V imp analytics appli ed

Prediction Impact, Inc.© Copyright 2008. All Rights Reserved.
Eric Siegel, Ph.D.Eric Siegel, Ph.D.
Prediction Impact, Inc.Prediction Impact, Inc.
eric@predictionimpact.comeric@predictionimpact.com
(415) 683 - 1146(415) 683 - 1146
Predictive Analytics Applied:
Marketing and Web
Predictive Analytics Applied:Predictive Analytics Applied:
Marketing and WebMarketing and Web
Brought to you by
Prediction Impact and
World Organization of
Webmasters (WOW)

1
Training Program OutlineTraining Program OutlineTraining Program Outline
1. Introduction
2. How Predictive Analytics Works
3. App: Customer Retention
4. App: Targeting Ads
5. Summary and take-aways

(415) 683 - 1146(415) 683 - 1146
About the speaker: Eric Siegel, Ph.D.
– President of Prediction Impact, Inc.
– Training and services in predictive analytics
– Former computer science professor at
Columbia University
– Before Prediction Impact, cofounded two
software companies

Business intelligence technology that
produces a predictive score for each
customer or prospect
Predictive Analytics:Predictive Analytics:Predictive Analytics:
34 12 27 79 42 5934 1234 271234 79271234 42 5979271234 1234

Optimizing Business ProcessesOptimizing Business ProcessesOptimizing Business Processes
4
“At a time when
companies in many
industries offer similar
products and use
comparable technology,
high-performance
business processes are
among the last remaining
points of differentiation.”
- Tom Davenport

Learn from Organizational ExperienceLearn fromLearn from Organizational ExperienceOrganizational Experience
5
Data is a core strategic asset –
it encodes your business’ collective experience
It is imperative to:
Learn from your data.
Learn as much as possible about your customers.
Learn how to treat each customer individually.

Predictive Analytics:Predictive Analytics:Predictive Analytics:
6
Your organization learns
from its collective experience
Collective experience: Sales records, customer profiles, …
Learn: Discover strategic insights and business intelligence
…and puts this knowledge to action.
Discover something that makes business decisions
automatically
Discover business rules

(415) 683 - 1146(415) 683 - 1146
How Predictive Analytics WorksHow Predictive Analytics WorksHow Predictive Analytics Works
Building and Deploying Predictive ModelsBuilding and Deploying Predictive Models
Predictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and Web

Wisdom Gained: A Predictive Model is BuiltWisdom Gained: A Predictive Model is BuiltWisdom Gained: A Predictive Model is Built
8
Predictive
model
Customer
behavior
Customer
contact logs
Customer
profiles
Modeling
tool
•Performed by modeling software
•Usually offline

Applying a Model to Score a CustomerApplying a Model to Score a CustomerApplying a Model to Score a Customer
9
Predictive
model
Customer
profile
Customer
behavior
Predicted
response
•Often performed by
modeling software
•Possibly online

Deploy to Take Business ActionDeploy to Take Business ActionDeploy to Take Business Action
10
•Usually not performed by
modeling software
Predicted
response
Business
logic
• Mail a
solicitation
• Suggest a
cross-sell option
• Retain with a
promotion
Business actions,
such as:
•Possibly online

Predictive models are created automatically from your data
So, they’re generated according to your:
– Product
– Prediction goal
– Business model
– Customer base
This means customer intelligence specialized for your business
– Customized business rules
– Unique, proprietary mailing lists
– Insights only your organization could possibly gain
Fine-Tuned Models for Your BusinessFine-Tuned Models for Your BusinessFine-Tuned Models for Your Business
11

Predictors: Building Blocks for ModelsPredictors:Predictors: Building Blocks for ModelsBuilding Blocks for Models
12
• recency – How recent was the last purchase?
• personal income – How much to spend?
Combine predictors for better rankings:
recency + personal income
2_recency + personal income

Training Data Is a Flat TableTraining Data Is a Flat TableTraining Data Is a Flat Table
13
Number
purchases:
Last
purchase: Gender: Income:
Likely
to buy:
7 shoes M high gloves
3 gloves F medium hat
1 hat M high shoes
46 gloves F low piano
This is the required form for training data; one record per customer.

Why Not Memorize The Training Examples?Why Not Memorize The Training Examples?Why Not Memorize The Training Examples?
14
To learn from history is to remember history.
So, we could just keep a lookup table and compare new
cases to old ones.
Answer: There are too many possible rows of data.
That is, each customer is unique!

Decision Tree for Cross SellDecision Tree for Cross Sell
15
Decision
Trees
•Non-linear
•Specialized
•Precise
Bought shoes?
Female?
Hat Gloves Shoes Piano
High Income?
If they bought shoes and they’re not female then sell gloves.
If they didn’t buy shoes and they’re high income then sell shoes.

Response Modeling for Direct MarketingResponse Modeling forResponse Modeling for Direct MarketingDirect Marketing
16
Lower campaign costs and increase response rates
• Make campaigns more targeted and more selective
• Identify segments five or more times as responsive
• Achieve 80% of responses with just 40% of mailing
• Increase campaign ROI & profitability

Predictive Models Score Each CustomerPredictive Models Score Each CustomerPredictive Models Score Each Customer
17
Name:
• Each customer is scored with a response probability
• The customers are listed in order of prediction score
• The highest-scoring customers are targeted first
No !14%T. Mitchel674
Yes "22%G. Clooney256
No !31%D. Leong528
Yes "35%E. Siegel429
Response:Score:ID number:

18
-700,000
-600,000
-500,000
-400,000
-300,000
-200,000
-100,000
0
100,000
200,000
300,000
400,000
Percent of Customers Contacted
Profit
0% 25% 50% 100%75%
Customers ranked with predictive analytics
Customers not ranked
CampaignProfitCurveCampaignProfitCurveCampaignProfitCurve

Lift ChartLift ChartLift Chart
19
9.3 30.6
16.4 43.5
20.3 51.1
32.4 70.1
44.0 80.2
0
20
40
60
80
100
0 20 40 60 80 100
%Class
% Population
0
20
40
60
80
100
• 50,000 customers 3 times as likely to buy
• 200,000 customers 2 times as likely to buy

Example Business RuleExample Business RuleExample Business Rule
20
New customers who come to the website
off organic search results, buy more than
$150 on their first transaction, are male,
and have an email address that ends with
“.net” are three times as likely to be
return customers.

(415) 683 - 1146(415) 683 - 1146
App: Customer RetentionApp:App: Customer RetentionCustomer Retention
Retain Customers by Predicting Who Will DefectRetain Customers by Predicting Who Will Defect

Growth = Acquisition - DefectionGrowth = AcquisitionGrowth = Acquisition -- DefectionDefection
22
Customer
base
Acquisition:
New customers
Defection:
Lost customers
Which is the best way to
increase growth?
1. Increase acquisition
2. Decrease defection

Increase retention by 3%
and boost growth by 12%
Increase retention by 3%Increase retention by 3%
and boost growth by 12%and boost growth by 12%
23
3%

Predictor VariablesPredictor VariablesPredictor Variables
24
• Age of membership
• External acquisition source
• U.S. state of residence
• Willing to receive postal mailing (opt-in)
• Num days since last failed login attempt
• Num days between logins

At-Risk SegmentAt-Risk SegmentAt-Risk Segment
25
• SOURCE_COBRAND$ == chat
• SUBSCR_AGE <= 237.819
• LOGIN_DAYSSINCELAST_F > 1.85344
• LOGIN_DAYSSINCELAST_F <= 22.6578
Churn rate: 33.5% (vs. 20% average)

Loyal SegmentLoyal SegmentLoyal Segment
26
• STATE is one of many, including ME, NE, NH, NS, QC, SK, WY
• SOURCE_COBRAND$ == chat
• SUBSCR_AGE > 237.819
• TIME_SINCE_JOINED <= 535.456
• LOGIN_DAYSSINCELAST_F > 99.6539
• LOGIN_DAYSSINCEFIRST_F > 112.003
• LOGIN_DAYSSINCELAST_S > 131.45
Churn rate: 6.5% (vs. 20% average)
Very loyal
Small segment

27Cost per mailing: $25, ave profit: $100, num subscribers: 100,000
Forecasted Profit of Retention Campaign

Loyal Segment: Online RetailLoyal Segment: Online RetailLoyal Segment: Online Retail
28
• ITEM_QTY > 2.5
• TAX_SHIP <= 8.78
• 19% will return (versus 10% average)

(415) 683 - 1146(415) 683 - 1146
App: Targeting AdsApp:App: Targeting AdsTargeting Ads
PredictivelyPredictively Modeling Which PromotionModeling Which Promotion
Each Customer Will AcceptEach Customer Will Accept

3030
Target content
– Landing page
– Featured product
– Color of product displayed
– Promotion or
advertisement
Based on:
– Customer behavior profile
• Browsers vs. hunters
– Time of day
– Geographical location
– Pages
– Where they came from
• Ad that clicked them through
• Site they came from
• Search query they were on
• Profile, when available
Learn More From AB Testing:
“Dynamic AB Selection”
Learn More From AB Testing:
“Dynamic AB Selection”
A
B
C
?

Dynamic AB SelectionDynamic AB SelectionDynamic AB Selection
31
Predictive
model for B
Mary’s
profile
Trials of B
Trials of A
Modeling
tool
Modeling
tool
Predictive
model for ATrials of A
Trials of BTrials of BTrials of B
offline online
Predictive score:
What’s the chance
Mary responds to A?
Predictive score:
What’s the chance
Mary responds to B?

Selecting Between 291 Sponsored PromotionsSelecting Between 291 Sponsored PromotionsSelecting Between 291 Sponsored Promotions
32
Client: A leading student grant and scholarship search service
Sponsors include:
– Universities
– Student loans
– Military recruitment
– Other misc.

The Business: Great Potential GainThe Business: Great Potential GainThe Business: Great Potential Gain
33
• High bounties, up to $25 per acceptance
• High acceptance rates, up to 5%, due to
general relevancy of the promotions
• Big Data: Wide and Long
– Rich user profiles volunteered for funds eligibility
– Over 50 million training cases: 01/05 - 09/05

34
Prior solicitation of this program
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
No
Yes
Opt_in_status
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
Y
N
Region of current address
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
south
northeast
west
midwest
Sority or Fraternity
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
frat
none
soro
Already seen? N/Y Email opt-in Y/N
Region: S, NE, W, M-W Fraternity/None/Sorority

Highly Responsive Segment for "Art Institute" AdHighly Responsive Segment for "Art Institute" AdHighly Responsive Segment for "Art Institute" Ad
35
Segment definition included:
• Still in high school
• Expected college graduation date in 2008 or earlier
• Certain military interest
• Never saw this ad before
Segment probability of accepting ad: 13.5%
Average overall probability of accepting ad: 2.7%

Highly Responsive Segment for a "Navy" AdHighly Responsive Segment for a "Navy" AdHighly Responsive Segment for a "Navy" Ad
36
Segment definition included:
• Has opted in for emails
• Has not seen this ad before
• Is in college
• SAT verb-to-math ratio is not too low, nor too high
• SAT written is over 480
• ACT score is over 15
• No high school name specified
Segment probability of accepting ad: 2.6%
Average overall probability of accepting ad: 1.6%

Money-Making Model:
Improved Resulting Revenue
Money-Making Model:Money-Making Model:
Improved Resulting RevenueImproved Resulting Revenue
37
A-B test deployment to compare:
A. Legacy system based on acceptance rates across users
B. Model-based ad selection
Result:
25% increased acceptance rate
3.6% increased revenue observed; 5% later reported by the client
This comes to almost $1 million per year in additional revenue,
given the existing $1.5 million monthly revenue.

(415) 683 - 1146(415) 683 - 1146
ConclusionsConclusionsConclusions
Summary and take-Summary and take-awaysaways

Predictive Analytics InitiativesPredictive Analytics InitiativesPredictive Analytics Initiatives
39
• Per-customer predictions: Unlimited
range of business objectives
• Management: An organizational process
ensures predictions are actionable, driven
by business needs; multiple roles and
skills
• Deployment: Mitigate risk and track
performance

Predictive Analytics TechnologyPredictive Analytics TechnologyPredictive Analytics Technology
40
• Data preparation: An intensive
bottleneck, critical to success
• Modeling methods and tools: No one
method that is always best; compare
multiple methods

41
Prediction Impact
Predictive Analytics and Data Mining
Services:
• Defining analytical goals & sourcing data
• Developing predictive models
• Designing and architecting solutions for model deployment
• "Quick hit" proof-of-concept pilot projects
Training programs:
• Public seminars: Two days, in San Francisco, Washington DC, and other locations
• On-site training options: Flexible, specialized
• Instructor: Eric Siegel, Ph.D., President, 15 years of data mining, experienced
consultant, award-winning Columbia professor
• Prior attendees: Boeing, Corporate Express, Compass Bank, Hewlett-Packard, Liberty
Mutual, Merck, MITRE, Monster.com, NASA, Qwest, SAS, U.S. Census Bureau, Yahoo!
© Copyright 2006. All Rights Reserved Prediction Impact, Inc.© Copyright 2008. All Rights Reserved
If you predict it, you own it.www.PredictionImpact.com

42
Prediction Impact
© Copyright 2006. All Rights Reserved© Copyright 2008. All Rights Reserved
If you predict it, you own it.www.PredictionImpact.com
Applications:
• Response modeling for direct marketing
• Product recommendations
• Dynamic content, email and ad selection
• Customer retention
• Strategic segmentation
• Security
– Fraud discovery
– Intrusion detection
– Risk mitigation
– Malicious user behavior identification
• Cutting-edge research for
groundbreaking data mining initiatives
Prediction Impact, Inc.
Verticals:
• Online business: Social networks,
entertainment, retail, dating, job hunting
• Telecommunications
• Financial organizations
• A fortune 100 technology company
• Non-profits
• High-tech startups
• Direct marketing, catalogue retail

43
Prediction Impact
© Copyright 2006. All Rights Reserved© Copyright 2008. All Rights Reserved
If you predict it, you own it.
Team of several senior consultants:
• Experts in predictive modeling for
business and marketing
• Relevant graduate-level degrees
• Communication in business terms
• Complementary analytical
specialties and client verticals
• Published in research journals and
industrials
Extended network of many more:
• Closely collaborating partner firms
• East coast coverage
Eric Siegel, Ph.D., President
San Francisco, California
eric@predictionimpact.com
(415) 683 - 1146
For our bi-annual newsletter, click “subscribe”:
www.PredictionImpact.com
To receive notifications of training seminars:
training@predictionimpact.com

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