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Analytics Magazine

Analytics Power Player

Editor’s Note: The following article will appear in the September/October issue of Analytics magazine.

PAW founder Eric Siegel discusses the power of predictive analytics, privacy issues, his new book and what the future may hold for analytics professionals and consumers.

By Peter Horner

Eric Siegel, founder of Predictive Analytics World (PAW, a series of conferences held throughout the year in major U.S. and European cites) and the author of the new book, “Predictive Analytics: The Power to Predict who will Click, Buy, Lie or Die,” is without question a key player in the ongoing global analytics movement that’s transforming the way organizations conduct business. The college-professor-turned-entrepreneur recently shared his perspective on the dynamic analytics world with Analytics magazine. During a 30-minute chat, we talked about several issues raised in the book including privacy concerns in the big data era, PAW and his prediction for the future of predictive analytics. Following are excerpts from the interview.

Tom Davenport and Jeanne Harris’ 2007 book, “Competing on Analytics,” brought analytics to the attention of the business world on a mass scale and launched a wave of analytics-oriented books. What motivated “Predictive Analytics”?

I wanted to bring the concepts of predictive analytics to anybody and everybody who might be interested in the remotest sense of the word, including people who aren’t even business consumers let alone technical practitioners. I strove to write a book that, although informative and conceptually coherent and comprehensive, is accessible. It’s an intro textbook disguised as a fun, entertaining, pop science book. I don’t mean in any way to diminish the book’s value to people who are new to the field and are interested in making use of this technology – business users, business readers and prospective technical practitioners – because it very much covers the main concept behind predictive analytics, how it’s used and how it works.

You don’t often see the words “analytics” and “fun” mentioned in the same sentence, but you see it in your book. What is fun about analytics?

Predictive analytics is an incarnation of machine learning where the computer learns from experience, from data. It is learning how to predict, and the science involved is gee-whiz cool.

It’s compelling from a philosophical standpoint in terms of what the challenge is and what it means to actually discover new knowledge from historical data that will apply in the future under new circumstances. That is really interesting, fun stuff. There’s no reason that shouldn’t be explained in a way that anyone could understand exactly what’s going on and why it’s so cool.

On the business side, it’s exciting because it’s so valuable. It is, in a sense, the holy grail for all sorts of applications in marketing, fraud detection, credit scoring and outside of business in government, law enforcement, education, nonprofits, even presidential campaigns.

The value is so strong because the ability to make predictions for each individual directly informs operational actions and decisions, so this is the ultimate of data-driven decision-making per individual. The prediction for the individual directly informs how to treat or contact or whatever action to take with that individual. That’s extremely valuable. It’s fun because the science is really cool and it’s amazing to explore, and because the resulting value is changing the world and irrefutable.

The subtitle of your book, “The power to predict who will click, buy, lie or die,” is provocative and, in a way, troubling since it conjures up visions of Big Brother and privacy concerns associated with big data and analytics.

That is a semi-humorous subtitle to signal the reader that, hey, this is not your traditional business book … but certainly predicting when you will die is one of the places where ethics come up in civil liberty issues such as privacy. In general, the biggest negative contribution that predictive models have for privacy is that in addition to what data should be shared to what degree and what data should be considered sensitive, we are now introducing the power to infer new data that can be even more sensitive such as: When are you going to die? Are you likely to get pregnant? Are you going to quit your job? Are you going to commit a crime again if we release you from prison?

These things are extremely sensitive, and the way they are acted upon needs to be carefully monitored. In general, it’s very hard to delineate hard and fast lines of where things start to become problematic and where they become ethically questionable. However, the first and most important thing we can do is spread the word. The world at large needs to see exactly what’s going on. This new power is very valuable, but it also has this risk associated with it.

Given all of the competitive advantages analytics offers, why are so many organizations still reluctant to jump on the analytics bandwagon?

Predictive analytics is a relatively complex initiative, particularly if an organization has never used it before. My book is trying to help change the perception that it is overly complex, but there are challenges. It’s a new concept, it’s a new way of doing things, there’s a certain amount of inertia to overcome and resources need to be carved out in order to take that first step. It doesn’t happen overnight.

It’s been very exciting over the last several years to watch analytics explode, but it’s still just the tip of the iceberg. There’s still so much untapped potential. Everyone’s excited about big data, and there’s no question that data is exploding like mad, but that’s sort of easy. All you have to do to explode your data is to not delete it, which is sort of a no-brainer because the data is so cheap to store.

The hype around big data, however, doesn’t directly address what the actual value is and what the purpose of the data is. One of the most actionable things you can get from data is to learn from it to predict behavior at the individual level. That’s predictive analytics. So given all that hype over big data and the incentive to make use of it and leverage the data, predictive analytics is a key way to do so, and it is really taking hold in many sectors at a breakneck pace.

Over the last 15 or 20 years, the corporate world has tried to catch and ride other big, so-called game-changing waves such as “re-engineering” and “enterprise resource planning” with mixed results. What makes analytics different?

That’s a good question. The examples you mentioned had to do with infrastructure – having a larger disk drive and how corporations remember things. Analytics isn’t an engineering thing; it’s science in the sense that it’s about content; it’s about substance; it’s about applying certain kinds of math; it’s about finding meaning in the data. The data is not just about a bunch of boring ones and zeros; it’s a recording of business history.

Of course, corporations need engineering and infrastructure to keep and store the data and to be able to access it. With analytics, though, we find out what the data is actually telling us and what we can learn from it, and that speaks directly to the heart of improving the mass-scale operations that organizations conduct. The way – the ultimate way – to improve those operations is by guiding them with predictions on a per-individual basis.

The number of analytics-oriented conferences are popping up almost as fast as the number of analytics-oriented books. How do you keep PAW conferences fresh and relevant in such a competitive, fast-changing environment?

It’s been astonishing just how many big data and other kinds of analytics conferences have popped up in the last two or three years. We launched Predictive Analytics World in February 2009, and the conference now takes place seven times a year and that number will probably increase in 2014, including conferences in Canada and Europe.

We have a small number of repeat speakers who are just amazing – you might call them rock star consultants – and we attract many interested brand-name practitioners who are at the top of their craft and have great stories to tell from their organizations. The program is extremely rich, but I think the main differentiator from the other events is that we’re focused very specifically on predictive analytics. Very few other events have attempted to compete directly with us in that way. I think there are a lot of people out there who are interested in that sort of clear focus on predictive analytics rather than the broader realm of analytical methodologies.

As the head of PAW, you’ve no doubt observed dozens if not hundreds of organizations and their respective analytical expertise. If you had to pick just one organization that “really gets it,” an organization that best uses the power of analytics for competitive advantage, who would it be?

I’ve seen many, many such examples. In marketing there’s everyone from Target on down to small organizations like Vermont Country Store. There’s Harrah’s Las Vegas, which Tom Davenport made famous for their analytical methods. There’s Fed Ex and all the large cell phone companies, and that’s just within marketing applications. An insert in my book provides 147 examples across nine sectors, including financial risk, healthcare, crime fighting, government, education, etc.

As to which organization is doing the best, frankly my focus has been on finding juicy individual case studies rather than evaluating an organization’s overall analytical performance. I think there’s a need for that type of thing, perhaps some kind of award that’s given to a company for general analytical success in a particular sector. I see that as a worthy exercise, but it’s not something I have studied in a coherent way.  I can say there are a lot of very talented analytical professionals doing a lot of great work in many, many organizations across many different sectors.

Last question. What’s your prediction for the future of predictive analytics?

Two main things are going to continue to happen. Predictive analytics is going to penetrate further and become more pervasive. Even within the well-trodden applications areas – marketing, credit risk, fraud detection – there is so much more that can be done and it is constantly expanding. That’s more of a quantitative difference.

The place where there is a qualitative difference is where organizations predict something new that you might have not thought of as a value proposition. For example, Google uses a predictive model to help inform the ranking of search results. That might be a no-brainer since that’s the whole point of Google’s value to the user. However, Google’s revenue comes from ads. They also separately predict, on behalf of their advertisers, which new ad that hasn’t been tested yet is most likely to have perceived low quality and get a high bounce rate. So there are all sorts of behaviors that can be predicted and new values propositions that can be derived there.

The second prong is predictive analytics is going to become increasingly more apparent and visible to the end user. The end-user will perceive value in being predicted, and that, as a consumer, it is actually helping you.

There are places where that exists now. Netflix predicts which movies you’re going to like, Amazon on books and Pandora on music. In addition to products recommendations, spam filters have been greatly improved so the spam problem has been largely solved. The junk mail problem is not going to be entirely stopped because it’s a numbers game the marketers are playing, but it will be alleviated because it’s a win-win when marketers can predict those people who are unlikely to respond and say, let’s not send them what they will perceive as junk mail.

You are starting to see consumers and citizens become more and more aware of the power of predictive analytics. I included in the book as an afterword 10 predictions for the first hour of 2020 – you’re driving to work and 10 different things happen, and they are all assisted by predictive analytics. The main thing is connecting the technology – connecting your smart phones to your car, for example. All sorts of things are happening now that just need to be integrated in order to help you in your daily life.

Peter Horner ( is the editor of Analytics magazine.

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