1. What’s New in Uplift Optimizer 5.3
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What’s New in Uplift Optimizer 5.3
Patrick Surry
Product Manager
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3. What’s New in Uplift Optimizer 5.3

4. What’s New in Uplift Optimizer 5.3
Presenter: Patrick Surry
Audience: Current users of Uplift Optimizer
Goal: Highlight the key new features and functionality provided in version 5.3
Format: A live demo with some slides

5. Portrait Solutions Portrait presentation template
Friday, April 14, 2017
Portrait Solutions
Interaction Optimizer™
Campaign Manager™
Uplift
Analytic
Insight
Analytic Insight
Marketing Channels
Campaign Management
Campaign
Management
Marketing
Interaction Management
Your CRM
Siebel
Microsoft
Salesforce.com
Sales
Customer
Interaction
Management
So how do we do this? Let’s look at what an optimized marketing process looks like. Of course, you have your 3 major arms of your business feeding information to the customer – Marketing, Sales and Service. Your customer is engaging frequently with one or even all of these.
Service 5

6. Expert Opinion on Portrait’s Uplift Solution
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Expert Opinion on Portrait’s Uplift Solution
Strategic Planning Assumption: Through 2012, less than 35% of customer service centers will invest in readily available CRM functionality.
“Marketers can now add uplift modeling to the arsenal of tools”
- Forrester Research “Optimizing Customer Retention Programs”, Suresh Vittal
“Innovative”
Gartner, “Magic Quadrant for Customer Data-Mining Applications 2008” , Gareth Herschel
“Telenor will realize an 11-fold increase in uplift campaign ROI when compared with existing programs”

7. Typical Analytical Tasks
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Typical Analytical Tasks
Uplift Optimizer
“uplift
modeling”
Incremental value vs. apparent response
Savability
Predictive Customer Analytics
Predict response, attrition, credit risk, value
“predictive
modeling”
Diagnosis of emerging issues
Responding to market changes
“segmentation
and profiling”
Create marketing segmentation
Profile a set of customers
Self-Service Analytics
“calculation”
Calculate historic customer value
When looking at the market in terms of what people mean by “analytics” there is a wide variety of tasks that people describe under this banner.
The first distinction is between analytics and predictive analytics – car windshield analogy
Often people think of the ability to select a group of customers on the basis of what you know about them – database driven marketing for the purposes of contacting them
The ability to create ad-hoc queries against the customer base to test hypotheses and dispel myths – e.g. to have accurate information and not just hearsay about the number of customers, products that they own and where they live
The ability to calculate new values based on existing customer information – net present value for example
The ability to create a segmentation of customers on demand i.e. separating customers into groups for targeting based on similarities of spend frequency, value, geography etc. This is typically combined with the ability to profile those customers so if you choose all customers in a particular region – you can then profile their value, frequency and product holdings for example.
Then we move to the ability to perform “data mining” i.e. understand why things are changing in the customer base – market changes, competitor actions. This is where the ability to rapidly diagnose these issues is key.
Then “real data mining” predictive customer analytics where you wish to move to predicting the likely outcomes for customers.
And as you are all hopefully aware the most sophisticated analytical tasks include focusing on the incremental value of demand generation and focusing on savability for retention
We have a suite of analytical tools that allow different users with different analytical requirements to address these issues. Self-Service Analytics we are talking about today. Quadstone Customer Analytics – I hope you are all familiar with and Uplift Optimizer. The depth of these bars shows the degree of coverage and overlap that these tools have.
Self-Service analytics is all about fast-counts, selection and segmentation and profiling for a wider audience than is typically using the Quadstone customer analytics tool.
“fast counts”
Ad-hoc query
Test hypothesis
“selection”
Select group of customers 7

8. Uplift Modeling: Core Idea
Traditional targeting is based on modeling outcomes, not uplift in outcomes
Uplift Optimizer directly models the difference in behavior between a treated group and a control group to find the people most affected by an intervention
This allows us to:
target people who are more likely to buy given an incentive, rather than simply those likely to buy
target people who can be saved from attrition by an intervention, rather than simply those likely to attrite
target people whose risk of default (or expected loss) can be reduced by intervention, rather than simply those with high risk
Notes
No point in giving discounts to people who will spend anyway
No point in targeting people with retention initiatives that won’t work, still less with ones that will be counterproductive
Particularly relevant in environments where there are many competing drivers of behavior and you need to separate out one marketing action
“Success has many parents; failure is always an orphan”, i.e. if someone buys, the direct mail manager will claim it’s because of his piece, the director of advertizing will claim it’s her TV ads and billboards, and the chief strategist will claim it’s all because the competition screwed up. Uplift modelling will not only allow the argument to be settled: it will tell you which groups the action worked best for

9. Uplift Targeting vs. Attrition Probability
Targeted
Attrition Rate 0% 4% 6% 8%
10% 2%
-6%
-2% 0% 2% 4%
-4%
Control
Reduction in Attrition (Lift)
(change in Attrition probability)
Uplift rate
NB. We’ll illustrate with retention but argument applies equally to both 0%
20%
40%
60%
80%
100%
Proportion targeted
(Most likely to be saved first)
(Most likely to Attrite first) 9

10.     Fundamental Segmentation for Retention Leave Stay Stay Leave
SLEEPING DOGS 
LOST CAUSES 
Leave
When targeted
SURE THINGS 
PERSUADABLES 
Stay
This is an “in principle” segementation for renention (tackling churn, attrition, lapsing).
Although we can’t know it for any individual (because we can’t both treat and not treat them), in principle it must be true that each individual is in one of these four groups:
Persuadables: people who stay only if treated
Sure Things: people who stay whether or not we treat them
Lost Causes: people who leave, whether we treat them or not
Sleeping Dogs: people who only stay if we don’t treat them. (Bears in Sweden)
Clearly, the Persuadables are the only group we would ideally like to treat (hence the tick), and there is double-negative in treating the Sleeping Dogs, where we are spending money to drive away customers.
Uplift modelling focuses on separating these segments out and targeting on the truly incremental responses i.e. the persuadables
Stay
Leave
When not targeted 10

11. Predicting Savability
MORE
SAVABLE
Attrition probability
if not targeted
SLEEPING DOGS
LOST CAUSES
SURE THINGS
PERSUADABLES
if targeted
This is an “in principle” segementation for renention (tackling churn, attrition, lapsing).
Although we can’t know it for any individual (because we can’t both treat and not treat them), in principle it must be true that each individual is in one of these four groups:
Persuadables: people who stay only if treated
Sure Things: people who stay whether or not we treat them
Lost Causes: people who leave, whether we treat them or not
Sleeping Dogs: people who only stay if we don’t treat them. (Bears in Sweden)
Clearly, the Persuadables are the only group we would ideally like to treat (hence the tick), and there is double-negative in treating the Sleeping Dogs, where we are spending money to drive away customers.
Uplift modelling focuses on separating these segments out and targeting on the truly incremental responses i.e. the persuadables 11

12. Incremental Impact for Retention
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Incremental Impact for Retention
No impact
Positive
impact
Negative 1 2 3
Put this money back into your marketing budget
Persuadables
Only act if treated 1
Sure Things
Act whether you
treat or not 2A 2
Lost Causes
Will not act, even if treated 2B
This is the basic “uplift” graph (Qini Graph / Incremental Gains Chart). It shows the fundamental segmentation of the base into
(1) the Persuadables --- the people we make more like to stay by treatment
(2) the Unshakables --- people we don’t really affect. These can be further divided into the “Sure Things”, people who will stay whether we treat them or not, and the “Lost Causes”, who will leave whether we treat them or not. From an uplift perspective, these two groups are the same
(3) the Sleeping Dogs --- people who we actually trigger to leave if we treat them. (“Sleeping Dogs” because we should “let them lie”.)
The lines are curves because we are assuming here that we have a good uplift score, rather than a perfect one.
Note particularly that by targeting, in this case, 40% of the population, we not only spend less but achieve MORE incremental saves than if we target everyone.
Targeting group (1) is spending money to save people.
Targeting group (2) is spending money to no useful effect.
Targeting group (3) is spending money to drive customers away.
By the way we call this the Italian Flag picture
Sleeping Dogs
Act only if not treated 3
© copyright Portrait Software 2007 12

13. Uplift Optimizer 5.3 Themes
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Uplift Optimizer 5.3 Themes
Increased Efficiency
Removed multiply-up stage
Performance enhancements for wide foci
Support implied case-weighting
Improved Usability
Better diagnostic reports (navigation, heat-maps, summaries)
Integrated with Analytics 5.3, updated documentation
Added progress reporting, better system-wide defaults
SAS Integration
Drive uplift modeling & reporting from SAS scripts
Deliver uplift models as SAS code, reports as SAS data
Read and write SAS datasets as always
General Availability: 3 November 2008
(Rollout to all customers coordinated by Portrait Support)

14. Demonstration

15. Uplift Optimizer Futures
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Friday, April 14, 2017
Uplift Optimizer Futures
Themes:
Business-user focus: vs. SAS user
(Excel reporting, financial forecasting, what-if analysis)
Enhanced automation: fire & forget
(uplift assessment, better parameter search, recommend best model)
Intelligent targeting for outbound
Self-arbitrating response vs. uplift analysis
Web-based
Simple wizard-style interface
Integrated with PCM
Estimated availability: early 2009

16. Customer Case Studies Business Goals ROI
Cross-sell HELOC to existing customer base via direct mail
ROI
Campaign ROI increased over 5 times previous campaigns (75% to 400%)
Incremental revenue increased 327%
Best 60% of targets generate >$500K of incremental revenue
Reduced baseline cost from lower volume
More incremental response by avoiding negative effects (downlift)
Net $$ Increased
Churn Reduced

17. Customer Case Studies Business Goals ROI
Deliver next generation targeting for customer retention (churn) campaigns
Removing negative effects thus decreasing both churn and campaign costs
ROI
Campaign ROI increased over 11 times previous campaigns
Churn rate decreases by an additional 36% over traditional approach
Traditional targeting reduced churn by 5% (from 23% to 18%)
But significant negative effects observed
Uplift model reduced these significantly
Saving 40% of treatment costs
Further reducing churn to 6.8%
Introduction of first prediction model
Introduction of refined prediction model
months
annual defection rate
Churn Reduced

18. A Next Generation Marketing Solution
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A Next Generation Marketing Solution
Slash marketing costs while boosting revenue and retention
Reduce customer ad fatigue
Eliminate the negative impact of marketing
Perfect for both inbound and outbound marketing
Proven - power to extend any analytics environment
The only packaged solution of its kind
Your Organization’s New Competitive Advantage

19. Where to find out more … Uplift Optimizer Support website:
Documentation
What’s new in the Uplift Optimizer release notes
Updated Uplift Optimizer help
Uplift Optimizer Support
Web Site:
Tel: US ; All

20. Questions? www.portraitsoftware.com Portrait presentation template
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Europe (Headquarters)
The Smith Centre, The Fairmile
Henley-on-Thames, Oxfordshire,
RG9 6AB, United Kingdom
T: +44 (0)
F: +44 (0)
Questions?
Edinburgh
39 Melville Street
EH3 7JF, United Kingdom
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F: +44 (0)
Oslo
Maridalsveien 87, Bygg Oslo Norway T: F:
Americas
125 Summer Street
16th Floor
Boston MA 02110, USA T: F:
Asia Pacific
Level 7
15-17 Young Street
Sydney NSW 2000
Australia F: 20 20