December 16th 2014

Wise Practitioner – Predictive Analytics Interview Series: David Schey of Digitas

Wise Practitioner – Predictive Analytics Interview Series:  David Schey of Digitas

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Uplift Modeling Versus Traditional Response Modeling – Which One is Right for You?, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked David Schey, Senior Director, Analytic Consulting Group, Digitas, 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:  We model a wide variety of behaviors including response, attribution, spending, attrition, ‘clone’, customer value, market basket, fraudulent activity, next most logical product/product lifecycle, insurance claims, and a variety of credit evaluation dimensions.

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

A:  Predictive analytics delivers a superior experience to our client’s customers. By more actively engaging with customers, we optimize the customer experience, increase retention rates, and enhance spending patterns. Our models help managers deliver the right message to the right customers through the right medium. Contact decisions are frequently made based solely on some of the models referred to above.

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

A:  A major retailer found that control groups were performing in line with mailed groups. Although response models were used, they did not provide the incremental lift that the retailer was expecting. Out uplift model resulted in identifying 20% of the mailed population that provided 16.5 basis point lift, a result that the retailer had never experienced.

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

A: The primary lesson is not to take data at face value! During one exercise for an auto manufacturer, we found that 39% of our clients’ customer base owned 9 cars! Seems outlandish, and it was. The value ‘9’ was used as missing value indicator. If it’s too good to be true-it isn’t.

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

A:  There’s more than one way to skin a cat. While there are several approaches for developing uplift models, not all of them provide satisfactory results all the time.  Also, uplift models provide a completely new lens by which direct marketers will increase the sophistication of their contact strategy.  The concept of not "waking a sleeping dog" (i.e. customers that are negatively influenced by communications) is often foreign to traditional direct marketing principals…not only will you increase profit by suppressing certain customers, but those funds can be reallocated to new communications for even greater impact! 

Don't miss David Schey’s conference presentation, Uplift Modeling Versus Traditional Response Modeling – Which One is Right for You?, at Predictive Analytics World San Francisco, on Tuesday, March 31, 2015, 3:55-4:40 pm.  Click here to register for attendance. 

By: Eric Siegel, Founder, Predictive Analytics World

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating. He is the author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. 

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December 9th 2014

Wise Practitioner – Workforce Predictive Analytics Interview Series: Carl Schleyer of 3D Results

Wise Practitioner – Workforce Predictive Analytics Interview Series: Carl Schleyer of 3D Results

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference Carl_Schleyerpresentation, Using Predictive Analytics to Create a Leadership Index, we interviewed Carl Schleyer, People Strategist & Senior Principal Consultant Workforce Analytics and Planning at 3D Results. View the Q-and-A below to see how Carl has incorporated predictive analytics into the workforce of 3D Results. 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:  My personal focus on value creation and enhancing the internal brand of HR have led to analytic work interventions across the entire employee lifecycle as well as helping Operations with scheduling and productivity optimization. 

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

A:  While I grew up in Line/Operations at a national retailer, my last 8-10 years have focused exclusively in HR Analytics.

Q:  What workforce outcomes do your models predict?

A:  Some of my favorite predictive interventions involve:

1) Staffing algorithms that proactively determine where vacancies should be posted

2) Performance reviews simplified with metrics and goals that inspire profitable employee behaviors and

3) Identification of where Leadership Risk exists within organizations (more on that later).

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

A:  Candidate preferencing.  Like it or not, our lives are becoming increasingly dependent on algorithms.  Many employers are using them to mine through the big data their Applicant Tracking Systems produce in order to prioritize applicants and recruiter workload.  If you can get a referral from an employee at your targeted company, you’ll likely more than double your chances of an interview and offer.

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:  I worked on an enterprise sales force effectiveness program that designed, tested, communicated, piloted, executed, and validated a data driven approach to helping field managers coach consultative sales associates. This approach leveraged targeted metrics that were tied to profitable employee behavior, and then incentivized through a new variable compensation plan.  Our first department was so successful we couldn’t convince the business to stay in pilot phase longer and after 3 months they deployed nationally.  From there we took our model to other departments and 12 months after the multi-department implementation we had generated an 8.3% improvement in productivity and an incremental $100 Million in margins.  Those were fun times and I was blessed to be surrounded by talented team members.

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

A:  This is the hardest part of our work as there are often many layers to a problem and it can be difficult to know when to stop.  Outliers in the data often point to process or systems problems and can be interesting but time consuming to chase.  But it’s the spicy myth busting discoveries that immediately come to mind…  Once we proved that internally placed managers run more profitable stores in their first 12 months.  This finding reversed an alarming external placement trend that was nearing 50% AND changed the business focused towards developing internal bench strength.  Another politically charged discovery was around the cost of a FT employee.  Benefit costs are often managed by reducing the ratio of FT employees, but we proved that the performance differential on key financial metrics of FT versus PT employees fully offset the cost of benefits.  As a result we added FT jobs to the following year’s staffing plan instead of cutting them.

Q:  What area of the workforce do you think has seen (or will see) the greatest advances or ROI from the use of predictive analytics?

A:  Culture.  The intentional use of culture to drive business results is not done enough.  Many feel culture of an organization develops naturally and cannot be changed. However, culture is driven by specific leader and employee behaviors.  Today’s technology connects leaders with information on an unprecedented scale. Enterprise-wide data warehouses and big data analytics provide the ability to inform decisions and validate actions like never before. With this information, empowered leaders can manage performance in deeper and more meaningful ways, inspire employee behaviors and achieve desired results.  The data outcomes of those actions could then be identified, quantified and used as predictive measures that help an organization develop or maintain its desired culture. That means we should be able to build models to assess the impact of organizational or leadership changes on culture, engagement, and ultimately the bottom line.  Cultural Models would likely have executives rethinking many enterprise decisions.

Q:  Why do you think Business Leaders, HR Leaders and Analytics professionals should attend Predictive Analytics World for Workforce?

A:  This is still an emerging space.  Traditional educational degrees do not adequately prepare practitioners to do the work.  I believe that all of us are smarter than any one of us and quality conferences like PAW are the best way for us to upskill ourselves and create future standards.

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:  From a business perspective there are only two options- evolve or die.  And that extinction just might happen to the Human Resource function.  If we, as HR Analytic professionals, don’t learn how to adequately solve organizational problems, someone else from Finance, Strategy or Operations will.  

Q:  What is one misunderstanding people have about using predictive analytics to solve employee challenges?

A:  We DV 8.  That was my license plate for a while.  We deviate was meant to express that humans are complex and frequently change their minds.  They are engaged one morning, but looking at a job posting e-mail that same afternoon.  What drives and motivates someone one month/year isn’t of interest the next.  The needs of the workforce are so segmented and dynamic it’s difficult to get the degrees of precision that mathematicians, chemists, or engineers expect.

Q:  How involved has the business unit been in the work you’ve done inside of your organization?

A:  I have a somewhat controversial answer to this question.  Our work should be focused on improving the lives of our employees, the brand of our HR function and the profitability of our organizations.  This means the business unit has to be a strong partner in our work and should be actively involved with the analysis, design, testing, and deployment of our interventions.  In fact, my personal formula for success includes more involvement with the business than with my HR partners.  Just don’t tell that to the CHRO who lobbied hard to fund and grow the Workforce Analytics team.  As long as you are finding meaningful problems to solve and collaborating- you are on the right path.  The more strategic the question, the more likely we need access to information that extends beyond HR’s reach.  That means the internal owners of customer, financial, or operational data should be aware and involved in what you are doing.

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

A:  After Recruiting, my first break into the HR Generalist space was a role in Ethics.  I later had the opportunity to manage a centralized employee relations team.  It was both fascinating and scary to see what happened when the communication and collaboration between the employee and the supervisor broke down.  The process of watching over thousands of cases and seeing the negative energy and outcomes planted a question deep in my mind.  What if… What if I could detect the places where poor leadership exists?  What if I could intervene before the smoke turned to fire?  What if I could scan for management risks real-time?  What if I could identify and proactively resolve workforce conflict before it escalated?  What if I could prevent expensive employee relations scenarios from occurring?

After many focus groups, round tables, and quantitative attempts, a process I call The Digital Fingerprints of Leadership TM emerged.  At the conference I’ll present a new perspective on quantifying Leadership.  It’s a method of reimagining employee engagement, but without surveys.  Through the usage of readily available surrogate metrics you can target areas where communication, respect, and trust are breaking down.  Once identified, HR resources can be deployed, action plans can be created, and progress can be measured. 

Imagine being able to augment the once-a-year organization engagement survey? Many forward thinking companies have already realized that this snapshot in time approach to measuring the passion of their workforce is outdated and doesn’t suit their needs. The concept of leveraging existing real-time data opens the door for significant improvements to dynamically measuring action plans, retention efforts and overall business performance.

Don't miss Carl Schleyer’s conference presentation, Using Predictive Analytics to Create a Leadership Index, at PAW Workforce, on Tuesday, March 31, 2015, from 4:55-5:40 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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December 2nd 2014

Wise Practitioner – Predictive Analytics Interview Series: Josh Hemann of Activision

Wise Practitioner – Predictive Analytics Interview Series: Josh Hemann of Activision

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Cheating Detection in Call of Duty, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Josh Hemann, Principal, Game Analytics at Activision, 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 team focuses on integrating analytics into large scale, operational processes for our online, multiplayer games, and mostly for the Call of Duty franchise. A lot of that work is more about optimization rather than prediction per se. But one area that is certainly prediction/classification is algorithmically detecting cheating, which can encompass a lot of different behaviors to identify in various settings. In some settings, the prediction absolutely must happen in real-time; in others, we can do batch processing and build evidence over time.

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

A:  We have various statistical models to characterize players’ engagement, their likelihood to quit playing, and even their styles. The process of fitting and testing these models teaches us a lot about how different elements of game play affect our players, which in turn helps inform design decisions for future game features. 

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

A:  In algorithmically detecting cheating the main benefit has been scale. Before using algorithms the manual review of player telemetry data could lead to at most a couple hundred cases being identified each day. Now we can act on thousands of cases per day, leading to a healthier player community while also freeing up valuable time for my colleagues to focus on other areas.

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

A: We collect a lot of telemetry when people play online, everything from where you are on a map at a particular point in time to how many shots you fired with a particular weapon. So the following is not so much a surprise as it is my constant amazement:  I only get to work with these data because there is physics code, graphics code, server code, etc. that is rendering many tens of thousands of events for each player in a single game, at 60 frames per second, and this is happening for millions of games played every day all over the world.  

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

A:  It’s easier to make predictive analytics useful when you give a lot of attention up front to the business decisions you are trying to affect and what monetary value there is in improving them.

Don't miss Josh Hemann’s conference presentation, Cheating Detection in Call of Duty, at Predictive Analytics World San Francisco, on Wednesday, April 1, 2015, from 10:00-10:20 am.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating. He is the author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. 

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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

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating. He is the author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. 

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November 11th 2014

Wise Practitioner – Workforce 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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