May 12th 2015

Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher of National Consumer Panel

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, The Importance of an Early Win in Promoting a Greater Predictive Analytics Capability, at Predictive Analytics World Chicago, June 8-11, 2015, we asked Thomas Schleicher, Sr. Director, Measurement Science at National Consumer Panel (by Nielsen), a few questions about his work in predictive analytics.

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

A: The simple answer is attrition. Our prospective panelists who scored well according to our predictive model were shown to have a statistically significant, lower 12-month attrition rate than those who the model scored as poor prospective panelists. Panelists, for us, are analogous to subscribing customers in other industries, such as telecommunications.

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

A: Because we are obligated to maintain a 100,000 member consumer panel as a client KPI, reducing attrition is critical. Deploying our predictive model lowers attrition, reducing our need to recruit new panelists, which of course also has the effect of reducing recruitment costs.

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

A: The predictive model lowered our rate of attrition by an average of 3.5% over two independent evaluations of its impact. We’re still working on establishing the best gauge of ROI, given that there are direct, short-term benefits achieved through reduced recruitment costs, as well associated savings through a lower need to purchase scanners for panelists. Senior management is interested in ongoing improvement of the model as well as clearer measures of its value.

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

A: Some of the variables that were part of the predictive mix were not immediately intuitive. This has helped to reinforce the Measurement Science team’s quest to develop an integrated database that will enable expanded predictive capability.

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

A: I plan to share how the success of this model has helped to promote not just the benefit, but the need for a predictive analytics capability. I believe our success is applicable to other organizations also interested in developing and/or expanding a data driven culture that adds true value. Knowing where to start is critical.

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Don't miss Thomas Schleicher’s conference presentation, The Importance of an Early Win in Promoting a Greater Predictive Analytics Capability, at Predictive Analytics World Chicago on Wednesday, June 10, 2015 from 4:15-5:00 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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May 7th 2015

Wise Practitioner – Predictive Analytics Interview Series: Delena D. Spann of US Government

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, 21st Century Fraud Analytics (Detection) in a Predictive Analytics World, at Predictive Analytics World Chicago, June 8-11, Delena_Spann2015, we asked Delena D. Spann, Fraud Analytics Expert at a United States Government Agency, a few questions about her work in predictive analytics.

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

A: The behaviors that my models predict are within the elements of fraud.

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

A: I can't speak as to how it delivers value within my organization due to it being comprised of a wide range of specific divisions. However, I will say that within the realms of academia in which I am an Adjunct Professor as well; its value has provided me with an in-depth understanding of my findings in a credit card fraud case study. It drives decisions based on the mere fact that it allows one to gain a better understanding of the variables that are derived directly or indirectly to the findings of concern. 

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

A: Within the ROI analytics initiative the predictive analytics methodology used ensured the most thorough approach to deterring and detecting fraud in the credit card industry. 

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

A: Although, using predictive analytics can be an exhaustive and reiterative process with built-in flexibilities; it can enhance the looks for stability and repeatability of its findings. 

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

A: The one take-away is that Predictive Analytics is the emerging tool of the 21st Century.

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Don't miss Delena D. Spann’s conference presentation, 21st Century Fraud Analytics (Detection) in a Predictive Analytics World, at Predictive Analytics World Chicago on Tuesday, June 9, 2015 from 3:05-3:25 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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May 5th 2015

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

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Buy or Wait? How the Bunny Predicts When to Buy Your Plane Ticket, at Patrick_SurryPredictive Analytics World Chicago, June 8-11, 2015, and keynote at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked Dr. Patrick Surry, 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.

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Don't miss Patrick Surry’s conference presentation, Buy or Wait? How the Bunny Predicts When to Buy Your Plane Ticket at 1) Predictive Analytics World Chicago on Wednesday, June 10, 2015 from 11:15 am-12:00. Click here to register to attend.  2) Keynote presentation at Predictive Analytics World Boston on Monday, September 28, 2015 at 1:30-2:15 pm. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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April 30th 2015

Wise Practitioner – Predictive Analytics Interview Series: Viswanath Srikanth of Cisco

By: Eric Siegel, Founder, Predictive Analytics World 

In anticipation of his upcoming conference co-presentation, Building a Digital Marketing Data Viswanath_SrikanthScience Discipline at Cisco: Experiments to Excellence, at Predictive Analytics World Chicago, June 8-11, 2015, we asked Viswanath Srikanth, Senior Program Manager, Analytics at Cisco, 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 predict for our digital visitors, topics of interest, lead potential, likelihood to consume a type of offer, and probable path through the site.

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 used for understanding propensity to buy, amount of potential purchase as well as identifying leads. Identified leads are actively pursued by our outbound marketing teams (email, contact centers) and by our Sales teams. The lead potential is a very useful filter in prioritizing which leads to pursue, significantly cutting down on unproductive calls.

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

A: I cannot disclose the actual lift percentage here, but suffice it to say attaching scores to leads allowed for a double digit improvement in number of deeper conversations we had with potential customers (that is, took the next step along the journey).

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

A: Perhaps not entirely surprising – but our digital visitors want better information, timely information and want to figure out solutions to their problems – and the more intuitively we can make our digital property in helping them get there, the more likely they are to engage in deeper conversations with us.

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

A: The focus of the talk will be on the journey that Cisco's Digital team undertook in moving from an environment of experimenting in data science to making it an entrenched part of the business – and we hope the audience will be able to apply some of the successful aspects (and avoid the unsuccessful aspects) in their own business – both from a business process and modeling aspect.

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Don't miss Viswanath Srikanth’s conference co-presentation, Building a Digital Marketing Data Science Discipline at Cisco: Experiments to Excellence, and workshops at Predictive Analytics World Chicago on Tuesday, June 9, 2015, from 3:05-3:25 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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April 28th 2015

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

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference keynote presentation, UPS Analytics – The Road Jack Levisto Optimization, at Predictive Analytics World for Business Chicago, 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.

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

A: Two surprises come to mind. The first has to do with the maintenance of our delivery fleet. Through the use of Telematics data with diagnostic and predictive models, we can predict vehicles and parts that are getting ready to fail. This has reduced our failure rate and maintenance cost at the same time.

The second surprising discovery is from our ORION optimizations. We have seen significant additional improvements beyond predictive analytics. ORION has been able to determine new ways to run a delivery route that we did not think of through previous methods.

ORION and Telematics are examples of how analytics can take an already efficient organization and make additional gains through predictive and prescriptive modeling.

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

A: Conventional wisdom says that as an organization moves from descriptive analytics to predictive to prescriptive, the skills need to grow. However, the benefits grow tremendously during this process as well. This matches UPS's experience exactly.

We will show our journey through these stages of analytics, as well as the lessons learned along the way. The presentation will culminate with examples of "non-intuitive" insights that can only be seen through the use of advanced analytics.

Don't miss Jack Levis' keynote presentation, UPS Analytics – The Road to Optimization, at Predictive Analytics World for Business Chicago on Tuesday, June 9, 2015, from 8:50-9:40 am. Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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April 21st 2015

Wise Practitioner – Predictive Analytics Interview Series: Arcangelo Di Balsamo of IBM

By: Eric Siegel, Founder, Predictive Analytics World 

In anticipation of his upcoming conference presentation, Applied Predictive Analytics to Workload Automation, at Predictive Analytics World Chicago, June 8-11, 2015, we asked Arcangelo Di Balsamo, IBM Workload Automation Chief Architect at IBM, a few questions about his work in predictive analytics.

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

A:  As the Workload Automation architect, I'm applying predictive analytics mainly to IT Automation-focused use cases. Those use cases are about:

                1) Predict job duration using history
                2) Resolve issues quickly by learning from the past
                3) Improved insight by detecting relationships across resources
                4) Optimize performances

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

A:  In my case, predictive analytics is delivering value to the product I develop (it's a workload automation platform which includes also a job scheduler). The predictive analytics is adding to the product a reliable Operational Level agreement monitoring capability (such as being sure that a critical workload completes within a given deadline). Moreover it provides the knowledge required to reduce failures by discovering hidden relationships between jobs and resources, so that if a resource is failing, it's possible to also predict the failure of the job bound to it.

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

A:  When you run a million jobs per day, a failure rate of 1% brings to 10.000 failures per day. Imagine how expensive it is to find the root cause of all of these and take the remediation action!  We have customers that with the enhanced predictive capabilities in the product have reduced the failure rate to 0.01%, which is a huge operational cost savings for them.

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

A: Frequently, we discover recurring patterns in job duration (i.e., a job taking longer at the end of each month) that are not obvious. We also discover correlations between job failures and resource unavailability (like job1, job2 and job3 fail when the RDBMS is not available).

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

A:  Predictive analytics applied to workload automation is a tremendous opportunity to better know what's happening within your data center in order to reduce costs and prevent failures and delays in your critical business applications.

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Don't miss Arcangelo Di Balsamo’s conference presentation, Applied Predictive Analytics to Workload Automation, and workshops at Predictive Analytics World Chicago on Tuesday, June 9, 2015 from 3:55-4:40 pm.  Click here to register for attendance.

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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April 14th 2015

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

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, The Revolution in Retail Customer Dean_AbbottIntelligence, at Predictive Analytics World Chicago, June 8-11, 2015, we asked Dean Abbott, Co-Founder and Chief Data Scientist of Smarter Remarketer, a few questions about his work in predictive analytics.

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

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 present 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 website 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 website 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 Chicago, 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.

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Don't miss Dean Abbott’s conference presentation, The Revolution in Retail Customer Intelligence, and workshops at Predictive Analytics World Chicago on Tuesday, June 9, 2015 from 10:30-11:15 am.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

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March 17th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: John Callery at AOL

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference John_Callerypresentation, How AOL is Using Predictive Analytics as a Strategic HR Solution, we interviewed John Callery, Director of People Analytics at AOL, Inc. View the Q-and-A below to see how John Callery has incorporated predictive analytics into the workforce of AOL, Inc. 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?

A: We're not focusing on a narrow area when we use predictive analytics. Our goal is to use data and predictive methods to improve all aspects of interactions with our workforce. 

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

A: AOL's People Analytics team is part of the Human Resources organization, but works closely with our business units and finance organizations. 

Q: What workforce outcomes do your models predict?  

A: We use predictive models for as many questions as we can, and use the insights to drive better outcomes in all areas.  

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

A: Predictive analytics allows leadership to anticipate changes, conduct better planning and be proactive about intervening when a different outcome is desired.  

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: As we grow our analytical capabilities, the most important result has been providing better visibility into what is happening within our workforce. By understanding and explaining what is and will be happening within our teams, our HR and business leaders have been able to focus more on implementing their vision for the organization rather than reacting to issues and changes after they happen.  

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

A: One of my most surprising and satisfying discoveries hasn't been in the data itself, but in how quickly an organization can make improvements by using the available data to drive discussions and decisions.    

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: Productivity and employee satisfaction will see the largest improvements from the use of predictive analytics. By better understanding and supporting employees, organizations will be able to develop more successful products and services and lead to significant gains for the workforce and the company as a whole.   

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

A: Every organization faces different challenges at different points in their lifecycles, and it's important to learn from what others are doing in this new and growing space. Events like Predictive Analytics World for Workforce bring us together to share ideas and build relationships that can lead to great things at our individual organizations and across the field of People Analytics.  

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: In the race for attracting, developing and retaining top talent, predictive analytics provides a significant leg up on the competition. Anyone not building these capabilities within their organization will soon be left behind in the marketplace.  

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

A: When I'm at industry events, I often get questions that suggest that predictive analytics is a magic bullet for solving employee challenges. It's important to remember that predictive analytics is an important complement to good management practices, but it does not replace them.   

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

A: Partnership with business leadership is a critical part of the success of our People Analytics team. We work closely with business leadership, HR and finance to ensure the best possible outcomes for our people and the company.  

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

A: You don't have to have a mature analytical function to start making improvements for your employees and your business. By simply looking at the data in new ways and asking smarter questions, it's possible to begin a transformation that will continue to grow alongside your team's analytical prowess.

Don't miss John Callery’s conference presentation, How AOL is Using Predictive Analytics as a Strategic HR Solution, at PAW Workforce, on Wednesday, April 1, 2015, from 10:00-10:45 am. 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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March 10th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Chad Harness at Fifth Third Bank

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Names and Numbers: Leveraging HR Culture to Accelerate the Adoption of Chad_HarnessWorkforce Analytics, we interviewed Chad Harness, Lead Human Capital Analytics Consultant, Fifth Third Bank. View the Q-and-A below to see how Chad Harness has incorporated predictive analytics into the workforce of Fifth Third Bank. 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: I originally joined Fifth Third’s Human Capital Analytics Team with the intention of building the predictive modeling capabilities of 5/3rd’s Human Capital Division. However, within my first six months, I realized that I needed to re-prioritize my efforts towards building a workforce analytics-capable culture before I could gain sufficient buy-in for achieving my original goal.

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

A: Organizationally, I am aligned to HR but I primarily work with the Wholesale Line of Business.

Q: What workforce outcomes do your models predict?

A: During my tenure with 5/3rd’s HC Analytics we have implemented two models: one that predicts our expected total mortgage loan officer population as a function of interest rates and another that forecasts retirement likelihood among our commercial relationship manager and wealth management advisor populations. However, the bulk of our analytics’ work focuses on introducing and defining rigorous measures, capturing data, consulting and managing change, e.g. education, promotion and facilitation of the adoption and use of analytics in workforce planning.  

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

A: The retirement likelihood forecasts have been extremely well-received. As a result, the commercial and wealth management lines of business leaders are paying closer attention to their talent pipelines for those roles. Specifically, increased awareness of the need to develop replacement talent has led to initiatives to mitigate the key man risk inherent in these strategically important roles.

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: Not in those terms, no. One of my 2015 initiatives is to create a rigorous framework for measuring the cost and benefit of specific employee segments, beyond just sales and compensation that will provide a common way for Fifth Third to quantify acquisition and termination decisions. I also hope to return to my roots and build a few predictive models against some of Fifth Third’s high-priority workforce challenges.

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

A: One of the most surprising discoveries I’ve unearthed is that some of Fifth Third’s “poor” and “middle” Wholesale performers are also some of our most profitable employees. This suggests that, for certain categories of employee, Fifth Third’s employee valuation methodologies may not be good measures of workforce ROI, our employee performance metrics are missing some important factors of these employees’ performance or Fifth Third’s current Wholesale staffing model may be sub-optimal.

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: I think any area of the workforce that exhibits either high turnover, significant up-front human capital investments or both will see the greatest advances and ROI as retention rates and talent quality improve from the judicious use of insights derived via predictive analytics.

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

A: I think Business Leaders, HR Leaders and Analytics professionals should attend Predictive Analytics World for Workforce because the conference presents an invaluable opportunity both to connect with and to learn from the individuals and groups who are trailblazing the use of predictive analytics to tackle workforce-specific challenges.

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: I feel a tremendous sense of urgency for organizations to prepare themselves to capitalize on both the ubiquity of data created by the exponentially growing “Internet of Things” and the accompanying need to generate and act upon insights that can allow them to create and sustain competitive advantage in a constantly changing landscape. The problem of achieving this transformation may be framed as a set of “employee challenges”: when every product, service and customer encounter becomes a data generating process, every employee needs to be equipped to maximize those opportunities in order for the organization to remain relevant.

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

A: One misunderstanding I frequently encounter is that the use of predictive analytics somehow reduces the need for expert judgment. On the contrary, increasing the use of predictive analytics introduces additional complexity into the decision making process, which requires more expert judgment to ensure favorable outcomes. At a minimum, every employee increasingly must be able to ask good questions, to differentiate and communicate only relevant information, and to recognize and act on the “right insights” at the “right time”.

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

A: Fifth Third has two primary lines of business: Wholesale and Retail. Leaders within both LOBs have been very involved with our work. I would say they are our most valuable partners and primary customers with respect to the work we’ve done inside our organization.  

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

A: You don’t need predictive analytics to see that the skill set necessary for the competent practice of analytics – mathematics, computer science, and business acumen – represent a rare combination that is difficult (and expensive) to obtain but increasingly essential to achieving and sustaining competitive advantage. My presentation will demonstrate proven, cost-effective ways for any organization to begin building, deploying and leveraging that skill-set in its workforce.

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Don't miss Chad Harness’ conference presentation, Names and Numbers: Leveraging HR Culture to Accelerate the Adoption of Workforce Analytics, at PAW Workforce, on Wednesday, April 1, 2015, from 4:15-5:00 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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March 3rd 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Patrick Coolen of ABN-AMRO Bank

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, ABN-AMRO’s 2-year Journey with HR Predictive Analytics, we interviewed Patrick Coolen, Manager HR Analytics at ABN-AMRO Bank.  View the Q-and-A below to see how Patrick Coolen has incorporated predictive analytics into the workforce of ABN-AMRO Bank.  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?

A: Our mission is moving HR towards fact-based decision making. Fact-based decision making that also supports HR itself but primarily our business. For this we conduct different research projects that aim to optimize e.g. workforce productivity, business productivity or client satisfaction. We also invest in training our HR staff so that they can identify the opportunities HR analytics is offering.

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

A: Our analytical team reports within HR. But 80% of our research is focused on a real business opportunity or problem. Therefore we intensively work together with senior business management and their subject matter experts. Some examples of lines of business we do research for are: retail, large corporates, IT, private banking and transaction banking.

Q: What workforce outcomes do your models predict?

A: In most cases we run models predicting client satisfaction or financial outcomes like revenue, profit or target realization and in some cases we also predict the quality of our products or engagement.

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

A: Predictive analytics can help our organization to focus on what really matters. Do we believe all types of leadership are important or can we determine which one is more effective in a certain context? Also predictive analytics can help us evaluate the impact of HR interventions like our leadership program. Both examples allow us to improve decision making. 

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: In one of our research projects we managed to predict products sold in a business unit based on HR data including engagement survey data. The research results were made actionable and are input for the strategic people plan of the business unit involved.

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: I do not think predictive analytics is more beneficial for a specific area of the workforce. We already saw some good results in multiple lines of business. However the line of business that has the ‘best’ business data can expect to receive the best results from analytics. The ‘best’ data can mean

  • financial data on employee level instead of on department level or
  • the availability of historical data or
  • the availability of high frequency process data on human behaviour like call handling data

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

A: Because they are all a piece of the puzzle. Predictive analytics is not only about data scientists. The different areas of expertise have to work closely together to make predictive analytics successful.

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

A: Many organizations do not know how to start or set up predictive HR analytics. One misunderstanding is that you need large investments in technology or skills. Start small and learn fast. If you have a relevant dataset you can start with analytics today. Depending on your own maturity in terms of statistics and/ or machine learning ask a vendor to help you with the basics. 

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

A: In all our research that is focused on predicting business outcomes the business itself is intensively involved. This is true for all our lines of business.

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

A: I will talk about the 10 golden rules of HR analytics (see also LinkedIn under my profile). I will address our lessons learned while setting up analytics and conducting research. Then we will present a case study of one of our real research projects. This will be presented together with Luk Smeyers (iNostix).

Don't miss Patrick Coolen’s conference presentation, ABN-AMRO’s 2-year Journey with HR Predictive Analytics at PAW Workforce, on Wednesday, April 1, 2015, from 11:15-11:45 am. 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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