July 29th 2015

Wise Practitioner – Predictive Analytics Interview Series: Scott Jelinsky of Pfizer, Inc.

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

In anticipation of his upcoming conference presentation at Predictive Analytics World for Healthcare Boston, Sept 27-Oct 1, 2015, we asked Scott Jelinksy, Principal Research Scientist at Pfizer, Inc., a few questions Scott_Jelinskyabout incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Crowdsourcing Predictive Analytics to Enhance Clinical Trial Design, and see what’s in store for the second annual PAW Healthcare conference in Boston.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A: We use predictive analytics to support a variety of different areas of health care, but our primary interest is to help support precision medicine to be sure that the right patients get the right drugs.

Q: What outcomes do your models predict?

A: We use a combination of health care records and baseline clinical data to be able to predict which patients would most likely have a positive response to therapy and to identify those patients that are at a higher risk for potential adverse side effects.

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: By improving the understanding of the risk/benefit ratio we strive to decrease the risk of the product and improve patient safety profile.  Our work has positively impacted the clinical trial design by being able to enrich for patients that are more likely to benefit from treatment.


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

A: Using predictive analytics, thus far we have seen approximately 20% improvement of predictability (e.g. patients more likely to have worsening of symptoms) over baseline model used prior. 

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

A:  There are many surprises using advanced predictive analytics.  The most encouraging was that the predictive ability achieved was much greater than what we initially thought possible.  More specifically, the number of individual features that contribute makes for a much more advanced algorithm.  

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

A: The greatest advances using predictive analytics is the ability to collect and decipher large scale genetic data as it applies to individual clinical presentations and outcomes.

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

A:  I will describe how distributed innovation and crowdsourcing can be an effective tool in developing predictive analytics to improve and fine tune algorithms. 

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Don't miss Scott’s presentation, Crowdsourcing Predictive Analytics to Enhance Clinical Trial Design, at PAW Healthcare on Monday, September 28, 2015 from 4:45 to 5:30 pm. Click here to register for attendance.

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

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July 27th 2015

Wise Practitioner – Predictive Analytics Interview Series: Chris Franciskovich at OSF Healthcare System

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

In anticipation of his upcoming conference co-presentation at Predictive Analytics World for Healthcare Chris_FranciskovichBoston, Sept 27-Oct 1, 2015, we asked Chris Franciskovich, Data Scientist at OSF Healthcare System, a few questions about incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Preventing Readmissions and Reducing Costs with Predictive Analytics, and see what’s in store for the second annual PAW Healthcare conference in Boston.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A: My work crosses multiple areas of OSF’s integrated healthcare system.  I’ve worked on projects focused on patient outcomes, ones that are more akin to traditional insurance projects, staffing efficiency projects and multiple quality improvement projects.  Our predictive analytics projects are aligned to our strategic priorities, but are not constrained to specific operational functions.

Q: What outcomes do your models predict?

A:  The upcoming talk in Boston will focus on the development and implementation efforts surrounding the 30 Day Readmission Risk model.  It predicts the patient level risk of all-cause 30 day readmission and is designed to provide our clinicians and support staff the ability to proactively identify and mitigate patient risk. 

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:  We’re using predictive analytics to address a range of needs, from identifying our highest risk patients to assisting with more efficient staff scheduling.  The 30 Day Readmission Model is currently being used to direct workflow activity in a variety of operational areas such as; inpatient case management, ambulatory care management, post discharge follow-up phone calls, outpatient palliative care and home care reporting/monitoring.  Through the use of this model, we’re able to efficiently focus resources to those patients who are in the most need.

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

A: Prior to the deployment of the 30 Day Readmission Risk model, I completed a comparative analysis between the then current approach, and the model.  The then current approach achieved a cross validated AUC of 0.63 while the 30 Day Readmission Risk Model achieved a cross validated AUC of 0.76.  The 30 Day Readmission Risk Model is also based upon data collected as part of the normal operational and clinical workflows of our organization.  Thus, in addition to a significant increase in model performance, the new approach also provides the ability to approach our work in a much more efficient manner.

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

A: Each project finds its gold, but I think the main discovery I’ve seen, from an organization level perspective, is the growing realization that we own the proverbial mine.  Multiple projects have produced previously unknown insights from data we’ve been actively using for years or have proven that we can internally produce higher quality models than we can purchase.  The success of the projects to date have solidified the organization’s realization that advanced analytics is major multiplier for the return on data technology and infrastructure related investments.

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

A: With the changes in payment approaches and proliferation of electronic medical records, it is an incredible time to work as a Data Scientist in healthcare.  Historically predictive models have lived more in the insurance related areas of the industry, or have been simple and easy to calculate clinical tools.  Both areas provided value in the past, but the availability of large amounts of structured and unstructured data, coupled with ample computing power and the financial motivations to compete on analytics makes for an amazing environment.  I believe all areas of the industry are able to benefit from the appropriate use of predictive analytics.

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

A: The success, or failure, of a project is not solely tied to the performance of the model.  You have to be able to translate the model into a meaningful story with which the business can relate.  One of my favorite quotes is “Every act of communication is an act of translation.”  As a Data Scientist, you must remember to function as a translator for both your business and its data.

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Don't miss Chris’ co-presentation, Preventing Readmissions and Reducing Costs with Predictive Analytics, at PAW Healthcare on Monday, September 28, 2015 from 2:40 to 3:00pm. Click here to register for attendance,

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

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July 23rd 2015

Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien at Paychex

In anticipation of his upcoming conference presentation, Predicting Employee Churn with Anonymity, at Philip_O-Brien

Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked Philip O’Brien, MIS and Portfolio Manager at Paychex, a few questions about his work in predictive analytics.

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

A: Our models are targeted to four main portfolios: Our Operations Portfolio focuses primarily on retention and sensitivity to price, while our Sales Portfolio concentrates on product upsell potential. The Risk Portfolio is focused on customer default and resolution. Lastly, our Strategy Portfolio is a variety of models designed to influence the strategic decision making at a corporate level, and contains the employee model we will be discussing in Boston.

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

A: The value of modeling can be summarized by a quote from Shao Ming Lo, “Strategy in fact is the art of focusing.”  The separation our models create allow targeted decisions to be applied to cross sections of our clients.  Through this application of predictive analytics you can make the greatest impact with a strategy at a reduced cost.

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

A: The value derived from our models can be demonstrated in a trio of retention models in our Operations Portfolio. Those models focus on controllable losses, uncontrollable losses, and price sensitivity, which we have presented on at previous PAW conferences.  What is most impactful about these models is the way they are used across the organization, from discounting and retention to exception processing – these models help shape the way we view a significant portion of interactions with our clients. 

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

A: I continue to be amazed at how predictive analytics positively impacts every aspect of business. Surprisingly, even with consistently inconsistent data, there is value in putting a model to it. Once you have identified the data that signals the behavior you are going after, simply start applying the science to uncover valuable insight that can change the way you do business. 

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

A: In dealing with customers, no matter the business, we understand the inherent value the relationship with the customer has on their longevity with your business. However, in looking at employee data, we found evidence that the inverse is also true: the relationship employees have with your customers impacts their employment longevity as well. 

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Don’t miss Philip’s conference presentation, Predicting Employee Churn with Anonymity on Tuesday, September 29, 2015 at 11:15am to 12:00pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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July 21st 2015

Wise Practitioner – Predictive Analytics for Healthcare Interview Series: Daniel Chertok at NorthShore University HealthSystem

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

In anticipation of his upcoming conference presentation at Predictive Analytics World for Healthcare Boston, Daniel_Chertok

Sept 27-Oct 1, 2015, we asked Daniel Chertok, PhD, Sr. Data Scientist at NorthShore University HealthSystem, a few questions about incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Predictive Analytics Applications to Populations Health Management and Staffing Optimization, and see what’s in store for the second annual PAW Healthcare conference in Boston.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A: The objectives of my work include improving patient outcomes, increasing operational efficiency and managing costs. Examples of these efforts include assessing patient mortality, admission and readmission risks, optimizing nurse staffing in the Emergency Department (ED), and making cost containment recommendations based on comparing procedure costs by provider.

Q: What outcomes do your models predict?

A: We have successfully predicted relative probabilities of mortality, admission and readmission risks for patients with congestive heart failure (CHF) and chronic obstructive pulmonary disorder (COPD), admission risks for patients with diabetes mellitus (DM) and for the general patient population. By “relative probability” I mean patient ranking with respect to the corresponding risk: while the “absolute” probability may not be “correct,” patients appearing at the top of list are much more likely to have a negative outcome than those below them.

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: The Clinical Analytics team at NorthShore University HealthSystem is fortunate to be part of concerted effort by the top management to use the wealth of available clinical data for the mutual benefit of both the patients and the organization. Our mission is to streamline the efforts of clinicians, case managers and operational leaders by providing them with predictive and reporting tools for efficient decision-making. One recent example is MyPanel, a Tableau-based tool hosted on a server accessible from individual physicians’ offices, which aggregates patient data for the providers on a menu of visually rich dashboards; its current utilization rate exceeds 96%. Another example is the ED utilization dashboard showing the most recently available ED demand data superimposed over historical aggregates; it allows ED staff managers to make tactical decisions about resource deployment.

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

A: A case management program developed by the Clinical Analytics team based on the Elixhauser comorbidity method yielded a lift in excess of 40x. To put it in perspective, a case manager reaching out to those NorthShore patients who are most at risk for hospitalization due to CHF, COPD, coronary arterial disease (CAD) and DM is able to reduce the number of patients in her cohort from 1000 to 25 while achieving the same result. While it is very hard estimate actual savings achieved as a result of using our models in the context of managed care, a very rough estimate would place that number potentially in the hundreds of thousands of dollars.

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

A: In the course of analyzing ED demand during different times of the year, it turned out that the weekend following Labor Day had a higher utilization rate than the actual holiday weekend. I did not realize that a hangover could last that long…

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

A: Speaking in monetary terms, healthcare organizations can undoubtedly benefit from applying machine learning techniques to billing. One of our team members is currently working on identifying billing errors using a rule-based algorithm and has already achieved a remarkably high true positive rate. From the care point of view, we started to think about collecting bedside sensor data in order to alert staff to potentially critical changes in the patient’s condition. We could even potentially pair up with aggregators of wearable device data with the view of monitoring our patient’s health in real time, but for now it is still in the distant future.

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

A: I will be covering our accomplishments in developing ED utilization and staffing model. My hope is to show the attendees that staffing optimizing in the ED department can be achieved through accurate data collection and relatively straightforward modeling tools. Improving ED patient outcomes and experience is a realistic high impact goal that can be accomplished internally with relatively modest resources. The main ingredient of success here is unwavering commitment on the part of the organization.

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Don’t miss Daniel’s conference presentation, Predictive Analytics Applications to Populations Health Management and Staffing Optimization, on Tuesday, September 29, 2015 at 4:15 to 5:00 pm at Predictive Analytics World for Healthcare. Click here to register to attend.

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

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July 16th 2015

Wise Practitioner – Predictive Analytics Interview Series: Herman Jopia of American Savings Bank

By: Eric Siegel, Founder, Predictive Analytics World   

In anticipation of his upcoming conference presentation, Driving Superior Growth Through Self-Developed Code, Scoring Modeling, and Price Optimization, at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, Herman_Jopia

we asked Herman Jopia, First Vice President and Data Analytics Manager at American Savings Bank, 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 developed and implemented attrition, profitability, and response models.

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

A: Predictive analytics helps us to understand our customers and prospects.  In practice that means a better answer to questions like who to target,  what to offer, why it makes sense, and when and how to do it.  For example, our response model for direct mail helps us to manage volume and reduce costs by excluding prospects that have a low propensity of taking the offer; therefore, it drives a lift on our profitability metrics.

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

A: Besides monitoring the models and metrics, we actually look at how these models impact both growth and profitability.  For example in 2014 our targeted direct mail offers dramatically increased the volume and value of our unsecured loan portfolio, a remarkable achievement in a limited and matured market.

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

A: One interesting finding was the sensitivity to interest rates in a loan offer.  Though it follows the general theory of supply and demand for credit, the price elasticity by segment turned out to be surprisingly different.  This makes us think very hard when we want to give the right offer to the right customer, a continuous and crucial challenge in Marketing.

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

A: The presentation will illustrate that “business as usual” would have never gotten us to where we are today, and the effort of moving from descriptive to predictive analytics was the right thing to do, especially in terms of results.

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Don't miss Herman’s conference presentation, Driving Superior Growth Through Self-Developed Code, Scoring Modeling, and Price Optimization on Monday, September 28, 2015 at 10:30am to 11:15am at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

 

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July 13th 2015

Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan of Cicero Group

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Pricing and Segmentation Utilizing Menu-based Conjoint at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked Lawrence Cowan, Partner at Lawrence_CowanCicero Group, a few questions about his work in predictive analytics.

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

A:  The majority of my analytics experience has dealt with predictive consumer behavior across all stages of the customer lifecycle.  So this would span from acquisition through the development of “typing” tools for targeting and segmentation, to response and uplift modeling for existing customers, to attrition modeling and customized intervention strategies.

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

A:  Predictive analytics defines our organization.  With it, we would not have an offering.  As a full service data-driven strategy consulting firm, it is our job to provide the technical and analytical expertise to help our clients leverage data to make smarter decisions.  And in all engagements involving predictive analytics, our ultimate objectives are results and implementation – if our clients cannot actively use the models and insights to make decisions, we have failed.

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

A:  Engagements involving attrition are some of my favorite.  With the right model, it’s amazing how you can identify the characteristics, behaviors, or events that preclude attrition.  During a recent engagement we were able to leverage a sophisticated attrition model (proportional hazards model) to identify critical events, and then designed customized interventions based on the defining characteristics.  After a 6 month period of implementing the interventions, attrition had been reduced by 300 bps, which effectively improved valuation of the company by $300MM.

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

A:  I’m always surprised at the opportunities unearthed with predictive analytics – things that you would never expect if it were not for efforts in data mining.  For example, for a large grocery retailer, we were able to identify two critical customer behavior trends (made possible through loyalty data) that were significant predictors of customer profitability.  These two trends were counter to heuristic judgment at the executive level (executives have since changed their perception of the event after seeing the compelling evidence).

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

A:  One pricing and product bundling strategy doesn’t work for all audiences.  As you think about pricing and bundling changes, be sure to consider the implications of various consumer segments – their preferences and sensitivity to pricing are unique!

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Don't miss Lawrence’s conference presentation, Pricing and Segmentation Utilizing Menu-based Conjoint on Monday, September 28, 2015 from 11:20am to 12:05pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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July 7th 2015

Wise Practitioner – Predictive Analytics Interview Series: John Smits of EMC

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting B2B Sales Success at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked John Smits, Chief Data Officer, Global Business John_SmitsOperations at EMC, 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 realized great success in our Sales efforts through the prediction of a variety of different behaviors. We predict territory “opportunity”, leveraging insights on customer/prospect clustering techniques – this enables us to design and optimize our market coverage. We also analyze customer propensity in certain technology solutions, here we are able to associate customers into peer groups where common purchase patterns and business needs help us position the next best solution at the optimal time.

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

A:  We are actively applying analytics on the inspection of our sales forecast. Leveraging both internal and external factors, we are able to isolate factors relative to our customers, the market/economy, and the sales channel performance to provide us with leading indicators of risk. With more than 50 actionable risk factors, we are able to drive focused inspection and support to manage our pipeline quality.

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

A:  We witness successful results on the performance of targeted sales campaigns, our outbound predictive models are generally more than 5X as effective in opportunity conversions vs. traditional targeting methods. We also analyze the productivity of our sales resources based on their adoption and use of predictive tools; we have seen a strong correlation with the use/leverage of account intelligence resources with rep quota attainment.

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

A:  That it’s not perfect! Far from it — but leveraging multiple data sources and generating a common master identifier we are able to find insight that while not always complete is still actionable!

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

A: EMC will share our real-life journey bringing predictive analytics to a global sales operation. The discussion will share hard lessons learned with data, tools, talent and business process. 

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Don't miss John Smits’ conference presentation, Predicting B2B Sales Success on Monday, September 28, 2015 at 11:20am to 12:05pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

Eric Siegel is the founder of Predictive Analytics World (www.pawcon.com) — the leading cross-vendor conference series consisting of 10 annual events in Boston, Chicago, San Francisco, Toronto, Washington D.C., London, and Berlin — and the author of the bestselling, award-winning book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

 

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June 30th 2015

Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming keynote conference presentation, Buy or Wait? How the Bunny Predicts When to Buy Your Plane Ticket, at Predictive Analytics World Boston, Sept 28-Oct 1, 2015, we asked Dr. Patrick Patrick_SurrySurry, Chief Data Scientist at Hopper, a few questions about his work in predictive analytics.

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

A: At Hopper we predict how airfares are likely to move so that we can advise consumers whether they should buy now or wait for a better price.

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

A: Our goal at Hopper is to help consumers save money on flights with data-driven advice about when to fly and buy, and even where to go.   Airfare predictions are a core part of our value proposition: On average the “buy or watch” advice in our app saves consumers 10% over the first price they see for a trip.

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

A:  In extensive back-testing across tens of thousands of trips, we’ve shown that 95% of the time our “buy or wait” recommendations will either save the consumer money or do no worse than the first price they saw, saving 10% on average.

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

A:  We’ve been surprised at how much airfare fluctuates.  Although on average airfare rises as departure approaches, watching continuously is very effective at capturing lower prices.  More than half the time we’re watching a trip, we’ll see a price that’s 5-10% lower than our initial price within just 24 hours.

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

A:  Airfare trends are surprisingly predictable, enabling consumers to save up to 40% on their flights using the Hopper app, while avoiding the frustration of manual comparison shopping.  Top tips for saving: Start watching prices early, be flexible with dates and destinations if possible, and do your homework for the route(s) you’re interested in so you know what prices other people are paying.  One example: Fares rise higher for routes with lots of business travelers, so you won’t find a last minute deal from New York to San Francisco, but you might find one from New York to Hawaii.

Patrick Surry-PAWCH15

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Don't miss Patrick Surry’s keynote conference presentation, Buy or Wait? How the Bunny Predicts When to Buy Your Plane Ticket on Monday, September 28, 2015 at 1:30-2:15 pm and Patrick’s conference presentation, Applying Next Generation Uplift Modeling to Optimize Customer Retention Programs, on Monday, September 28, 2015 at 2:40-3:25 pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

Eric Siegel is the founder of Predictive Analytics World (www.pawcon.com) — the leading cross-vendor conference series consisting of 10 annual events in Boston, Chicago, San Francisco, Toronto, Washington D.C., London, and Berlin — and the author of the bestselling, award-winning book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

 

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May 19th 2015

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Thompson of Robert Bosch, LLC

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2015 

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference Jeffrey_Thompsonpresentation, Manufacturing Analytics at Scale: Data Mining and Machine Learning inside Bosch, we interviewed Jeffrey Thompson, Senior Data Scientist at Robert Bosch, LLC. View the Q-and-A below to see how Jeffrey Thompson has incorporated predictive analytics into manufacturing at Robert Bosch, LLC. Also, glimpse what’s in store at the PAW Manufacturing conference.

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

A: At Bosch we work on a wide variety of different use-cases.  The target applications of our predictive models include manufacturing, supply chain and logistics, engineering, and Internet of Things and Services.    

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

A: Predictive models are used across products, processes, and operations at Bosch. An example use of our predictive models is in providing important insight into the root causes of failures on manufacturing lines.  These insights often lead directly to the resolution of a problem and are an important part of our continuous improvement efforts.

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

A: In one case, we used a predictive model to narrow down the root cause of a particularly expensive, internal defect in one of our automotive manufacturing lines.  The relative improvement in this case was 85%.   

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

A: Data often tells you many things that you are not looking for and hence offers many surprises in all our projects. For example, many data sources or measurements never change over the course of a product’s or a process’ lifetime. It might be expensive to measure them, yet they continue to be measured based on an initial and incomplete design specification.

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

A: In many of our applications, high accuracy, and especially, low false alarm rates are critical. In such scenarios, even with large amounts of data, we find that better features often beat better algorithms, and subject matter experts are the key to getting those features. 

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Don't miss Jeffrey Thompson’s conference presentation, Manufacturing Analytics at Scale: Data Mining and Machine Learning inside Bosch, at PAW Manufacturing, on Tuesday, June 9, 2015, from 2:40-3:25 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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May 14th 2015

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Kumar Satyam of PricewaterhouseCoopers, LLP

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2015

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference co-presentation, Utilizing On Board Technologies To Improve Maintenance Practices in Airlines, we interviewed Kumar Satyam, Manager, Advisory at PricewaterhouseCoopers, LLP. View the Q-and-A below to see how Kumar Satyam has incorporated predictive analytics into manufacturing at PricewaterhouseCoopers, LLP. Also, glimpse what’s in store for the PAW Manufacturing conference.

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

A: PwC Analytics group works on a wide range of advanced analytics projects. Some of the examples of types of predictive models developed are customer choice, market response, demand forecasting, lifetime value analysis, sentiment analysis, failure modeling, supply chain optimization and propensity modeling

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

A: Through predictive analytics, PwC enables its clients to rapidly discover, quantify and deliver value from data with intelligent analytics and scalable end-to-end business solutions.  By enabling our clients to quantify the potential benefits from analytics engagements, we help them prove the value of predictive analytics to their respective organizations.

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

A: Recently PwC Analytics team and Travel and Transportation team worked together to develop a delay/cancellation prediction model for predictive maintenance for a large US carrier. Through a couple of months of  pilot test run, we were able to demonstrate that the developed model can help the carrier save up to 25-30% of the currently occurring maintenance related delays/cancellations for the modeled fleets and components. This effectively translates to millions of dollars’ worth of savings per year if expanded across fleets.

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

A: One of the surprising discoveries from our analysis was that the maintenance logs of airlines contain lot of relevant information about a potential delay/cancellation that can occur in the future once the unstructured test data is analyzed.

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

A: Key Takeaway – Current computing power and analytics capabilities enable us to combine sensor data from aircrafts with unstructured maintenance log data to predict a significant percentage of delay/cancellations before they occur. This can enable airlines to perform predictive maintenance resulting in improvement of on-time performance and customer satisfaction with reduction in delay associated costs.

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Don't miss Kumar Satyam’s conference co-presentation, Utilizing On Board Technologies To Improve Maintenance Practices in Airlines, at PAW Manufacturing, on Wednesday, June 10, 2015, from 3:30-4:15 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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