Andreas S. Weigend, Ph.D. was Chief Scientist at Amazon.com, where he specialized in understanding how people behave online, helping Amazon build its customer-centric, measurement-focused culture.
Has built his expertise is built on my academic background in predictive modeling, machine learning, cognitive science, and behavioral economics. 15 years of consulting have enabled me to apply this knowledge successfully in the real world. He now works as an independent consultant with firms including World Economic Forum, Alibaba, Lufthansa, MySpace, and Nokia, helping some of the most brilliant executives around the world leverage user data to produce innovative products and business models. He also teaches at Stanford and UC Berkeley, and is often invited to speak at international events.
Check out the three minute podcast on the PredictiveAnalytics.org website.
Bill Cullifer, PredictiveAnalytics.org: Good afternoon. Dr. Weigend Bill Cullifer, calling with predictiveanalytics.org
Andreas S. Weigend, Ph.D: Hello.
Bill Cullifer, PredictiveAnalytics.org: Thanking you for agreeing to the call.
Andreas S. Weigend, Ph.D: Sure.
Bill Cullifer, PredictiveAnalytics.org: On the topic of predictive analytics, for the subscribers of this podcast us with a couple of specific walk aways.
Andreas S. Weigend, Ph.D: All right. So, let’s unpack the term predictive analytics. One element is analytic, the other element is predict. What predictive analytics for me is the focus on prediction; in the ’90s, a lot of emphasis has been on given a set of data, what insight can we get. Often those insights were not actionable. Then in the late ’90s, people focused on making predictions, but still predictions off dead data for dead data and I spend lots of tutorial time for top Wall Street quantitative traders explaining to them how to do backtesting and cross validation and all of those things and then in the last two years as the corporate communication has dropped dramatically and we actually are now able to do experiments very cheaply, we move from what I call dead data to live data. So, the problem has been no longer to be given a set of data what insights can we get, but given the problem what data can we get. So, that is the shift which I elaborate on and if you think about companies here, of course is a beautiful example, where people just give lots of data about themselves and if you reveal your intention then it is often much easier to make predictions than if we just have your data from the past. If you say, you want to go on a date on Valentine’s day, then that is information, that is we try to figure out from your clicking behavior on Facebook what you might want to do on Valentine’s day.
Bill Cullifer, PredictiveAnalytics.org:: Very well said. I am curious to know are there barriers to entry at this point or is it just that simply an educational process for those decision makers to understand how predictive analytics.
Andreas S. Weigend, Ph.D: Well, it truly is a paradigm shift and I typically call it the consumer or the customer data revolution and it is no longer about which analysis package you buy or which algorithm to use or what the mindset that CRM, customer relationship management, which we are at the end of predictive analytics seeing one who they should be sending their mailing to, what’s is called CMR, not CRM, but CMR, customer managed relationship, where the customer is now at the center and manages the relationship with potential vendors. Sometimes, I also call it that we move from e-business where the firm in the center to me-business where again the customer is in the center. So, it is not really about buying software packages, but it is about companies realizing that the customer is willing to give them information if he gets value out of it and not just to help the company, but ultimately it will help themselves.
Bill Cullifer, PredictiveAnalytics.org: Excellent. Well said. Terrific summary. I certainly appreciate the perspective and your time today.
Andreas S. Weigend, Ph.D: Absolutely.