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Analytics

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SAS Analytics: The Power to Deliver Profitable Business Results
Analytics Consulting SAS Institute – Darius Baer, Jim Hornell, & Ross Bettinger
Copyright © 2004, SAS Institute Inc. All rights reserved. SAS is a registered trademark or trademark of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or Trademarks of their respective companies

April 12, 2005

Beyond BI with SAS Analytics

Objective
 Discuss the value of analytics as part of
the solution to business problems

 Demonstrate two examples of using
analytics to solve business problems

Copyright © 2005, SAS Institute Inc. All rights reserved.

2

Agenda
 Overview
• • • • Why Analytics? Business Problems that can be addressed with analytics Analytic approaches to solving business problems Introduction to the two examples

 Marketing Performance Optimization
Trade Promotion Optimization

 Bank Call Center Text Mining  Conclusion
Copyright © 2005, SAS Institute Inc. All rights reserved.

3

Volumes of Data – How to Extract Maximum Utility
Data Intelligence Information Knowledge

Foresight

Hindsight
OLAP

Insight
Advanced Analytics Drilldown

ETL Sums and Means Statistical Predictions

 Exponential growth of Operational Decisions corporate data and computing power in the
past two decades
• ETL with sums and means provides hindsight from corporate measurements • OLAP with drilldown provides insight from the ETL data warehouse • Only advanced analytics with statistical predictions provides foresight from the ETL data warehouse

 Data Availability + Computing Power + Advanced Analytics →
Competitive Advantage and Best Decisions
Copyright © 2005, SAS Institute Inc. All rights reserved.

4

 Means are useful. Understanding the distribution around the
mean and what contributes to that distribution is essential to compare populations and make predictions

Interpreting the Variability of a Population

 Statistical techniques “predict” the future by apportioning
variance in the population to explanatory variables

 As sales change over time in a well defined pattern, future sales
can be predicted

 If the likelihood of buying a product is associated with
demographic characteristics, then we can predict how likely a particular individual is to buy that product

 With a goal of maximum profits and knowing constraints within
which a company operates, we can solve a series of linear (or non-linear) equations to obtain an optimal solution
Copyright © 2005, SAS Institute Inc. All rights reserved.

5

The Problem Defines the Solution
 Business executives and analysts have always made
operational decisions
• Intuition and experience can be used • Sums and means can provide an historical direction • OLAP and drilldown can provide a better or more detailed perspective • Only advanced analytics can provide a sophisticated point of view on the future of the business

 The problem provides processes and parameters that must be
addressed by the solution
• How would you make the business decision if you did not have advanced analytics? • How can you structure your analysis to follow that process and use those parameters?
Copyright © 2005, SAS Institute Inc. All rights reserved.

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 Railroad must have efficient schedules to move freight

Problem Defines Solution – Example 1

• Before computers, colored strings on a bulletin board were used – time on the X-axis and distance on the Y-axis • Constraints included no crossing of trains except at sidings and stations

 With computers, the business analyst could manipulate the trains
and visualize on the screen
• However, there was no guarantee of a “best” decision that produced optimal usage of the tracks to move the most freight in the minimum amount of time

 With analytics, one takes the problem and goal as stated above
• One has constraints of the trains such as: Minimum and Maximum departure and arrival times Minimum and Maximum Speeds Departure and Arrival Stations Available routes • The goal is solved for using an OR algorithmic approach with PROC NETFLOW and visually represented on a screen • Interaction is provided to the user to modify the analytic result as desired
Copyright © 2005, SAS Institute Inc. All rights reserved.

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Problem Defines Solution – Example 2 Herbicide producer wants to deliver time sensitive herbicide to
farmers immediately prior to the planting of the corn
• Chemical company uses hindsight as to when the farmers planted the corn in previous years • Business experts also have a “sense” for whether the planting will be earlier or later than previous years

 Since the problem is to know beforehand when the farmers will
plant their corn → Go visit the farmers!
• Farmer walks out of house in the morning and sticks wet finger in air to gauge temperature, kicks dirt to gauge moisture, and looks over horizon to see if neighbors are planting their corn. • Using a linear regression approach in each of 98 agricultural districts with the following inputs:
− Daily temperatures combined as necessary in day groups − Precipitation amounts grouped as appropriate − Records of previous years plantings

 With analytics, one takes the problem and understands process

• Each year and each district provide a regression equation • Using a model selection approach provided a limited set of predictive equations for the current year resulting in forecasts being within 2-3 days for 95 out of the 98 districts
Copyright © 2005, SAS Institute Inc. All rights reserved.

8

Analytic approaches to solving business problems

 The best solutions often involve the combination of a
number of analytic techniques (as necessary) combined with business rules that also constrain the solution

 SAS/OR – Finds optimal solution in system of constraints  Enterprise Miner – Predictive modeling, e.g., which customers
are most profitable and/or most likely to respond to an offer

 ETS and HPF – Forecasting, e.g., what are the future sales or
demand based on history and other related factors

 SAS/STAT – Regression, ANOVA, Factor Analysis – how can
we explain the largest amount of variance using statistical techniques
Copyright © 2005, SAS Institute Inc. All rights reserved.

9

Business Cases
 Marketing Performance Optimization /
Trade Promotion Optimization
• Understand and predict the ROI on promotions, advertising and other mass marketing tactics • What’s the optimum mix of marketing tactics?

 Bank Call Center Text Mining
• Explore use of text mining to add value to Bank modeling efforts to predict attrition • Analyze call center comments for additional lift in predicting attrition from primary accounts

Copyright © 2005, SAS Institute Inc. All rights reserved.

10

– MPO/TPO – Marketing Performance Optimization Trade Promotion Optimization
Jim Hornell Analytical Consultant April 12, 2005
Copyright © 2004, SAS Institute Inc. All rights reserved. SAS is a registered trademark or trademark of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or Trademarks of their respective companies

“Half of my advertising is wasted; I just don’t know which half.”
-- John Wanamaker, retail pioneer in the late 1800’s

Copyright © 2005, SAS Institute Inc. All rights reserved.

12

Questions, with historically few answers
 Marketers have tried – for years –
to understand and predict the ROI on promotions, advertising and other mass marketing tactics
• How much does each marketing tactic contribute? • What is the effect of events and activities I cannot control? • What is the “right” level of spend? Overall? By tactic? • How do seasonality and geography affect results? • What’s the optimum mix of marketing tactics?
Copyright © 2005, SAS Institute Inc. All rights reserved.

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“The transformation of TPM [Trade Promotion Modeling], in conjunction with MMM [Market Mix Modeling], from a tactical to a more overarching and encompassing strategic function is well on the way. At this very moment…the question of full functionality is less of an ‘if’ , but ‘when.’”
-- Michael Forhez and Charlie Chase, in ‘Consumer Goods Technology’, March 2005.

Copyright © 2005, SAS Institute Inc. All rights reserved.

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The “When” is Now
 MPO/TPO is designed to:
• Calculate the business impact of multiple marketing channels. − In isolation − In combination • Consider any and all potential variables - controllable and uncontrollable • Allow for changes in variables and desired outcomes with minimal effort • Predict future business outcomes based on specific marketing mix and promotional scenarios • Provide the platform for marketing mix optimization

Copyright © 2005, SAS Institute Inc. All rights reserved.

15

Standard solutions vs. MPO/TPO
Standard Solutions

MPO/TPO

 Analytic short-comings  Too fragile  Inflexible  Fixed in time  Not forward looking

 Calculates impact of

multiple variables – alone and in combination – extremely robust

 Analytic framework exists  Change input and target
variables as needed marketplace activity

 Accounts for changing  Designed to be forward
looking – predicts future outcomes
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Copyright © 2005, SAS Institute Inc. All rights reserved.

The MPO/TPO Offering
 Foundational elements include:
• Flexible data model • Model automation procedures • User interface elements − Interactive − Web based • Executable Master Marketing and Promotional Plan • Marketing campaign scenario forecasts to test effectiveness and cross product cannibalism

 Customized elements include:
• Client-specific data inventory • Coverage of client specific markets and segments • Coverage of client specific products • Customized interface reflecting client needs
Copyright © 2005, SAS Institute Inc. All rights reserved.

17

Sample Variables for a Financial Client
across multiple geographies, on marketing performance
• Product transaction data • Advertising data • Promotion data • Direct marketing data • Econometric data • Demographic composition and segment distribution • Share of market • Share of voice • PR activity • Event / sponsorship activity • Distribution data • Brand data
Copyright © 2005, SAS Institute Inc. All rights reserved.

 The MPO/TPO offering considers the effect of multiple variables,

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Sample Variables for a CPG Client
• Syndicated data (AC Nielsen, IRI) • Shipment and Order history • Promotion calendars • Fund allocations • Pricing • Brand/category/market development index

 The MPO/TPO offering considers the effect of multiple
variables, across multiple distributors, on trade promotion performance

Copyright © 2005, SAS Institute Inc. All rights reserved.

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The User Interface

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Accesses the Modeling Procedure
 Assimilates past business
history using:
• Singular Value Decomposition • Linear regression with Lagged Values • Dynamic Neural Network Modeling

 By correlation rather than
causal modeling

 Resulting in Week by

Week Forecasts over your planning horizon.
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Copyright © 2005, SAS Institute Inc. All rights reserved.

Which Links Business Results to Advertising and Promotional Expenditures
Market Volume Lift Incremental Volume Lift

New York Boston Philadelphia

9,500 2,400/pt 41,378 5,200/pt 42,855 2,150/pt 641/pt 2,676/pt 1,322/pt

Moving away from a growing condition towards a plateau condition:

Rat Lift

e

Incremental Lift = 0

Sales Volume Lift vs. Spend

Insight: Different market areas demonstrate varying upside ad potential
Copyright © 2005, SAS Institute Inc. All rights reserved.

22

The Value Equation
Estimated Benefits
$10 $9 $8 $7 $6 $5 $4 $3 $2 $1 $0
% of Impacted Promos 45% 35% 25%

Assumptions:  Marketing Trade Spend: $100,000,000 (held constant)

$MM Improvement in Trade Spend

 % of Marketing and

Promotions Impacted: 25% – 35% – 45% based on prior client experience

 10-20% improvement

10%

15%

20%

% Improvement

Copyright © 2005, SAS Institute Inc. All rights reserved.

23

Delivery and Implementation
 SAS Software Foundation and Analytics  Consulting for customization to business needs
• Requirements
− Client data access − Customized analytics − Customized reporting

• Design • Customized Development • Testing, Documentation, and Installation

 With Domain Partners
• THMG, Thompson Hill Marketing Group • CSC, Computer Sciences Corporation
Copyright © 2005, SAS Institute Inc. All rights reserved.

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 Advertising and promotional spending is coming
under increased scrutiny

The Commencement of a New Era

 Getting the spend “right” is a complex problem  More and more data are available
• Robust data management, sophisticated modeling, and content expertise are ‘must haves’ to predict results and optimize spending

 SAS has assembled the right software, partners, and
experience to make this work

 Questions??
Copyright © 2005, SAS Institute Inc. All rights reserved.

25

Bank Call Center Text Mining
Ross Bettinger Analytical Consultant April 12, 2005
Copyright © 2004, SAS Institute Inc. All rights reserved. SAS is a registered trademark or trademark of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or Trademarks of their respective companies

Text mining can reveal hidden concepts not previously known

How Can Text Mining Add Value?

 Clusters of terms may contain information about a customer’s
behavior unavailable from structured data decisions

 Information content in clusters can be used to inform business
• Warranty: Do I see a trend of product failures from customer comments? • Surveys: What do employees say about the reorganization? How do we use that information to improve employee productivity? • Medical: Are the proper medications being prescribed for patients based on their verbal statements to the doctor? • Insurance: What are the characteristics of fraudulent claims based on the text on the claim? • Call Center: Do I have enough drop-down categories to cover the information I get from the free-form fields? • Marketing: What are my customers thinking? What are their wants and needs?
Copyright © 2005, SAS Institute Inc. All rights reserved.

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Objective
 Explore use of text mining to add value to
Bank modeling efforts to predict attrition
• Loss of deposits  less money to loan at interest  adverse impact on Bank’s profits

 Analyze call center comments for additional lift in
predicting attrition from primary accounts
• Information in unstructured text may add significant value to model performance when combined with “traditional” data mining practices

Copyright © 2005, SAS Institute Inc. All rights reserved.

28

Agenda
 Discuss SEMMA methodology to build predictive
attrition models
• Sample, Explore, Modify, Model, Assess

 Discuss results of exploratory data analysis to
justify sampling approach
• Unusual properties of Bank call center data require creativity

 Build DM and TM models
• Compare individual DM, TM models, DM + TM model

Copyright © 2005, SAS Institute Inc. All rights reserved.

29

Sampling
 Bank call center data collected from
June, 2003 (Numbers altered for confidentiality) May, 2003-

• 900,000 records at account level supplied to SAS • Chose existing primary customers (750,000 records) • Multiple calls per account required consolidation of data and comments to single account-level observation − After consolidation:

600,000 accounts in good standing 9,000 voluntary attritors (1.47% attrition rate) 4,500 involuntary attritors (0.73% attrition rate) -----------613,500 accounts used in analysis
30

Copyright © 2005, SAS Institute Inc. All rights reserved.

Exploratory Data Analysis
 Findings
• Attritions are a “rare event” (voluntary attrition rate = 1.47%) • Significant imbalance in comments − 40% Blank, 30% Direct Mail

• Strong concentration of comments into few classes will affect performance of text mining models

Copyright © 2005, SAS Institute Inc. All rights reserved.

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EDA (continued)
 Observe similar distribution of comments in voluntary
attritor, nonattritor comments

 Since distribution of comments and “Direct Mail” is similar,
we will assume that these two kinds of comments may be removed without affecting the analysis so that other comments may “speak”

Copyright © 2005, SAS Institute Inc. All rights reserved.

32

EDA (Text Mining Node)
 Using complete data produced two clusters
• 20% sample of voluntary attritors, good accounts
Blank comment Mostly Direct Mail Terms

 Omitting blank and “Direct Mail” comments eliminates

imbalance in comments, reveals more clusters (20% sample)

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Modify
 Perform “optimal binning” of interval variables with
respect to target variable to change them into ordinal variables
• Represent continuous variable as set of ordered indicator variables to better concentrate target variable into small number of bins • Variables Age_Yrs, Cust_Tenure_Mo, N_Phone_Calls were transformed − For example, Age_Yrs was binned into following intervals 0-24, 24-38, 38-75, 75+

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Model
 Modeled voluntary attrition to predict who would
deliberately close account

 Partitioned data
• 50% Training / 25% Validation / 25% Test (Holdout)

 Built stratified models based on voluntary attrition
• Used all voluntary attritors (N=9,000), randomly-selected nonattritors (N=9,000) • Data Mining model (no text-based information) • Text Mining model (only text-based information) • Hybrid Data + Text Mining model − structured data + structured text-based information

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Assess
 Results for test (holdout) dataset
 Model
• DM • TM Node NN Tree Misclas AUC .3808 .4135 .6632 .5884 Lift 1.56 1.28 1.62
Best Model

• Hybrid NN .3840 .6578 − Misclas is misclassification rate − AUC is area under ROC curve − Lift is top 5% lift

 Hybrid model has similar misclassification rate, AUC as
DM model but higher lift

 Conclude that combining DM + TM provides strongest
performance in predicting voluntary attrition
Copyright © 2005, SAS Institute Inc. All rights reserved.

36

Applying Results of Text Mining
 Combine blank, “Direct Mail”, Text Miner- clustered
comments to determine voluntary attrition “lift”

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Applying Results of Text Mining (cont’d)
 Use cluster membership as “trigger”
• Cluster 3 has lift of 4.59 − Terms: – Trigger is life cycle event: marriage, birth of child, buying a home, death, … • Cluster 5 has lift of 2.37 − Terms: – Trigger is financial distress: bankruptcy

Copyright © 2005, SAS Institute Inc. All rights reserved.

38

Applying Results of Text Mining (cont’d)
 Combine blank, “Direct Mail”, Text Miner- clustered
comments to determine involuntary attrition “lift”

Copyright © 2005, SAS Institute Inc. All rights reserved.

39

Concept Linking
 Which terms are related to “dep”?

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Value Proposition
 Use Enterprise Miner to extract information from
“structured” data

 Use Text Miner to turn “unstructured” text into
“structured” data for “traditional” data mining

 Use Enterprise Miner and Text Miner give you an
unbeatable combination for business advantage

Copyright © 2005, SAS Institute Inc. All rights reserved.

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Conclusion
 Hindsight with ETL and Sums & Means is Good
• Important to get a view into your data

 Insight with OLAP and Drilldown is Better
• You obtain a better sense of where your business is now and at whatever level of summary or detail you want

 Foresight with Analytics is Best
• You obtain a confidence of where your business is going in the future so that you can take appropriate action now to be prepared.

Beyond BI with SAS Analytics
Copyright © 2005, SAS Institute Inc. All rights reserved.

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