MBR.TYPE
HAVING
ONE_PLUS_STAYS >= 1 AND
ZERO_STAYS = 0

24. Cluster Analysis
Definition: The identification and grouping of consumers that share similar characteristics
Yields: better understanding of prospects/customers
Translates into: improved business results through revised strategies attributes
Process:
Data Selection
Missing Values
Standardization
Removal of Outliers
Cluster Analysis Considerations

25. Cluster Analysis Only want a small subset of variables for clustering
Weed out undesirable variables
Can use PROC FACTOR, PROC CORR
Can use expert system
Consideration for observations, weighting
Probably done with factor analysis
If not, then two options
Set Missing to Mean of data
Set Missing to Value of Equivalent Performance
No right or wrong answer
Might do both - depending on variables

26. Prospect Base Other Clustering Midscale / Business Traveler
Midscale / Leisure Traveler
Upscale / Business Traveler – Loan Dependent
Upscale / Leisure Traveler
Prospect
Base
Country Club / Resort Set
Upscale / Business Traveler – Prosperous Traveler
Other

27. Cluster Analysis Attribute Cluster Name A B C D E (ALL)
Age of Head of Household 38 62 48 44 52 43
Length of Residence in high income group zip codes 7 12 9 6
Household Income (,000) 45
102 73 71 72
Weekday Check in 13 1 3 2
Weekend Check in 69 29 51 30
No. Stays (resort) between Jan 1, 2001 and Jun 30, 2002 5
No. Stays (mid properties) between Jan 1, 2001 and Jun 30, 2002 11 55 21 15 32 16
No. Stays (upscale properties) between Jan 1, 2001 and Jun 30, 2002 24 10 8

28. Cluster Analysis

29. Cluster Analysis Cluster 1 Cluster 1 ------------ Cluster 1 Mail
Calculate Scores
(ROI, Response, Utilization)
Overlay Profitability Estimate
High
RETURN
Low
Evaluate Risk-Return Tradeoff (by Offer and by Cluster)
Low
Mail
Make Final Selections
RISK
No-Mail
High
DM/Offer 1
DM /Offer 2
DM /Offer N