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Transcript of MGS4020_10.ppt/Apr 16, 2013/Page 1 Georgia State University - Confidential MGS 4020 Business...
MGS4020_10.ppt/Apr 16, 2013/Page 1Georgia State University - Confidential
MGS 4020
Business Intelligence
Data Mining and Data Visualization
Apr 16, 2013
MGS4020_10.ppt/Apr 16, 2013/Page 2Georgia State University - Confidential
Agenda
ExampleData MiningMarketing
Analytics
MGS4020_10.ppt/Apr 16, 2013/Page 3Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 4Georgia State University - Confidential
OLAP – Online Analytical Processing
• MOLAP – Multidimensional OLAP
Data Warehouse/ Data Mart
RDBMS
• ROLAP – Relational OLAP
MGS4020_10.ppt/Apr 16, 2013/Page 5Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 6Georgia State University - Confidential
Market Basket Analysis
• 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
MGS4020_10.ppt/Apr 16, 2013/Page 7Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 8Georgia State University - Confidential
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.
MGS4020_10.ppt/Apr 16, 2013/Page 9Georgia State University - Confidential
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”
MGS4020_10.ppt/Apr 16, 2013/Page 10Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 11Georgia State University - Confidential
Agenda
Data Mining ExampleMarketing
Analytics
MGS4020_10.ppt/Apr 16, 2013/Page 12Georgia State University - Confidential
Why use Analytics?
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
MGS4020_10.ppt/Apr 16, 2013/Page 13Georgia State University - Confidential
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?
MGS4020_10.ppt/Apr 16, 2013/Page 14Georgia State University - Confidential
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 numbersBusiness 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 ReacquisitionRetention
MGS4020_10.ppt/Apr 16, 2013/Page 15Georgia State University - Confidential
Direct Marketing Campaign Platform
ACQUIRE
RETAIN
REACTIVATE
“FIRE”
STORE DIFFERENT CHANNELS
A C T I V A T I O N P R O M O T I O NA C T I V A T I O N P R O M O T I O N
E-mail Address
Vehicles:
• Statements
• Newsletters
• Inserts
• Direct mail
• Personalized kits
• Telephone
Vc Cost to reactivateIf:
Vc < Cost to reactivateIf:
Ugly Postcard???
TestArea
• POS
• Partners
• Advertising
Vehicles:
• Direct Mail
• Statements
Triggered Promotions
highest value
customers
lowest value
customersdowngrade
trigger *
(for example)Days since last purchase = X
X = 30 days for PTNM
X = 60 days for GOLD
X = 120 days for CLUB
Direct Marketing Campaign Platform
PURCHASED
NO PURCHASE
PURCHASE
* < 1 purchase in last 12 mo
If : Time since inactive = X, and
Point balance > X
MGS4020_10.ppt/Apr 16, 2013/Page 16Georgia State University - Confidential
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:
MGS4020_10.ppt/Apr 16, 2013/Page 17Georgia State University - Confidential
Analytics Process
DISCOVERY DATA PREPARATION
KNOWLEDGE DEVELOPMENT
LEVERAGING ANALYTICS
POST ANALYSIS
OPPORTUNITIES
IDENTIFYING
SCOPING
OBJECTIVE SETTING
DATA WAREHOUSE
EXTERNAL DATA APPEND
DATA EXTRACTION
DATA VALIDATION
STATISTICAL MODELING
SEGMENTATION
OFFER OPTIMIZATION
CUSTOMER BEHAVIOR SCORING
DIRECT MAIL
TELEMARKETING
LOYALTY CAMPAIGN
RESULTS DECOMPOSITION
REFININGANALYTICS
FEEDBACK
HYPOTHESISTESTING
DEVELOPINGHYPOTHESES
EFFORT
FEEDBACK FOR
MGS4020_10.ppt/Apr 16, 2013/Page 18Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 19Georgia State University - Confidential
Agenda
Data Mining ExampleMarketing
Analytics
MGS4020_10.ppt/Apr 16, 2013/Page 20Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 21Georgia State University - Confidential
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 '01-01-2001' AND '06-30-
2002' THEN 1 ELSE 0 END) AS ONE_PLUS_STAYS,SUM (CASE WHEN EVENT.CHECK_OUT_DATE BETWEEN '07-01-2002' AND '12-31-
2003' THEN 1 ELSE 0 END) AS ZERO_STAYS
MGS4020_10.ppt/Apr 16, 2013/Page 22Georgia State University - Confidential
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' )
MGS4020_10.ppt/Apr 16, 2013/Page 23Georgia State University - Confidential
SQL
GROUP BYMBR.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 ANDZERO_STAYS = 0
MGS4020_10.ppt/Apr 16, 2013/Page 24Georgia State University - Confidential
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
• Definition: The identification and grouping of consumers that share similar characteristics
• Process:
• Data Selection
• Missing Values
• Standardization
• Removal of Outliers
• Cluster Analysis Considerations
MGS4020_10.ppt/Apr 16, 2013/Page 25Georgia State University - Confidential
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
MGS4020_10.ppt/Apr 16, 2013/Page 26Georgia State University - Confidential
Clustering
ProspectBase
ProspectBase
Midscale / Leisure Traveler
Midscale / Leisure Traveler
Upscale / Leisure Traveler
Upscale / Leisure Traveler
Country Club /
Resort Set
Country Club /
Resort Set
Midscale / Business Traveler
Midscale / Business Traveler
Upscale / Business Traveler –
Prosperous Traveler
Upscale / Business Traveler –
Prosperous Traveler
OtherOther
Upscale / Business Traveler –
Loan Dependent
Upscale / Business Traveler –
Loan Dependent
MGS4020_10.ppt/Apr 16, 2013/Page 27Georgia State University - Confidential
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
7
7
Household Income (,000)
48
45
102
73
71
72
Weekday Check in 13
1
3
6
2
3
Weekend Check in 69
6
29
51
7
30
No. Stays (resort) between Jan 1, 2001 and Jun 30, 2002
0
5
6
5
3
2
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
2
10
15
8
7
MGS4020_10.ppt/Apr 16, 2013/Page 28Georgia State University - Confidential
Cluster Analysis
Cluster Population % Resp. Index Avg. Profit
A 6 250 (75)
B 16 30 5
C 5 110 48
D 8 175 86
E 7 80 (5)
.
. . .
.
. . .
All 100 100 35
MGS4020_10.ppt/Apr 16, 2013/Page 29Georgia State University - Confidential
Cluster Analysis
Cluster 1 Cluster 1 Cluster 1------------
Calculate Scores
(ROI, Response, Utilization)
Overlay Profitability Estimate
Evaluate Risk-Return Tradeoff (by Offer and by
Cluster)
Make Final Selections
DM/Offer 1 DM /Offer 2 DM /Offer N--------
LowRETURNHigh
Low
RISK
High
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