predictive analytics for business and marketing

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 Response1. 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 Template1. 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!



Predictive Analytics and Data MiningApplications: 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



Predictive Analytics and Data MiningTeam 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., PresidentPrediction Impact, Inc.San Francisco, California

[email protected](415) 385-1313

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

www.PredictionImpact.comTo receive notifications of training

seminars: [email protected]