Archive for January, 2009

January 26th 2009

Blog interviews – more predictive analytics FAQs

Here are six recent blog interviews I’ve given about predictive analytics and the Predictive Analytics World conference. These add to the running FAQ I’ve begun here.

Interview by Romakanta Irungbam on DataLLigence

  • What are the most common mistakes you’ve encountered while working on data mining projects?
  • Which approaches do you recommend/use to define the acceptable accuracy/cut-off level for a data mining project?
  • What are the new areas/domains where data mining is being applied?
  • And more

Interview by John Langford on

  • How fast or difficult is it to transfer academic methods to business use?
  • And more

Interview by Sandro Saitta on

  • Data mining, machine learning, knowledge discovery in databases, pattern recognition, etc. Are these fields really different?
  • What is the most common data mining question you have heard?
  • Imagine that I can give you any data set by tomorrow. What kind of data would you like mining?
  • And more

Interview by Vincent Granville on AnalyticBridge

  • Which analytical fields are likely to experience growth, and why?
  • Which methodologies might become obsolete, which ones are likely to entertain growth?
  • What do you recommend for students starting an analytical career or choosing a University curriculum?
  • What are the biggest successes of data mining and statistical sciences in the corporate world?
  • What are the best practices for analytic professionals?
  • And more

Interview by Lars Johansson on WebAnalysts.Info

  • What is your definition of predictive analytics?
  • And more

Interview by Gary Angel on SemAngel

  • Why do you think analytics - especially advanced analytics – has proven challenging for many industries to really embed?
  • Do you sometimes find yourself surprised at the low-level of analytic sophistication in even very big organizations with very large marketing budgets?
  • And more

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January 23rd 2009

Predictive analytics FAQ #1: Prerequisites for success

Predictive analytics FAQ #1: What does it take for predictive analytics to deliver business value – what are the prerequisites for success?

Predictive analytics can only succeed with the right ingredients in place:

  • One or more experts in-house or deeply engaged
  • A business case for predictive analytics deployment, such as one of the business applications listed in this article (i.e., a way a predictive model can and will be used, rather than just being a nifty model that may not provide business value); management buy-in for the integration and deployment of predictive scores
  • Sufficient data to train a predictive model for the prediction goal at hand
  • General understanding and buy-in of a predictive analytics initiative by stakeholders across business functions
  • Implementation of organizational process best practices.  For analytics, this means CRISP-DM (Cross-Industry Standard Process for Data Mining — or equivalent.  An iterative process that ensures comprehension, feedback and buy-in is attained across a group of relevant managers at key phases of a predictive analytics project
  • When initial deployment success is achieved, sufficient executive buy-in to facilitate long-term maintenance that keeps the deployment alive and effective

Some of these are elusive; if one goes astray, adoption or longevity is not attained.

The good news is that in fact these ingredients usually do exist for mid-tier to large companies – and often for smaller companies, if they have data pertaining to enough customers or prospects.  And, with these ingredients place, predictive analytics delivers high returns – significantly higher than analytics that are not predictive in nature.  An IDC study showed that predictive analytics initiatives show an average ROI of 145%, in comparison to 89% for non-predictive analytics (“Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study,” September, 2003).

What makes the difference is widespread understanding and buy-in, executive buy-in (and perhaps a bit of executive understanding :), and adoption of best practice business processes (on top of killer core analytical methods).  This is where Predictive Analytics World comes in.  There’s no better way for non-experts to learn what predictive analytics does and how it works – and to become convinced of its effectiveness – than named case studies, which is why PAW’s program is built primary of such success stories, across verticals.  See the full program, at

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January 15th 2009

LIVE WEBCAST: Predictive Analytics – Optimizing Business and Reducing Costs Across Verticals

Join me on a webinar and hear my presentation next Wednesday!

“Predictive Analytics – Optimizing Business and Reducing Costs Across Verticals” A free webinar hosted by BetterManagement.

Wednesday, January 21
3:00 p.m. EST

Here’s the description:

In this webcast, Dr. Eric Siegel will summarize the wide range of business applications of predictive analytics, illustrating how each provides business value by predicting a different type of customer behavior. “These predictions translate directly into actionable steps for achieving increased returns by driving operational decisions. In so doing, an enterprise can optimize their strategies based on what customers WILL do.”

In tough times, attention turns away from increasing returns, and towards decreasing costs – Dr. Siegel will focus especially on the application of predictive analytics to lowering costs without decreasing business. Case studies will be overviewed for the more pervasive commercial applications of predictive analytics.

For more information and to register, click here

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January 10th 2009

Take the Predictive Analytics World Survey

As predictive analytics quickly expands across verticals and applications, we need your help to understand what the evolving landscape looks like. Please take a few minutes, answer a handful of questions, and help us keep you informed. Click here to take the survey Survey result will be made available before Predictive Analytics World. The PAW survey focuses on business applications of predictive analytics, complementing the Rexer Analytics 2008 Data Miner Survey, which covers the most popular software tools, which verticals have embraced modeling and more.  Access the Rexer survey results for free.

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January 7th 2009

PAW Speaker Wins the Netflix Progress Prize

News flash: The Netflix Prize leader, presenting at Predictive Analytics World, Feb 18-19 in San Francisco, won the Netflix Progress Prize last month! The last three years of Netflix's "brutal" competition have seen remarkable innovation that to this day is edging the needle closer to the 10% mark required to win the $1 million prize. Team "BellKor in BigChaos" achieved a 9.44% increase to claim the Progress Prize, and the team has since increase their performance to 9.63% – they're getting pretty close, folks.  In a sense, that is 96% of the way there. Winning team member Andreas Töscher will speak on "Advanced Approaches for Recommender Systems and the Netflix Prize" – see: Product recommendation systems is a hot application of predictive analytics for all kinds of business.  This is where the rubber hits the road, making it possible to target cross-sell across thousands of products, taking steps towards evening out what is typically a "long tail" distribution of sales, and improving customer satisfaction by helping them find what they want most from overwhelmingly large selections. Several other sessions at PAW-09 will also address product recommendations, including:

With all this, attendance is recommended…

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