James Taylor of Decision Management Solutions hosts Anunay Gupta of Marketelligence. Anunay discusses a practical approach to your very first predictive analytics model. Webinar recording available at https://decisionmanagement.omnovia.com/archives/42450
5. About today’s presenters
Anunay Gupta
Co-founder and Head of Analytics, Marketelligent
10 years of experience in Consumer Banking, Risk
Management and Decision Management at American
Express and Citigroup
Work with clients to leverage their data for strategic
and tactical decisioning
6. What you should get out of this webinar
• English-version overview of what folks mean when they talk about business
intelligence, analytics, predictive analytics, models, scorecards, etc…
• Some idea of the mathematics and the sophisticated techniques that work
behind-the-scenes
• More importantly, real-life situations where you can leverage Predictive
Analytics to drive profitable growth in your business
• In the end, its not rocket science. However it does require specialized skills
and expertise for successful build and deployment
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7. Agenda
• What is Predictive Analytics
• Critical Requirements for success
• Real life applications
Direct Marketing : Maximizing ROI
Consumer Finance : Whom to sell? What to sell? Which Channel?
Consumer Packaged Goods : Marketing $ Optimization
• Summary
• Q and A
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8. www.puntersgenie.com
……….we take as much historical data from racing as we can and try to find the
things that are important for predicting the outcome of future races. Once
we find those things (in some cases we can be working with tens of
thousands of combinations of variables), we then run the models against a
test set of races and look at the results. We then look at the races that we
predicted correctly and work out what things made that possible for those
particular races. This is how we come up with the Bet Index. This
information is then fed back into the models to make them better
Predictive Modeling
…. predict the probability of a horse winning a race
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9. What is Predictive Analytics ?
“Use historical data to make certain predictions for the future”
Hindsight Insight Foresight
“What will happen?”
“What is happening ?” “Why is it happening ?”
“What should happen?”
Typical MIS or BI Business analysis Predictive Analytics;
Cognos; Business Objects; behavior analysis; trends; forecasting; optimization,
Hyperion; ProClarity; etc etc etc
Largely backward looking Gives us insights on what Uses past behavior to
is happening and why predict future outcomes
Referred to by many folks
as ‘Analytics’ although it Game changing
is not Forward-looking
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10. Some types of Predictive Analytics
Logistic Forecasting; Segmentation;
Regression OLS; ARIMA CHAID; CART
Commonly used when the Used to forecast Used to bucket or ‘cluster’
objective is to predict a outcomes that are of a like things
binary outcome continuous nature Each member in a cluster
Example: will Customer X Example: how much will is very similar to another
respond or not respond to this Customer Y spend in member in same cluster;
my marketing offer the next month? but very different from a
Example: What is the Example: movement of member in a different
chance Customer Y will the S&P 500 index on a cluster
dis-enroll in the next 12 weekly basis for the next Example: Customers in a
months 12 weeks particular segment have
similar behaviors
ARIMA: Autoregressive Integrated Moving Average
CHAID: Chi-squared Automatic Interaction Detector
CART: Classification & Regression Tree
OLS: Ordinary Least Squares
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11. Critical Requirements for Success
Business Objective
Data Expertise Culture
More data is better; Requires folks that Typically Senior
and data from are not only management buy-
varied information statisticians; but can in is critical.
sources is even also understand Successful
better business projects are top-
driven
Predictive Analytics
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12. Business Objective
I want to identify which Customers will ‘attrite’ so that I can take some
proactive actions
All Customers? Or just new Customers???
Attrite today / tomorrow / next month / etc
What is attrition to me? No activity for 6
months / 2 months / etc
I want to predict which of my high tenure Customers will ‘attrite’
or ‘churn’ in the next 6 months
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13. Analytical Framework
Business Objective:
I want to predict which of my high tenure Customers will ‘attrite’ or ‘churn’ in
the next 6 months
Past Future
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
Months
1. Historical Customer transaction data Decision Period
(mob>12; transactions, interactions)
2. External data
(Credit bureaus; demographics; psychographic,
macroeconomic; etc)
Decision Point
Dec09
13
14. 1. Data Collection
Identify a suitable time period in the past to collect relevant information
Past
-25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11
Months
1. Historical Customer transaction data Decision Period
(mob>12; transactions, interactions) • Identify Attritors; label them as 1’s
2. External data • All others labeled as 0’s
(Credit bureaus; demographics; psychographic,
macroeconomic; etc)
Reference Point
July08
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15. 2. Model Build & Deployment
Model
Raw data
Exploratory Data Variable Variable Development
& Deployment
Analysis Treatment Selection &
Sampling
Validation
Data Preparation Defining Missing Value Stepwise OLS / Logistic / Scorecard
Over sampling ? dependent Treatment regression CHAID / etc development
Reject
variable Variable Logit Plots KS Statistical paper
Inferencing Business sense Transformation Business Logic Rank-ordering Implementation
check Variable capping code
Multi-collinearity Out-of-time
& Flooring Validation
5 – 10 most
significant
variables
Ongoing Model Validation & Maintenance
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16. Output of Modeling Process
Every Customer has a unique ‘Score’ that captures the essence of
what is being modeled.
The ‘Score’ is essentially the ‘probability’ of something happening scaled in a
pre-defined fashion; having an upper- and an lower-bound
Called a ‘Score-card’
For Example:
1. Customer #17523 has a score of 769; translating to a 90% probability of ‘churning’ in the next 6
months
2. Household # 845 has a score of 423; translating to a 36% chance of accepting the offer for a
magazine if sent a Direct mail Offer
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18. Explaining the benefits
Random w/ MIDAS Blaze™
100%
90%
• Save: 25% improvement in marketing
efficiency; leading to annual cost
% Responders Captured
80%
70% savings of $1.5MM. Same number of
Boost
60% Customers acquired
50%
Save
40% • Boost: 25% more acquired
30%
Customers with a marketing budget
20%
of $6MM.
10%
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% • Build scenarios and optimize
% Mailbase
Sell the business impact; not the technical power !
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19. Business Applications
• Optimize your Marketing $
Direct Marketing • Maximizing Customer Lifetime
Value
Consumer Finance • Deepen relationships by cross-sell
& up-sell
Telecom & Utilities • Retain Profitable Customers
• Risk Management & Fraud
Healthcare • Collect past-dues faster
• Predict Part Failures
Manufacturing
• Web targeting
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20. 1. Direct Marketing
Cut marketing expenses significantly; while maintaining acquisition volumes
Random Mailing Intelligent Mailing
Response Rate: 4.5% Response Rate: 6.0%
Mailed
Mailed
Scorecard
Not Mailed
: Prospect
: Responder
Response Scorecards help in identifying Prospects/Customers to target
so as to maximize Response rates
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24. What is Customer Lifetime Value ?
Measuring Customer Lifetime Value
CLV is defined as the sum of cumulated Cash-flows – discounted using the Weighted Average
Cost of Capital (WACC) – of a Customer over his or her entire lifetime with the Franchise
Known from Predict Response
existing P&L’s Rates
Acquisition
Monthly
Costs
Expenses
Customer
Net Margin Lifetime Value
Monthly Accumulated
Revenues Margin
Customer
Lifespan
Predict monthly
Spend Predict Customer
Attrition
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25. Eg. Credit Cards
CLV(Customer1, product XY, Channel PQ) = f (P&L drivers, discount rate)
Customer / Segment
Acquisition Cost Acquisition Models:
Discount Rate -Product & Channel based
-p(Response Score)
Total Customers -p(Approval Score)
Revenue Models: Purchase Sales, $
-p(Activation)
Payment $
-p(Monthly purchase sales)
-p(Payment $) Net Credit Losses, $
-p(Attrition) Ending Loan Balances, $
Revenues
Expenses Expense Models:
Net Income (after taxes) -p(Credit Loss)
Terminal Value
Models can be built at Customer-
Discounted Net Income
level or Segment-level
Discounted Terminal Value
CLV
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26. Eg. Credit Cards Cross-sell
Over 80MM Combinations !
4 Channels
Business
constraints
10 Products
Optimize
Target
Right Product to right
2MM Customers
Customer in the right
Channel
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27. 3. Consumer Packaged Goods
Optimize marketing spend across channels
Marketing-Mix-Optimization
Optimize investments across Media so as to maximize Sales
Historical data is collected for sales (and/or other KPIs) and Multivariate regression analysis is used to quantify
all key Media Marketing activities incremental sales generated
$600,000 $600,000
$500,000 Past sales $500,000
performance
$400,000 $400,000
$300,000 $300,000
$200,000 $200,000
Incremental sales
$100,000 Past TV $100,000 generated by TV
activities
$0 $0
Week10
Week13
Week16
Week19
Week22
Week25
Week28
Week31
Week34
Week37
Week40
Week43
Week46
Week49
Week52
Week10
Week13
Week16
Week19
Week22
Week25
Week28
Week31
Week34
Week37
Week40
Week43
Week46
Week49
Week52
Week1
Week4
Week7
Week1
Week4
Week7
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29. Magazine gives the highest ROI per $ spend
Incremental Sales per ‘000 SGD media spend
0.14
0.12
For every $ spend,
Magazine gives 6
0.10
times the return of
Efficiency
0.08 TV and dailies
0.06
0.04
0.02
-
Total Spends Magazine TV Daily
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30. Key Takeaways
Predictive Analytics can be a potent weapon in
your toolbox
With increasing commoditization, it is truly the
next differentiator
It requires specialized expertise, talent
and tools to execute well
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31. About Marketelligent
anunay.gupta@marketelligent.com www.marketelligent.com
1.201.301.2411
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