MGS4020_10.ppt/Apr 16, 2013/Page 1 Georgia State University - Confidential MGS 4020 Business Intelligence Data Mining and Data Visualization Apr 16, 2013

MGS4020_10.ppt/Apr 16, 2013/Page 1Georgia State University - Confidential

MGS 4020

Business Intelligence

Data Mining and Data Visualization

Apr 16, 2013


Agenda

ExampleData MiningMarketing

Analytics


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


OLAP – Online Analytical Processing

• MOLAP – Multidimensional OLAP

Data Warehouse/ Data Mart

RDBMS

• ROLAP – Relational OLAP


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


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



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



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.



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”


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


Agenda

Data Mining ExampleMarketing

Analytics


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


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?


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


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

• E-mail

• Telephone

Vc Cost to reactivateIf:

Vc < Cost to reactivateIf:

Ugly Postcard???

TestArea

• POS

• Partners

• Advertising

Vehicles:

• Direct Mail

• E-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


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:


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

EMAIL

LOYALTY CAMPAIGN

RESULTS DECOMPOSITION

REFININGANALYTICS

FEEDBACK

HYPOTHESISTESTING

DEVELOPINGHYPOTHESES

EFFORT

FEEDBACK FOR


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


Agenda

Data Mining ExampleMarketing

Analytics


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


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


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' )


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


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


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


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


Prosperous Traveler

OtherOther


Loan Dependent


Loan Dependent


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


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


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

Mail

No-Mail

Documents

MGS4020_10.ppt/Apr 16, 2013/Page 1 Georgia State University - Confidential MGS 4020 Business Intelligence Data Mining and Data Visualization Apr 16, 2013