September 23rd 2016

Wise Practitioner – Predictive Analytics Interview Series: Miguel Castillo at U.S. Commodity Futures Trading Commission

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference co-presentation, Words that Matter:  Application of Text Analytics at the U.S. Commodity Futures Trading Commission, at Predictive Analytics World for Government, October 17-20, 2016, we asked Miguel miguel-castillo-imageCastillo, Assistant Inspector General for Audit at U.S. Commodity Futures Trading Commission, a few questions about his work in predictive analytics.

Q: How would you characterize your agency's current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: The agency recognizes that data and technology is critical to transform the CFTC into a 21st century regulator that can efficiently and cost-effectively surveil the derivatives markets. In its Strategic Plan, Goal 1 for IT is to deliver services aligned with core mission functions of the CFTC. Its priority is to meet business needs first by empowering users with self-service technology platforms for data analysis, then by enterprise-focused automation services.  The extensive research and analytical backgrounds of its senior economists also ensures that analyses reflect the forefront of economic knowledge and econometric techniques.  Staff expertise is grounded in a solid knowledge of market institutions and practices and experience communicating the results of quantita­tive analysis.

So while the agency is building its infrastructure, collecting market data, and hiring competent economists, there is an opportunity to introduce predictive sciences into the mix.

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: One challenge is changing the business culture to balance data access with information security.  We are testing an MOU to meet this challenge.

Q: Can you discuss any near term goals you have for improving your agency's use of predictive analytics?

A: Collaborate, collaborate, and collaborate on projects that use analytics to determine use cases for “predictive analytics.”  I think it’s a matter of time before the agency fully learns from the experience of using data to its advantage.

Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: Our audit text mining experiment used a simple mathematical approach using historical reports to understand how to better plan audits.

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

A: Not only will we discuss our audit planning text mining experiment but also another effort related the agency’s rule-making.

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Don't miss Miguel’s conference presentation, Words that Matter:  Application of Text Analytics at the U.S. Commodity Futures Trading Commission, on Tuesday, October 18, 2016, from 10:30 to 11:20 am at Predictive Analytics World for Government. Click here to register to attend. 

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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September 16th 2016

Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions

In anticipation of his upcoming keynote co-presentation, Picking the Right Modeling Technique for the Problem, at Predictive Analytics World London, October 12-13, 2016, we asked Michael Berry, Analytics Director at TripAdvisor Hotel Solutions, a few michael_berry-imagequestions about his work in predictive analytics.

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

A: At TripAdvisor for Business, one of our most important products is subscription-based. We price our subscriptions based on the value our product will deliver to hoteliers in the form of increased direct bookings on their web sites. This means predicting their future traffic, click-through rates, conversion rates, room rates, average length of stay, and so on. Beyond that, I worry about all the usual things subscription-based businesses worry about: What is the probability that a subscriber will renew? What actions of ours can increase that probability? Which non-subscribers are the best prospects? What actions on our part will lead to increased owner engagement?

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

A: I’ve already mentioned pricing.  Another area is sales efficiency.  There are over 900,000 hotels listed on TripAdvisor and our salespeople can’t reach all of them. We use predictive models to pick which properties to call.

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

A: No. In a public forum like this, I generally show graphs with no numbers on the axes. Of course internally we measure things like the increase in expected value of sales leads so we know how valuable our work is.

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

A: Here’s one that surprised me a bit when I first started looking at hotel ratings data: The average bubble rating of all reviews is higher than the average bubble rating of all hotels.  Both are pretty high since people tend to like the places they picked, but the difference is noticeable.  How can that be?  Well, some properties have enormous numbers of reviews. Think The Bellagio in Las Vegas.  These properties tend to be traveler favorites so their thousands of reviews bring up the average review score. But the Bellagio is still just one hotel, so it doesn’t affect the average hotel score any more than a Motel 6 on a truck route somewhere.

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

A: There is no one best type of predictive model; you need to pick your tools to match the problem you are trying to solve.

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Don't miss Michael’s keynote co-presentation, Picking the Right Modeling Technique for the Problem on Wednesday, October 12, 2016 at 11:45 am at Predictive Analytics World London. Click here to register to attend.  

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September 6th 2016

Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management

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

In anticipation of his upcoming keynote co-presentation at Predictive Analytics World for Healthcare New York, October 23-27, 2016, we asked Ken Yale, JD, DDS, Vice President of Clinical Solutions at ActiveHealth Management, a few questions about Ken Yale 2incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Predictive Analytics, Genomics, and Precision Medicine – Separating the Hype from the Reality, and see what’s in store at the PAW Healthcare conference in New York City.

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

A:  We focus on both clinical and financial outcomes for health insurance plans, and in fact are one of the few organizations that have the capability to derive clinical variables from data. As a care management company, we believe putting clinical knowledge and insight in the hands of doctors and patients can transform the healthcare system and improve lives. We do this by finding the latest developments in the clinical literature, translating these research findings into computer algorithms that mine consumer, member, and patient data, and presenting our findings to patients, providers, and payers so they can understand the situation and take action to improve care. Our job, what we do every day, is find people and give them actionable steps to improve their health.

Q: What outcomes do your models predict?

A:  Clinical actions, quality metrics, financial costs, and other outcomes of interest to our clients – such as personal interests so we can assist an individual with appropriate care management services and behavior change opportunities.

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

A:  One way that predictive analytics delivers value is enabling us to micro-segment a population, identify a “population of one” and deliver targeted services to improve care.

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

A:  In one micro-segmentation program we were able to obtain a 74% lift in response rate when using different methods of communication designed specifically to the individual, and a 99% lift in response rates when using different kinds of messages targeted to personal interests.

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

A:  The actual variables that are predictive of chronic conditions, such as obesity, and how easily these can be measured and used to improve health and care.

Q: What areas of healthcare do you think have seen the greatest advances or ROI from the use of predictive analytics?

A: Care management and outcomes have seen the greatest advances and ROI from the use of predictive analytics. In the future, using precision medicine and genomic sequencing, we believe the ROI shall be even greater as we target care and services to individuals. At that point, “population health” will have evolved to be “personal and prescriptive health,” and we shall no longer need to use “one-size-fits-all” population norms to deliver care.

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

A:  Personalized and precision medicine cannot wait for the perfect program to be developed; you have to start somewhere, so any improvement in health quality, outcome, or cost is beneficial and will move the field forward. We shall review and discuss specific improvements in health quality, outcomes, and cost reduction.

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Don't miss Ken’s keynote co-presentation, Predictive Analytics, Genomics, and Precision Medicine – Separating the Hype from the Reality, at PAW Healthcare on Wednesday, October 26, 2016 from 9:10 to 10:05 am.  Click here to register for attendance.

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

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August 26th 2016

Wise Practitioner – Predictive Analytics Interview Series: Frank Fiorille at Paychex, Inc.

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Risk Management Algorithms – Paving the Way to Value Creation at Predictive Analytics World Financial in New York City, October 23-27, 2016, we asked Frank Fiorille, Sr. Director of Risk Management at Frank Fiorille imagePaychex, Inc., a few questions about his work in predictive analytics.

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

A: We have a portfolio of models that predict both external client behavior as well as internal employee behavior.  Our external client portfolio includes (but is not limited to) models predicting upsell opportunities, client retention, discounting, segmentation and likelihood to default on payments.  Our internal portfolio consists of models related to employee churn.  As Paychex has over 600,000 clients and generates hundreds of millions of transactions yearly it is not economical to contact every client/prospect or screen every payroll Paychex processes.  This is where our models excel as they provide resource efficiency increases as well as enhance the impact of any actions taken.

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

A: One of our most impactful model ensembles predicts client losses over time. Due in large part to the success of the model, Paychex created a dedicated client retention team solely working off the results of the model. The team proactively reaches out to clients the model has identified as likely to leave Paychex, often engaging them in correcting previously unknown service issues, pricing concerns, etc.

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

A: Attributing quantitative value to predictive modeling varies depending on the model, how it is deployed, and how the value will be interpreted. In order to deduce value from the client retention model mentioned above, we compare the loss rates of clients across model scores and retention team activity. In one particular segment, losses were cut by 50% when they had proactively been called by the retention team.

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

A1: When clients switched IT platforms, it was expected that more errors in the transition lead to higher churn rates. We investigated if this expectation was backed empirically and found that the effect was inverted; clients with more errors were less likely to leave. This finding echoed similar results where anecdotal beliefs were refuted by empirical analysis.

A2: A natural experiment arose in a segment of our data where we were able to analyze how different discount rates affected client retention rates. We investigated to see if clients that automatically received extended discounts versus lower discounts had a higher risk of leaving Paychex. We found no significant correlation, a disappointing but common result in most cases but not in this case.

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Don't miss Frank's conference presentation, Risk Management Algorithms – Paving the Way to Value Creation at Predictive Analytics World Financial NYC, on Tuesday, October 25, 2016 at 2:40 to 3:25pm. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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August 22nd 2016

Wise Practitioner – Predictive Analytics Interview Series: Scott Zoldi at FICO

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference keynote presentation, Fraud Screening for 2/3rds of All Card Transactions: A Consortium and Its Data at Predictive Analytics World Financial in New York City, October 23-27, 2016, we asked Scott Zoldi, Chief Analytics Scot Zoldi IMAGEOfficer at FICO, a few questions about his work in predictive analytics.

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

A:  Falcon models generate transaction scores which determine the likelihood of the transaction being fraudulent, by incorporating multi-faceted fraud risk assessments from the cardholders’ historic spending behavior, characteristics of merchants and transaction instruments. The higher the score is, the more likely the transaction is fraudulent.

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

A: Falcon models are data-driven analytical models built from the FICO Fraud data consortium where banks agree to pool their payment card transaction and fraud data for building the most predictive models. These predictive models mitigate the financial impact of credit and debit card fraud and improve customer experiences.  The pursuit of the highest performing model has driven the creation of a payment card fraud consortium data asset that contains more than two decades of historical data. 

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

A: Since introducing Falcon models based on the data consortium, we monitor 2.5 billion payment cards world-wide, and the payment card fraud losses have been reduced by more than 70% in the US. The data consortium has enabled the creation of 88 granted fraud analytic patents and more than 44 fraud analytic patent applications pending derived from over 3,000 researcher-years working with the asset to maximize fraud detection while minimizing customer impact.

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

A: One is that in 2014 nearly half of the fraudulent cross-border transactions on UK debit cards took place in the US, as US had not yet implemented EMV technology. The card-not-present transactions (for example, online purchase) are seen to grow rapidly from year to year in volume and the fraud rate is much higher than card-present transactions. The data also reveal the occurrence of data compromise events associated with some merchants that lead to exposure to card data in the dark web.

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

A:  One take away that will be discussed is how we utilize auto-encoder technologies to ensure that the data on which our models are built is similar to the data seen in production.  This provides an ability to monitor data through these deep learners in order to determine when we may have models that will perform sub optimally based on transaction pattern shifts in production data environments.

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Don't miss Scott’s conference keynote presentation, Fraud Screening for 2/3rds of All Card Transactions: A Consortium and Its Data at Predictive Analytics World Financial NYC, on Wednesday, October 26, 2016 at 8:50 to 9:40 am. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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August 19th 2016

Wise Practitioner – Predictive Analytics Interview Series: Thomas Klein at Miles & More GMbH

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference co-presentation, Using Predictive Models for Demand Simulation – Purchase, Response and Uplift Modeling in Practice, at Predictive Analytics World New York, October 23-27, 2016, we asked Thomas Klein, Associate Thomas_Klein IMAGEDirector Advanced Analytics at Miles & More GmbH, a few questions about his work in predictive analytics.

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

A: Our advanced analytics team at Miles & More is responsible for turning data into insights that help our organization to make the right decisions and choose the right measures to take action. The larger part of business problems we work on are focused on optimizing our multichannel marketing campaigns and creating a better customer experience: We build predictive models in order to better target marketing campaigns and deliver personalized recommendations. We also use analytics to identify, predict and test meaningful events that serve as trigger to provide relevant information and create marketing offers based on the customer's context and current needs. Always with the aim to create and improve customer loyalty for our program and for partner companies.

Precisely, predictive analytics helps us to answer questions like the following:

  • Which customers will respond to a marketing campaign?
  • Which customers will book their flight on an airline of the Lufthansa Group or Star Alliance rather than the competition?
  • Which customers will book a hotel room, rent a car or buy a product from one of our various non-aviation Miles & More partner companies?
  • Which products or services fit with the customers’ preferences and needs?

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

A: With more than 29 million participants, Miles & More is the largest frequent flyer and awards program in Europe. Miles & More has more than 40 airline partners, including the Lufthansa Group and 28 airlines of the Star Alliance. In addition there are already more than 270 non-aviation companies affiliated with Miles & More, these include partners from the hotel, car rental and cruise industries, banks and insurance, telecommunications and electronics industries as well as shopping and lifestyle.

Put in a nutshell, our large partner network offers a wide variety of different products and services and our goal is to provide each customer with the most relevant offers, services and information based on his or her individual preferences, interests and current needs.

Predictive models enable us to choose the best offers or the most relevant information to dynamically tailor content and use the right channels to interact with our customers based on the data available. More personalized marketing communicaton instead of a one-size-fits-all approach generates additional revenue and results in higher customer satisfation. At the same time we can automate the decision process as well as our marketing campaign workflows to increase operational efficiency and save costs.

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

A: The answer to this question is not as straightforward as it seems, since the predictive lift always depends on the baseline you use to measure your results and we often try to improve models and processes which are already pretty optimized and mature. When it comes to improving marketing campaign it is also very important to set the right expectations and keep in mind that predictive analytics is a critical success factor, but still only one piece of the puzzle and no panacea for all challenges.

From my point of view, the recipe for success is doing a lot of experimentation and accept that you will fail more often than you succeed. During this process most improvements will result from the aggregation of marginal gains and sometimes you will hit a long shot.

And to finally give you one specific example; which we also presented in our case study on uplift modelling at Predictive Analytics World Berlin in 2014. Using an uplift modelling approach we were able to increase the lift of direct marketing campaigns for our Lufthansa Miles & More Cedit Card by a factor of 3.

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

A: Our analytics team was often asked about the typical customer of a given partner, product or service. The expectation was almost always to get a demographic segmentation or description of the perfect target group. When we started to educate colleagues from various departments on how a multivariate predictive model works they were often surprised to learn, that very different customers are actually equally valuable and that a predictive model always beats an old-school demographic or heuristic segmentation approach. 

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

A: Acting as awards program and expert in customer loyalty for more than 300 partners across all industries and providing a marketing platform to interact with 29 million customers certainly has its own and unique challenges.

We want to show some use cases about how we successfully apply predictive analytics at Miles & More and share our practical experience about what works best in such a setting. We hope to give our audience some new ideas they can take-away and apply to their specific challenges within their own business setting. We are looking forward to a fruitful discussion, in our Q&A session as well as during the whole conference.

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Don't miss Thomas’ conference presentation, Using Predictive Models for Demand Simulation – Purchase, Response and Uplift Modeling in Practice, at Predictive Analytics World New York on Tuesday, October 25, 2016 from 11:20 am to 12:05 pm.  Click here to register for attendance.  

By: Eric Siegel, Founder, Predictive Analytics World

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August 15th 2016

Wise Practitioner – Predictive Analytics Interview Series: Meina Zhou at Bitly

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Predictive Analytics for Different Business Types:  Optimize All the Funnels, at Predictive Analytics World New York, Meina Zhou IMAGEOctober 23-27, 2016, we asked Meina Zhou, Data Scientist at Bitly, a few questions about her work in predictive analytics.

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

A: My marketing qualified lead scoring model helps to identify high-quality leads. My churn prediction model helps to identify the current customers that have the highest probability to churn. My upsell prediction model helps to identify the best customers to upsell.

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

A: My predictive analytics helps the customer success team to better understand our customers’ behaviors and to identify key factors churning. It also helps the customer success team to design proactive retention actions accordingly.

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

A: The marketing team compared my model with third-party software and determined that my model was on par. Building the software in-house would allow us to save the money we would have spent on a third-party tool.

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

A: Some of the customers churned because they never understood how our product worked. They decided to pay for the product before they knew how to use it.

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

A: Utilize the usage data of your product to understand the customers’ engagement with your product. The change in the customers’ usage behavior can be key factors for churning and upselling.

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Don't miss Meina’s conference presentation, Predictive Analytics for Different Business Types:  Optimize All the Funnels, at Predictive Analytics World New York on Wednesday, October 26, 2016 from 11:15 to 11:35 am.  Click here to register for attendance

By: Eric Siegel, Founder, Predictive Analytics World

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August 12th 2016

Wise Practitioner – Predictive Analytics Interview Series: Dr. Shantanu Agrawal at Centers for Medicare & Medicaid Services

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference keynote presentation, Implementing Predictive Analytics at CMS: Lessons Learned and Future Directions at Predictive Analytics World for Government, October 17-20, 2016, we asked Dr. Shantanu Agrawal, Deputy Shantanu Agrawal Pic 08-2016Administrator for Program Integrity and Director of the Center for Program Integrity at the Centers for Medicare & Medicaid Services (CMS), a few questions about his work in predictive analytics.

Q: How would you characterize your agency’s current and/or planned use of predictive analytics? What is one specific way in which predictive analytics drives decisions in your agency?

A: Predictive Analytics is the backbone of the Centers for Medicare & Medicaid Services (CMS) Fraud Prevention System (FPS), which applies proven and effective predictive modeling tools into the Medicare claims processing system. The FPS was a result of a CMS mandate by the Small Business Jobs Act of 2010 (SBJA), Section 4241, to implement a predictive analytics system to analyze Medicare claims to detect patterns that present a high risk of fraudulent activity. Signed by President Obama in fall 2010, the SBJA enabled CMS to stop payment on high-risk claims, and perform post-pay analysis to identify emerging trends of potentially fraudulent activity.  The FPS analyzes information from multiple Medicare data sources (Part A, Part B, and DME claims, Composed Numbers Checklist, FID, and 1-800-Medicare Tips) to predict whether observed billing patterns or trends are likely to be fraudulent, similar to the way the credit card industry evaluates the consistency of a cardholder’s new charges against past transactions as a way of identifying potential fraud. 1 CMS designed the FPS to accommodate 4 model types including rule-based, anomaly, and social network analysis models, in addition to predictive models. These model types address multiple kinds of fraud schemes. In addition, they work together to increase the effectiveness of one or more models to identify potential and actual fraudsters.

Since June 30, 2011, the FPS has run predictive algorithms and other analytics nationwide on a daily basis against 4.5 million Part A and Part B claims to generate alerts for Program Integrity (PI) purposes.  The FPS alerts when egregious, suspect, or aberrant activity is identified, and automatically generates and prioritizes leads for review and investigation by the Agency.

1 Department of Health and Human Services Centers for Medicare & Medicaid Services, MLN Matters, Number SE1133, 2011.

 

Q:  Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: The FPS “big data” effort has had a profound impact by allowing CMS to quickly identify issues and take action. CMS has saved over $1.5 billion in inappropriate payments since implementing this system, producing a 11:1 return-on-investment in the latest year of operations.

There are a number of things an organization has to do well to really drive value from data intensive approaches. First, often disparate data has to be compiled into a single usable structure and technology solution that promoted ease of use and allows business-focused analytics. This can only be done by engaging end users early and promoting the use of data. Second, analytics have to be incorporated into existing business processes. In our case, program staff, policy analysts, and investigators had to be convinced that “big data” would make their work more efficient and effective so that it became part of their usual work-flow, instead of an add-on. Third, the business has to constantly feedback on the analytics to make sure that useful, high yield models are constantly being designed and rolled-out – and where necessary, models and analytical approaches are being improved to provide more actionable results to the business. Finally, you have to establish performance metrics and measure results, to know how “big data” is impacting work and hopefully making it better. Data without a measurable ROI is not necessarily an improvement.

Done right, big data leads to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk. For example, the FPS identified a home health agency in Florida that billed for services that were never rendered. As a result, CMS placed the home health agency on prepayment review and payment suspension, referred the agency to law enforcement, and ultimately revoked the agency’s Medicare enrollment. In Texas, the FPS identified an ambulance company submitting claims for non-covered services and services that were not rendered. Medicare revoked the ambulance company’s enrollment. Likewise, FPS identified that an Arizona, medical clinic had questionable billing practices, such as billing excessive units of services per beneficiary per visit. Upon review of medical records, it was discovered that physicians had been delivering repeated and unnecessary neuropathy treatments to beneficiaries. That medical clinic was revoked from Medicare enrollment. 

Q: Can you discuss any near term goals you have for improving your agency’s use of predictive analytics?

A: Now in its fifth year, CMS has begun work to build a new and more robust FPS system (FPS2) that is scheduled to go into production the first quarter of CY 2017. FPS2 will be enhanced to improve predictive analytics by (1) decreasing the time it takes to market and recode models to custom language, (2) expanding the toolset to include Social Network Analysis visualization, (3) improving the user interface based on previous and past user studies, (4) implementing the ability to suspend payments within the tool, and (5) providing an easy to use computer interface, enabling users to access and view claims level data in maps and charts.  Ultimately, FPS2 will continue to increase the CMS CPI return on investment noted in the original FPS.

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Don't miss Dr. Agrawal’s conference keynote presentation, Implementing Predictive Analytics at CMS: Lessons Learned and Future Directions on Monday, October 17, 2016 from 1:00 to 1:45 pm at Predictive Analytics World for Government. Click here to register to attend. 

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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August 10th 2016

Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Best Practices Enhancing Contextual Experience with Predictive Analytics, at Predictive Analytics World New York, October 23-27, 2016, we asked Madhusudan Raman, Innovation Incubator at Verizon, a Madhusudan_Raman IMAGEfew questions about his work in predictive analytics.

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

A: As in the past, Contextual Intent, Contextual Mood, Internet Advertising Bureau (IAB) Context, Contextual Interestingness, and Propensity to Buy/Act continue to be strong prediction targets.

Q: What emerging needs are you seeing related to the behaviors your models predict?

A: Recently the prediction of Thing behaviors to identify Contextual Interestingness has gained in importance due to the recent push to extend the embedded device boundaries to include cloud activity; in other areas, there seems to be increased interest in Propensity for Attrition where a body of best practices are emerging.

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

A: Knowing which market incubations to pursue and which ones to put on hold is a valuable insight. Predictive Analytics – specifically scoring ‘Propensity to Succeed’ has been a helpful operational support measure for prioritization of resource constrained pursuit of ideations.

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

A: Applying Context (Information) Gain to constrain datasets increases the resulting lift from the model deployment from 2X-9X when applied to a range of Consumer Experience enhancement scenarios.

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

A: One interesting discovery was that archetype (‘think extreme’) analysis seems to always be more effective than standard statistical (‘think average’) measures and analysis for predictive segmentation incorporating near-real-time sensor streams from Things.

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

A: Contextual understanding enhances Consumer Experience. We will share a systematic Predictive Analytic technique for dealing with context, its quantification within a dataset, and how channels and touchpoints can use this derived “context” in near-real-time across Industry verticals especially with Thing Sensor data.

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Don't miss Madhusudan’s conference presentation, Best Practices Enhancing Contextual Experience with Predictive Analytics, at Predictive Analytics World New York on Wednesday, October 26, 2016 from 4:20 to 5:05 pm.  Click here to register for attendance

By: Eric Siegel, Founder, Predictive Analytics World

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August 8th 2016

Wise Practitioner – Predictive Analytics Interview Series: Sanjay Gupta at PNC Bank

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference co-presentation, Predictive Analytics for Stress Testing – Industry Challenges at Predictive Analytics World Financial in New York City, October 23-27, 2016, we asked Sanjay Gupta, Executive Vice President and Head of Sanjay Gupta IMAGEModel Development at PNC Bank, a few questions about his work in predictive analytics.

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

A: We predict defaults of our clients and losses in our portfolio. This is used for the calculation of the Capital, Reserves and Profits of the Bank.

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

A: The predictive analytics helps us acquire customers, evaluate losses and defaults. By looking at the drivers of default or losses, we can design account management strategies that can mitigate loss or default. It can also provide early warnings that can help to better inform our stakeholders.

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

A: Data does not lie; sometimes our ability to see the truth is hampered by our biases.

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

A: Looking at fundamental credit analysis of a client and knowing your client can truly add value to your predictive work.

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Don't miss Sanjay’s conference co-presentation, Predictive Analytics for Stress Testing – Industry Challenges at Predictive Analytics World Financial NYC, on Tuesday, October 25, 2016 at 11:20 am to 12:05 pm. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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