Archive for July, 2013

July 29th 2013

Practitioner Interview Series: Brett Cohen of AOL


I originally published this article in Predictive Analytics Times.  

Practitioner Interview Series:  Brett Cohen of AOL

In anticipation of his upcoming conference presentation at Predictive Analytics World Boston“How Much Are You Worth? – Calculating Customer Lifetime Value,”  we asked Brett Cohen, Senior Business Intelligence Analytist at AOL, a few questions about his work in predictive analytics.

Q. In your work with predictive analytics, what behavior do your models predict (e.g., attrition, response, fraud, etc.)?

A. My models predict customer lifetime value as well as churn in customer engagement.

Q. How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A. At AOL, predictive analytics helps us decide which partnerships we should invest more in and which ones we should abandon. Our models show which partners will provide a positive ROI and looking at how engagement and lifetime value are trending in our model, we are not only better equipped to make these decisions, we can make them much quicker than before (incorporating a predictive analytics model).

Q. Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A. Originally, our model predicted lifetime value of traffic generated from the various partners on a monthly basis – looking at different cohorts every 30 days. With one partner, in the first half of a 30 day cohort, we noticed that engagement and ROI was much lower than usual, so we sprung into action and adjusted our model to look at the data on a weekly basis as well and predict engagement trends on a week to week basis, as well as work with the partner as to how to get their engagement up to a level that was acceptable and ROI positive. Now, we look at this for every partner to anticipate issues like this before they arise again.

Q. What surprising discovery have you unearthed in your data?

A. One surprising discovery that I have unearthed in my data is that month 1 engagement (not monetization) is the leading predictor of customer lifetime value. The higher engagement of customers in the first month, regardless of monetization, will drive a higher LTV than a similar increase in monetization (in our model, monetization is CPC – cost per click). For example, a 10% higher CPC will have a smaller impact on LTV than a 10% higher month 1 engagement.

Q. Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A. By taking a step back from the complex data intensive models, you will see how we created a 3 pronged model looking at audience, engagement, and monetization to predict the lifetime value of users coming from different properties or partners. This model is proven empirically, when looking at the expected (predicted) vs. actual data. You will also see how this model is adaptable to other areas and by changing the individual metrics that are measured, you can look at the lifetime value of customers in nearly any industry – not only what is the value of someone who came to a website; but how much is someone who enters a store worth or how much is someone who signs up for a mailing list worth, amongst others.

Q. What has been some feedback from stakeholders?

A. From the team that is in charge of the partnerships that my predictive models help assess: “this is an awesome tool that really helps us make smart decisions” and “the ability to get accurate LTVs timely has been helpful in making some quick allocation decisions.”

Don’t miss Brett’s presentation, "How Much Are You Worth? – Calculating Customer Lifetime Value"  at PAW Boston on Tuesday, October 1st. Register Now.

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July 22nd 2013

Predictive Analytics: Harnessing the Power of Big Data

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I originally published this article in  The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Predictive Analytics:  Harnessing the Power of Big Data

Every day’s a struggle. I’ve faced some tough challenges such as which surgery to get, how to invest for my business and even how to deal with identify theft. With so much stuff coming at me from all angles, daily prosperity relies on spam filters, Internet search engines, and personalized music and movie recommendations. My mailbox wonders why companies still don’t know me well enough to send less junk mail.

These predicaments matter. They can make or break your day, year or life. But what do they all have in common?

These challenges – and many others like them – are best addressed with prediction. Will the patient’s outcome from surgery be positive? Will the credit applicant turn out to be a fraudster? Will the investment fail? Will the customer respond if mailed a brochure?

There’s another angle. Beyond benefiting you and I as individuals, prediction bestows power upon an organization: Big business secures a competitive stronghold by predicting the future destiny and value of individual assets.

For example, in the mid-1990s, Chase Bank witnessed a windfall predicting mortgage outcome. By driving millions of transactional decisions with predictions about the future payment behavior of homeowners, Chase bolstered mortgage portfolio management, curtailing risk and boosting profit.

Introducing … the Clairvoyant Computer

Making such predictions poses a tough challenge. Each prediction depends on multiple factors: the various characteristics known about each patient, each homeowner and each e-mail that may be spam. How shall we attack the intricate problem of putting all these pieces together for each prediction?

The solution is machine learning; computers automatically discovering patterns and developing new knowledge by furiously feeding on modern society’s greatest and most potent unnatural resource: data.

Click here to read the full article in

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July 17th 2013

Obama Camp Reveals How They Scientifically Persuaded Millions of Voters

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I originally published this article in The Fiscal Times.  The article relates to my book,  Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. 

Obama Camp Reveals How They Scientifically Persuaded Millions of Voters

Elections hang by a thinner thread than you think.

By now you probably know that Barack Obama’s 2012 campaign for a second term “moneyballed” the election, employing a team of over 50 analytics experts.

You may also know that the huge volume of contentious and costly presidential campaign tactics – executed in the eleventh hour in pursuit of the world’s most powerful job – ultimately served only to sway a thin slice of the electorate: swing voters within swing states.

But what most people don’t realize is that presidential campaigns must focus even more narrowly than that, taking micro-targeting to a whole new level. The Obama campaign got this one right, breaking ground for election cycles to come by applying an advanced form of predictive analytics that pinpoints rare gems: truly persuadable voters.

This is the new microcosmic battleground.

We’ve heard a great deal about Nate Silver lately. Silver has soared past the ranks of sexy scientist to become the face of prediction itself. If mathematical “tomorrow vision” has a name, it’s Nate. Even before his forecasts were vindicated by the election results, it was hard to find a talk show host who hadn’t enjoyed a visit from Silver.

But an election poll does not constitute prognostic technology – it is plainly the act of voters explicitly telling you what they’re going to do. It’s a mini-election dry run. There’s a craft to aggregating polls, as Silver has mastered so adeptly, but even he admits it’s no miracle of clairvoyance. “It’s not really that complicated,” he told late-night talk show host Stephen Colbert the day before the election. “There are many things that are much more complicated than looking at the polls and taking an average . . . and counting to 270, right?”

Click here to read the full article in The Financial Times.

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July 15th 2013

The Most Content About Predictive Analytics I Could Jam into 300 Pages

Have you checked out the detailed table of contents for my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die?


Predictive Analytics
Table of Contents

Foreword       Thomas H. Davenport xiii

What is the occupational hazard of predictive analytics?

The Prediction Effect

How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die?

Chapter 1
Liftoff! Prediction Takes Action (deployment)

How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system?

Chapter 2
With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets (ethics)

How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policy holder death? An extended sidebar on fraud detection addresses the question: how does machine intelligence flip the meaning of fraud on its head?

Chapter 3
The Data Effect: A Glut at the End of the Rainbow (data)

We are up to our ears in data, but how much can this raw material really tell us? What actually makes it predictive? Does existing data go so far as to reveal the collective mood of the human populace? If yes, how does our emotional online chatter relate to the economy's ups and downs?

Color Book Insert
147 Examples of Predictive Analytics

A cross-industry compendium of 147 mini-case studies in predictive analytics, divided by vertical:

  • Personal Life
  • Marketing
  • Finance
  • Healthcare
  • Crime Fighting and Fraud Detection
  • Reliability Modeling
  • Government and Nonprofit
  • Human Language and Thought
  • Human Resources
Chapter 4
The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling)

What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis?

Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles)

To crowdsource predictive analytics—outsource it to the public at large—a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?

Chapter 6
Watson and the Jeopardy! Challenge (question answering)

How does Watson—IBM's Jeopardy!-playing computer—work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible?

Chapter 7
Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift)

What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward—but that can be predicted in advance?


Ten Predictions for the First Hour of 2020

A. Five Effects of Prediction 221
B. Twenty-One Applications of Predictive Analytics 222
C. Prediction People—Cast of "Characters" 225
Notes 228
Acknowledgments 290
About the Author 292
Index 293


FAQ: Is this book for practitioners and experts?

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July 1st 2013

Book FAQ: Is the Book “Predictive Analytics” for Experts?

When you invest the time to read a book, you're investing a lot more than the $17 to buy it.

Many ask whether my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, is at the right level for their needs. Is it too advanced? (Quick answer: definitely not.) Will it instruct me on how to execute on predictive analytics? (Not directly – it is an industry primer rather than a "how to.")

FAQ: Is Predictive Analytics for practitioners and experts?


Easily understood by all readers, Predictive Analytics is a conceptually complete primer for non-scientists (like a textbook) – but disguised as an entertaining "pop science" business reader.

Rather than a "how to" for hands-on techies, the book entices newcomers and experts alike by covering new examples and the latest state-of-the-art techniques. A kind of "pre-how-to," the book introduces the critical concepts for any would-be practitioner. After reading the book, you are directed elsewhere by its Notes section for the technical "how to" and advanced underlying math. Like a primer in economics or biology, if you are pursuing a career in the field, this book will set the foundation, yet only whet your appetite for more.

Although accessible to any reader, it's also of interest to experts with coverage of new case studies and the latest advanced methods. If you are more technically oriented, check out the article, "Five Reasons Siegel's Book Predictive Analytics Matters to Experts."

While there are a number of books that do approach the "how to" side of predictive analytics, this book serves a different purpose. I took on what turned out to be a rewarding challenge: sharing with a wider audience a complete picture of predictive analytics, from the way in which it empowers organizations, down to the inner workings of predictive modeling. With its impact on the world growing so quickly, it's high time the predictive power of data – and how to scientifically tap it – be demystified to reveal its intuitive yet awe-inspiring nature. Learning from data to predict human behavior is no longer arcane.

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