Predictive Analytics for Business and Marketing © 2007 Prediction Impact, Inc. All rights reserved...

Predictive Analytics for Business and Marketing

© 2007 Prediction Impact, Inc. All rights reserved

Hosted by Prediction Impact, Inc. in association with the Emetrics Summit

________________________Eric Siegel Ph.D.Prediction Impact, [email protected](415)385-1313

mailto:[email protected]



Case Study: Direct Response

1. Company overview: National veterans organization Non-profit organization; fund raising

2. General Objectives Find good donors Recapture “lapsed” donors Find “high dollar” donors

• There is often an inverse correlation between likelihood to respond and dollar amount of gift.

Cost-effective fund raising—net revenue



Problem-Solving Session Template

1. Overall strategy Outline of initiative and its primary phases

2. Predictive modeling approach Prediction statement/goal Data required

• Applicable segments• Predictors

Deployment: How the model will be integrated or otherwise made use of Business case

3. Evaluation KPIs (a la business goals) Final AB test; control group Baseline method for comparison

4. Challenges and bottlenecks anticipated Organizational Technical



Problem-Solving Session #1: Retention

• Business: Web-o-Rama House of Metrics (worhm.com)• Year: 2012• Type: B-2-B• Description: Web analytics services for small to medium

businesses• Customer Breakdown:

– 100k free subscribers

– 30k premium subscribers• Credit card auto-bill monthly• High churn rate: 35% per year

• Problem: Attrition rate has increased 20% since last quarter, while conversions have remained the same. Another team is working on increasing conversions.



Summary of Killer Apps

• Increased profit with response modeling for direct marketing

– Increase response rate

– Decreased spending

• Increased customer retention by predicting defection

– Retaining tenured customers

– Converting first-time customer

• Increased response with targeted content

– Dynamic, behavior-based content selection

– From AB selection to ABC...Z selection

• Increased sales by predicting cross-sell opportunities

– Recommendations engine

– Collaborative filtering

• Increased net worth by predicting customer lifetime value (LTV)

– Higher valued acquisitions

– Optimized retention targeting



Jellybeans Brain Teaser

• Good: red red red red red red red red red red red

• Good: blue yellow green orange

• Bad: black moive magenta beige turquoise fuisha



How a Crystal Ball Works



Predict This!

• Introduction

• Data Overload

• How Modeling Works

• Statistical Models in Fashion

• Modeling Methods

• Deployment & Results

• Conclusions



Can Computers Think

http://www.astrosurf.org/lombry/Documents/terminator-2.jpg



Next Steps for you

• Moving towards a predictive analytics initiative– List your top 3 to 5 business optimization objectives

– Match the list of killer apps to these objectives

– Scope the data collections requirements

• Other courses– The Modeling Agency's Level II and III Training

– Tools courses, such as Salford Systems

– These courses go beyond business apps to include science and engineering



Recommended References Sources• Prediction Impact’s bi-annual email newsletter: Case studies, articles, events.

Click “subscribe” at www.PredictionImpact.com.

• www.KDnuggets.com: A comprehensive online reference and newsletter.

• The UCI KDD Database Repository (kdd.ics.uci.edu): the most popular site for datasets used for research in machine learning and knowledge discovery. But all the core references such as this are found under KDnuggets, above.

• The Cartoon Guide to Statistics, L. Gonick and W. Smith.

• Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, E. Frank and I.H. Witten.

• Database marketing books for preliminary steps towards modeling, and for a more holistic, less technical marketing viewpoint:

– Strategic Database Marketing, Arthur Hughes

– See JimNovo.com for his “Drilling Down” book (first 9 chapters for free)

– Email Marketing by the Numbers, Chris Baggott



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 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• Training participants: Boeing, Corporate Express, Compass Bank, Hewlett-

Packard, Liberty Mutual, Merck, MITRE, Monster.com, NASA, Qwest, SAS, U.S. Census Bureau, Yahoo!




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

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




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

Prediction Impact, Inc.

San Francisco, California

[email protected]

(415) 385-1313

For our bi-annual newsletter, click “subscribe”:

www.PredictionImpact.com

To receive notifications of training seminars:

[email protected]

Predictive Analytics for Business and Marketing © 2007 Prediction Impact, Inc. All rights reserved...

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