November 18th 2014

Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott, Smarter Remarketer

Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott, Smarter Remarketer

By: Eric Siegel, Founder, Predictive Analytics World
 

In anticipation of his upcoming conference keynote and workshops at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Dean Abbott, Co-Founder and Chief Data Scientist, Smarter Remarketer, 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: I’ve built models that predict a wide variety of behaviors and patterns. A short list is provided here:

  • Customer behavior: response, churn, product up-sell and cross-sell, best marketing creative, days to next purchase, days to next visit;
  • Signals (radar): tank, truck column of tanks; (sonar): man-made vs. biologic;
  • Financial: fraud or suspicion of fraud, debt repayment period, debt repayment amount, insurance claim repayment likelihood, claim amount of repayment.

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

A:  I’ll speak to Smarter Remarketer, Inc., the company I’m co-founder of and Chief Data Scientist. There is no one specific way our predictive models drive decisions, but they are involved in the decision-making process in several ways, all related to selecting customers to promote to, whether that be selecting customers to send an email to, show a display ad, or content on a page that is of greater interest to the customer.

Consider our models that predict the likelihood that someone will purchase a product during a visit to the company’s web site within 3 days. Each visitor is scored while they browse on the web site and at the end of their session. The company now wants to create a new campaign to increase sales of a particular product by emailing them a promotion code with a 20% discount. If the customer is likely to purchase a product on the web site within 3 days, the models will exclude these customers from the email list; why take away margin from sales that are likely to occur anyway. Or what if a customer was very likely to purchase within 7 days last week but is no longer likely this week? This is a form of churn (but based on expected behavior, not actual behavior), and these customers could be given incentives to visit again.

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

A: It is difficult to describe most of the results my models have generated because they are considered sensitive information for the company or government agency. I’ve had models in use by organizations for 10 years before they were refreshed. I’ve had another model so successful that it was put on the “do not tell” list by the organization because it became a strategic initiative for the organization. I’ve had fraud models identify multi-million dollar cases to investigate that were clearly fraud but had previously eluded detection.

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

A: Most surprising? There have been many surprises over the years, usually related to the data itself and patterns of behavior that we may overlook, but are important nevertheless. For example, with the days to next purchase models, one expects that visitors on a web site who look at lots of hot products are more likely to purchase soon; these are engaged visitors. However, it turns out that some of the most likely purchasers are those who visit just one item. The vast majority of the time, one-item visitors are not engaged and therefore are unlikely to purchase. But, if these one-item visitors were previously highly engaged, it’s a different story; they are focused like a laser beam on one product only. So the surprise was that there is this subset of visitors who look awful but are actually fantastic!

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

A: The most important take-away in my talk is this: When you prepare data for modeling, think about how the algorithms interpret the data. Each algorithm has weaknesses that can result in strange or misleading behavior. It’s our job as predictive modelers to help the algorithms do the best job they can.

Q: In addition to keynoting, you will be teaching two one-day workshops at PAW San Francisco, Supercharging Prediction with Ensemble Models and Advanced Methods Hands-on: Predictive Modeling Techniques. How would you advise attendees to choose between these workshops and would it even make sense to attend both?

A: There are many workshop options, and all of them are worthy of attending. I think of the Supercharging and Advanced Methods workshops as complementary to your Online Introduction to predictive and John Elder’s Modeling Methods, with the sequence being (1) Intro, (2) Modeling Methods, (3) Advanced Methods, and (4) Supercharging. The Modeling Methods can be taken the day before Advanced Methods in the same conference; Modeling Methods provides a framework for predictive modeling, and Advanced Methods lets you try it out on commercial software. Supercharging takes predictive modeling to the next level, introducing the methods that win modeling competitions and have provided me with extra accuracy has made the difference between successful models and very successful models in my consulting practice.

Don’t miss Dean Abbott’s keynote presentation, "The Revolution in Retail Customer Intelligence," March 31, 2015, 8:50-9:40 am,  and workshops at Predictive Analytics World San Francisco, March 29-April 2, 2015.

By: Eric Siegel, Founder, Predictive Analytics World

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November 11th 2014

Wise Practitioner Predictive Analytics Interview Series: Scott Gillespie, Managing Partner of tClara

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2015

 

In anticipation of his upcoming conference presentation at Predictive Analytics World for Workforce, “Using Predictive Analytics to Predict and Manage "Road Warrior" Burnout (Frequent Travelers)” Greta Roberts interviewed Scott Gillespie, Managing Partner of tClara.  View the Q-and-A below to see how Scott has incorporated predictive analytics into the workforce of tClara. Also, glimpse what’s in store for the new PAW Workforce conference.

Q:  In your work with predictive analytics, what specific areas of the workforce are you focused on, (i.e., optimizing workforce productivity, using big data to solve workforce challenges, building a workforce analytics driven culture, etc.)?

A:  The corporate road warrior – the people whose jobs require a significant amount of travel.

Q:  Do you primarily work inside of HR – or inside of the Line of Business?  If Line of Business – which one(s):

A:  Our primary stakeholder – so far – is the corporate travel manager.  We're knocking on HR's door, but find it difficult to identify the HR executive most interested in retention and employee engagement.  We're also keen to identify the LOB executives with large travel budgets.

Q:  What workforce outcomes do your models predict?

A:  We identify cohorts of travelers with high risk of burning out from their travel workloads.

Q:  What is one specific way in which predictive analytics actively drives decisions?

A:  Companies can tailor their travel policies to address the needs of at-risk road warriors, such as encouraging less weekend travel, or allowing a better class of hotel.

Q:  Can you describe a successful result, such as the predictive lift of one of your models or the ROI of a predictive analytics initiative?

A:  Our value prop is reducing the cost of turnover among a very valuable segment of any company's workforce.  

Q:  What is an example of surprising discoveries you have unearthed in your data?

A:  We measure traveler wear and tear using a Trip Friction(R) metric.  Our data shows that turnover among frequent travelers is less related to the accumulated amount of Trip Friction, and more related to the pace and intensity of travel.

Q:  Do you feel any urgency you want to pass along to your fellow HR and Business Executives to implement predictive analytics to help solve employee challenges?  Why?

A:  As a pioneer in this niche of predicting traveler burnout, I am very keen to see companies pay attention to the predictive power of our models.  It's hard to get disparate functions, e.g. Travel, HR, Procurement and LOB Management, to collectively grasp the opportunities offered by predictive analytics.  I hope the PAW Workforce Conference helps to solve this problem.

Q:  SNEAK PREVIEW:  Please tell us a take-away that you will provide during your Presentation at Predictive Analytics World for Workforce.

A:  We'll provide benchmarks for recognizing true road warriors – those that travel more than 90% of all other travelers – and therefore, those at significant risk of traveler burnout.

Don't miss Scott Gillespie’s conference presentation, Using Predictive Analytics to Predict and Manage "Road Warrior" Burnout (Frequent Travelers), at PAW Workforce, on Tuesday, March 31, 2015, from 3:05-3:25 pm. Click here to register for attendance

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

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September 30th 2014

Wise Practitioner – Predictive Analytics Interview Series: John Cromwell, M.D., University of Iowa Hospitals & Clinics

Wise Practitioner – Predictive Analytics Interview Series: John Cromwell, M.D., University of Iowa Hospitals & Clinics

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of his upcoming keynote conference presentation at Predictive Analytics World Healthcare in Boston, “Real-Time Modeling of Surgical Site Infections,” we asked John Cromwell, M.D., Associate Professor at University of Iowa Hospitals & Clinics, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what area of healthcare are you focused on (i.e., clinical outcomes, insurance, quality improvement, etc.)?

A: Focusing is difficult given the broad array of challenges facing hospitals today. Having said that, our work has been primarily on clinical outcomes and quality improvement.

Q: What clinical outcomes do your models predict?

A: My group works on quality and performance in surgery. In the context of surgical patients, we are modeling readmissions, surgical site infections, and the development other hospital-acquired infections such Clostridium Difficile.

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

A: PA allows us to prioritize use of both institutional and community resources for improving outcomes for a large population. As an example, surgical site infections are dangerous and expensive. Being able to predict surgical site infections from the operating room before a patient’s incision has been closed allows us to change our wound management strategy up front. Targeting of resource-intensive and invasive wound management strategies to patients who will benefit the most is good for everyone.

Click here to read the rest of this interview.

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September 23rd 2014

Wise Practitioner – Predictive Analytics Interview Series: Linda Miner, Ph.D., Southern Nazerene University

Wise Practitioner – Predictive Analytics Interview Series: Linda Miner, Ph.D., http://www.predictiveanalyticsworld.com/patimes/wp-content/uploads/2014/08/Linda_Miner-image.jpgSouthern Nazerene University

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of her upcoming conference presentation at Predictive Analytics World Healthcare in Boston, “Developing a Mortality Prediction Model for Disseminated Intravascular Coagulation (DIC),” we asked Linda Miner, Ph.D., Professor at Southern Nazerene University, a few questions about her work in predictive analytics.

Q: What clinical outcomes do your models predict?

A: We would like to be able to predict that someone is at risk of dying from DIC symptoms, based on admission variables.

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

A: If death can be predicted before symptoms of DIC then extra care can be exerted in treatment. We might even be able to figure out which of the patient entry conditions might be most predictive for an individual and more tailored counter measures taken.

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

A: It is possible that even given the obvious benefit of having people live, hospital stays might be shortened and resources might not be wasted on ineffective treatments with the more targeted treatments.

Click here to read the rest of this interview

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September 16th 2014

Wise Practitioner – Predictive Analytics Interview Series: Marty Kohn, M.D. of Jointly Health

Wise Practitioner – Predictive Analytics Interview Series: Marty Kohn, M.D. of Jointly Health

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of his upcoming conference keynote at Predictive Analytics World Healthcare in Boston, “Big Data and Clinical Decision Support,” we asked Marty Kohn, M.D., Chief Medical Scientist at Jointly Health, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what area of healthcare are you focused on (i.e., clinical outcomes, insurance, quality improvement, etc.)?

A: Jointly Health focuses on patients with complex chronic diseases to improve health, reduce avoidable hospitalizations and acute care events and, as a result of decreased need for expense acute care, reduce costs.

Q: What outcomes do your models predict?

A: We predict which patients are likely to deteriorate so that a timely intervention can avoid the problem.

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

A: By identifying patterns in home monitoring physiologic data, coupled with interaction with the patient and the patient’s caregivers, we can give the care team early warning of a worsening of the patient’s clinical status. We develop such patterns in a way that is unique for each patient, allowing the care team sufficient warning to treat the problem when it is more likely to be successful.

Click here to read the rest of this interview. 

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September 9th 2014

Wise Practitioner – Predictive Analytics Interview Series: John Foreman of MailChimp

Wise Practitioner – Predictive Analytics Interview Series: John Foreman of MailChimp

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference keynote at Predictive Analytics World Boston, “Problems, then Techniques, then Toys. Keeping Your Predictive Analytics Right-side Up,” we asked John Foreman, Chief Scientist at MailChimp, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: At MailChimp, we use predictive modeling across the application to improve the experiences of our users. Some examples:

  • We predict users who are unlikely to send spam, and we allow them to begin sending email through the system without manual account vetting (manual vetting slows people down by a day)
  • We predict users who are likely to send spam, and we shut them down before they send in order to protect our email-sending ecosystem
  • We predict users who are on a free account but who are likely to pay in the future. We then give them the same customer support given to currently paid users
  • We predict users who are most certainly not bots and we remove reCAPTCHA entirely from the app for them
  • We predict the knowledge base articles that a user is most likely interested in when they contact customer support
  • We predict the best time to send an email address marketing content and provide that to users in our Send Time Optimization (STO) system
  • Given a small segment of email addresses, we predict other email addresses on a user’s list that have the same interests to facilitate better segmentation and targeting
  • We predict demographic data on email addresses

These are just some examples of the different models in play.

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

A: Predictive analytics is a key part of our user on-boarding and compliance process. MailChimp has over 6 million customers, and without predictive modeling, the company would be left linearly scaling the headcount of customer support and compliance. Predictive models enable us to automate the easy jobs, allowing our compliance personnel to hunt down the worst of worst in terms of bad actors. This lowers our headcount, saving us a great deal of money. We are able to manage 6 million customers with less than 300 people total at the company.

Furthermore, our user-facing predictive products (Send Time Optimization & Segment

Click here to read the rest of this interview.

 

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September 2nd 2014

Wise Practitioner – Predictive Analytics Interview Series: Jack Levis of UPS

Wise Practitioner – Predictive Analytics Interview Series:

Jack Levis of UPS

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference keynote at Predictive Analytics World Boston, “UPS Analytics – The Road to Optimization,” we asked Jack Levis – Senior Director, Process Management at UPS, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: We use a tremendous number of predictive and prescriptive models at UPS. They are used to help make decisions, which range from where to build a facility and what type of aircraft to purchase to which packages go in each trailer and how to maintain our delivery fleet.

We currently have 700 dedicated resources working on a system called ORION, which has been called “arguably the world’s largest Operations Research Project.” With ORION, we are using analytics to determine the best way for a driver to serve our customers at the lowest cost.

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

A: We do not do anything by the “seat of our pants.” Analytics is engrained so deep in our culture, it is difficult to separate analytics driven decisions from normal business processes.

In 1954, our CEO said, “If we did not have operations research, our rate of growth might have been affected. As we grow in size, our problems increase geometrically. Without Operations Research, we would be analyzing our problems intuitively only, and we would miss many opportunities to get maximum efficiency out of our operations.”

Analytics has helped UPS make better decisions in all parts of our business.

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

A: In 2003, UPS began using predictive models to better plan our delivery operations. This suite of tools called Package Flow Technologies along with Telematics has been responsible for a yearly reduction of 85 million miles driven per year. This reduced our fuel needs by over 8 million gallons and reduced carbon emissions by 8,500 metric tons.

In addition, because the analytics and business processes are fully aligned we have been able to deploy new products for customers. UPS’ MyChoice is a prime example of that.

Click here to read the rest of this interview.

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August 26th 2014

Wise Practitioner – Predictive Analytics Interview Series: Sameer Chopra of Orbitz

Wise Practitioner – Predictive Analytics Interview Series: Sameer Chopra of Orbitz

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference keynote at Predictive Analytics World Boston, “Blackjack Analytics: A Surprising Teacher from Which All Businesses Can Learn,” we asked Sameer Chopra, GVP of Advanced Analytics, Orbitz Worldwide, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: We have predictive models for various applications. For instance:

  • In the CPC (cost-per-click) online marketing channels we have response models to predict the Revenue Per Click (RPC).
  • We have user level purchase-propensity models (i.e. the likelihood of Eric transacting on Orbitz in say the next 24 hours – vs. Sameer transacting). As you can imagine, this can be an effective lever to use real-estate on our site effectively (eg: whom to show ads to, as a simple use case).
  • We also have models to predict user-attrition. Unlike industries with black & white subscriber models, we live in the gray — so a tool like Survival analysis can be a helpful friend.
  • We also have built credit-card fraud models.
  • I should highlight that we do actively test uplift models to get smarter about segments to pursue and whom not to. etc.

These are just some examples of the different models in play.

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

A: There are numerous areas where PA is driving value. One specific example where it actively drives decisions is in online marketing: determining how much to bid in CPC channels such as Google Adwords, Google Hotel Price Ads (HPA), or Travel Research partners such as TripAdvisor etc.

Click here to read the rest of this interview.

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August 20th 2014

Top Reasons to Attend Predictive Analytics World for Government

Eight Reasons to Attend Predictive Analytics World for Government

The clock is ticking and Predictive Analytics World for Government kicks off in less than ONE month on September 15th in Washington D.C. Have you been thinking about registering? Here are just some of the reasons you should attend this year’s conference.

  1. Stay on the Cutting Edge of Predictive Analytics in Government – Predictive Analytics is growing in the government and public sectors. This conference is designed to help agency managers understand how they can apply predictive analytics to more effectively and efficiently accomplish their mission. Learn about the latest progress in the field and apply it to your work.
     
  2. Solve Real-World Problems – More and more, data analytics are being used by government agencies to solve real-world problems. They are utilized to solve issues in surveillance, security, fraud detection, healthcare and in many other realms of the government. Learn about the difference that predictive analytics can make in these scenarios and in many more.
     
  3. Hear from Top Keynotes and Speakers – With speakers from the IRS, Federal News Radio, Elder Research Inc. and, potentially, the Senate, you will learn important information from authoritative leaders in the predictive analytics and government world.

 

  1. Digest the Incredible Agenda – The 4th annual Predictive Analytics World for Government agenda is packed with substantial speakers, workshops, topics, and content that you won’t want to miss. View the full agenda here.
     
  2. Exchange Best Practices – Witness many case studies and hear experiences that have paved the way toward the best implementation of predictive analytics in government. Discover similar routes to apply toward your company’s future success.
     
  3. Build your Network with Skilled Executives in your Field – Join 400+ participants at PAWGov. With hundreds of fellow federal, state, and local government leaders, analysts, and program managers, you will build a long-lasting network that grows far beyond the conference walls.
     
  4. Be Part of the ONLY Vendor-Neutral Predictive Analytics Conference for Government – With such a specific focus, this conference will nail down exactly what the hot topics are relating to your work with predictive analytics among government.
     
  5. Increase Your Performance – The conference delivers case studies, expertise and resources to empower you in federal, state, and local government to best achieve your objectives. Apply the takeaways you’ve gained immediately to increase your performance and deliver more effective and efficient results.

 

This is just a glimpse of the many reasons to attend Predictive Analytics World for Government. Join us for September 15th – 18th at the Grand Hyatt Washington. We look forward to seeing you there!

 

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July 22nd 2014

Eleven Analytics Conferences – New Verticals

                                                 

Eleven Analytics Conferences – New Verticals
 
Here is the line-up of Predictive Analytics World conferences and workshops coming over the next year. I'm the founding chair, so please let me know if you have any questions about any event programs.
 
Predictive Analytics World covers all the basics for both expert practitioners as well as newcomers. As the universal, cross-vendor meeting place that brings together the who's who of predictive analytics, PAW delivers not only unique opportunities to gain knowledge, but the industry's premier networking event.
 
NEW VERTICALS: HEALTHCARE & WORKFORCE. In addition to the usual business-focused PAWs, and the annual PAW Government, the inaugural PAW Healthcare (http://pawcon.com/health) & PAW Workforce (http://www.pawcon.com/workforce) are coming – these arenas are exploding with movement and interest.
 
CALL FOR SPEAKERS now open for 2015 events – see http://pawcon.com/cfs
 
———————–
 
2014 EVENTS – IN COMING MONTHS:
 
PAW GOVERNMENT – Sept 15-18, 2014
Discount Code for $150 off: LIN150
Early Bird Registration Ends July 25
 
PAW BOSTON – Oct 5-9, 2014
Discount Code for $150 off: LIN150
Early Bird Registration Ends Aug 15
 
PAW HEALTHCARE – Oct 6-7, 2014
Early Bird Registration Ends Aug 15
 
PAW LONDON – Oct 29-30, 2014
Discount Code for $150 off: LIN100
Early Bird Registration Ends Sept 1
 
PAW BERLIN – Nov 4-5, 2014
Discount Code for $150 off: LIN100
Early Bird Registration Ends Sept 20
 
WORKSHOPS: A plethora of 1-day analytics workshops are held alongside PAW
(For other cities, navigate from: http://www.pawcon.com)
 
- – - – - – - – - – - – -
 
2015 EVENTS – SAVE-THE-DATES / CALLS-FOR-SPEAKERS:
 
PAW SAN FRANCISCO – March 29-April 2, 2015
Speaker proposals due Sept 26 – http://pawcon.com/submit.php
 
PAW WORKFORCE – March 30-April 1, 2015 (San Francisco)
Speaker proposals due Sept 1 – http://pawcon.com/submit_workforce.php
 
TEXT ANALYTICS WORLD SAN FRANCISCO – March, 2015
Speaker proposals due Sept 1 – http://tawcon.com/call-for-speakers
 
PAW TORONTO – May, 2015
Speaker proposals due Oct 3 – http://pawcon.com/submit.php
 
PAW CHICAGO – June, 2015
Speaker proposals due Jan 9 – http://pawcon.com/submit.php
 
PAW MANUFACTURING – June, 2015 (Chicago)
Speaker proposals due Jan 9 – http://pawcon.com/submit.php

 

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