March 25th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Predictive Job Maps – Modeling an Entire Workforce with a Tree of Models, we interviewed Pasha Roberts, Co-Founder and Chief Scientist at Talent Analytics, Corp. View Pasha_Roberts imagethe Q-and-A below to see how Pasha Roberts has incorporated predictive analytics into the workforce of Talent Analytics, Corp. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions? How is your product deployed into operations?

A: Our clients use our predictions for performance or attrition every day, in the process of recruiting new job candidates. At PAW, I will be talking about rolling these up into a full network or tree of predictions, to predict performance for the candidate’s likely future roles, after a promotion or two. For this, we’ve built several dynamic job maps, and several composite benchmarks, and will soon deploy the full integrated solution that rolls all of this together.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: I’d create a living, interactive, visual map that shows everyone at the company who is going where, and how it affects corporate value. I’d populate the map with hundreds of predictive models to optimize employee changes, and keep it fresh with ongoing research. The result would be a living guide with tangible directives for hiring, terminating, and promoting employees to grow the organization right.

Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks?

A: Not for many years, if they are kept opaque. HR and Hiring Managers want to know what is driving selection, and that it makes sense with a management narrative that they can follow. Sometimes there is a tug-of-war between plausibility and accuracy, but fortunately the human ability to form narrative is strong.

This doesn’t mean you are limited to simple regressions. Variable selection is everything for regression models, and we often use random forests or lasso/elastic net methods to find a set of regression variables that robustly predict.

Also, you can use black-box models like Random Forests, Support Vector Machines or Neural Nets for better accuracy as long as you do the extra work to isolate key variables and trends in them, to build a story for your model users. There are methods to probe a winning black box method to identify variable importance and general directionality. This can take enough opacity off of a model to be able to give “face validity” to its users.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: We often use examples from other domains, preferably their own domain. If I’m explaining a hiring model to a banker, we’ll show how it’s like using a credit score before offering a loan to someone. You don’t extend the loan (or job offer) unless there is a good probability that the person will pay the loan back (or stay on the job, or perform on the job). They get that.

Some of the technical graphs, like survival curves, work well with management users, and you can explain them; to most. Other constructs, like an AUC curve or cluster silhouette plots, are just not going to work with most. We try to win a lot of trust by the time we get to that stage.

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

A: As mentioned above, hiring decisions – choosing candidates who are likely to not terminate early, and who are likely to overachieve (or not underachieve) real business KPIs. We work with clients to identify the KPIs – they are tangible things, like sales per month, or food safety scores, or cash drawer entries. We don’t try to drive mushy middle values like engagement or happiness.

You don’t put graphs or anything fancy in front of a recruiter for hiring/promotion decision. We just deliver calibrated, color-coded color bands – blue candidates are likely to over-perform, red candidates are likely to under-perform. They just pick up that color from the system; maybe get some auto-generated behavioral interview questions and talking points tailored to the candidates, and move to the next step in recruiting.

Real analytics ultimately doesn’t show graphs or paragraphs of “insights” to the user – it just helps them make the decision.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: A predictive analyst needs to earn their trust, and they need to learn to understand that their “gut” is just another form of decision-making. The HR users need to gently see that sometimes the “gut” can be more biased, and less fact-driven than rigorous analytics.

They don’t need to learn Chi-Square or Receiver Operator Characteristic – though we’ve converted several into junior analysts and gotten a few back into graduate school. Managers do need to understand that models are just trying to make decisions based on facts, the way they are, and that they need to be forever learning.

Q: Do you have specific business results you can report?

A: In one case we reduced annual attrition from 84% to 48% in 5 months. That saves the banking client over $1 million a year in replacement cost and employee lifetime value.

In another case we increased the ability to hire successful candidates (as measured by passing a Series 7 exam) by 12% – that high-volume situation saved the client over $4 million a year in replacement cost alone.

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​Don't miss Pasha’s conference presentation, Predictive Job Maps – Modeling an Entire Workforce with a Tree of Models, at PAW Workforce, on Tuesday, April 5, 2016 at 10:25 to 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 7th 2016

Dr. Data’s Music Video: The Predictive Analytics Rap

By: Eric Siegel, Founder, Predictive Analytics World

With today’s release of “Predict This!” – the rap video by yours truly (a former university professor trying to be a pop star) – I took the opportunity to ask a few questions of the music video’s featured character, Dr. Data.

Here is the video, followed by my interview with Dr. Data:

 

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Info, MP3 download, lyrics & more: PredictThis.org

 

INTERVIEW WITH DR. DATA:

Eric Siegel: Please summarize your vision for this rap video.

Dr. Data: Inspired by true events, this video recounts my origin story. More than a dance video for a song about predictive analytics, this film allegorically reconfirms the power of embracing your true inner self.

Eric Siegel: A pop song about predictive analytics? Why?

Dr. Data: Enlightenment. Infotainment tastes great and it’s good for you, too. A song can make a topic some find dry or intimidating fun and accessible. The more sophisticated viewer who listens carefully to the lyrics (click for full lyrics) will learn about this critical technology.

My goal was to think outside the quadrilateral parallelogram and make the best educational predictive analytics geek rap music video ever.

Done deal, due to a certain lack of competition.

And didn’t you yourself used to be a singing computer science professor?

Eric Siegel: Yeah, in the late 90s at Columbia University I would show up for a lecture with my keyboard and perform, for example, a rock ballad about the angst of debugging (online recordings are available). And in 1999, I published and presented on “Why Do Fools Fall Into Infinite Loops: Singing To Your Computer Science Class” (PDF of paper) at an education conference in Poland.

Dr. Data: Did your students like the songs?

Eric Siegel: Yeah, college students would much rather be at a rock concert, even a really bad one. One metric used in the above education paper was duration of applause.

Dr. Data: You, me, and Lady Gaga, we love that applause.

Eric Siegel: How many countries did you travel to for this video shoot?

Dr. Data: Well, truthfully, I was going to those countries anyway… but the video includes 10 locations across 5 continents – 6 countries plus Antarctica (did you notice the penguins?), which is a continent without any countries. Nor time zones or currency for that matter.

Eric Siegel: So, no green screen?

Dr. Data: Only for the outer space shots.

Eric Siegel: I noticed you play chess against a robot dancer in the video.

Dr. Data: More rap artists ought to also play chess with their dancers. In fact, that dancer (Claudine Quadrat) is actually a chess champ in real life, with a 1998 trophy from Kimball Wiles Elementary School to prove it.

Eric Siegel: That’s funny, cause I also was a childhood winner (at age 13, the 1982 Burlington, Vermont city chess champion of the “Booster” section, the lower of two sections across all ages).

Dr. Data: I’m glad things worked out in the end, what with the audience joining your party and your appearance on the cover of People Magazine.

Eric Siegel: It seems kind of absurd that, as Thomas Davenport and DJ Patil famously put it, data scientist is the sexiest job of the twenty-first century. Isn’t that status reserved for firefighters? But you, Dr. Data, really gave me courage with your mad flow and dance moves. I’ve always felt I was a pop star stuck in a geek’s body. And now I’ve got the moves like Jagger… errr… like his sound engineer.

Dr. Data: By the way, watch for the talented actor Nic Frantela in the video. He narrated the audiobook for Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die – and can be seen as the first of two individual men shown to transform magically into geeks (and elsewhere in the video).

Also check carefully the People Magazine cover at the end for two true-life predictive analytics gurus who frequently speak at Predictive Analytics World: John Elder and Dean Abbott.

Eric Siegel: Who are you, Dr. Data? Where do you come from?

Dr. Data: Isn’t it obvious? I’m your alter ego, dude.

Eric Siegel: You mean I’ve been interviewing myself this whole time?

Dr. Data: Gimme a hug. It’s time you embrace your inner geek.

Eric Image 2015 croppedEric Siegel, Ph.D. (aka Dr. Data) is the founder of the Predictive Analytics World conference serieswhich includes events for business, government, healthcare, workforce, manufacturing, and financial servicesthe author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—Revised and Updated Edition (Wiley, January 2016), executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide and follow him at @predictanalytic. Inquiries: eric@predictionimpact.com.

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March 7th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Employee Attrition in the Knowledge Economy – The Value lies in the Details, we interviewed Geetanjali Gamel, Predictive Analytics Leader, Global HR at G Gamel imageMasterCard. View the Q-and-A below to see how Geetanjali has incorporated predictive analytics into the workforce of MasterCard. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: We are doing work to understand and predict employee attrition risk through various lenses. Insights derived from analyzing risk by key talent segments are more likely to be top-of-mind and actionable. There is interest in using this analysis to provide the business with enhanced workforce intelligence around emerging trends in talent flight risk.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: Great question! A powerful recommender engine that leverages internal and external employee data on skillsets, experience, personality, interests, working-style and engagement to double up as a dynamic career pathing tool for individuals, and team builder for managers. It could be a multi-purpose solution for   career development and internal talent pipeline building. Also the same data could be harnessed for talent assessment during acquisitions.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: As the value of investing in predictive workforce analytics begins to be more widely known and proven with tangible results, the appetite for complex techniques, including such “black box” methods, will grow.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: Think about who your audience is and what matters most to them. Complex and novel methods may excite data scientists, but almost everyone else wants to know “So, what do I do with this now?” and “What value does this create?”

If the ask is for strategic solutions, focus on explaining how your analysis fits in with the big picture and impacts overall results for the business. If the ask is tactical, translate your work in terms of how someone could use it in their everyday functions.

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

A: Employee attrition is one area in which businesses are leveraging predictive analytics.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce.

A: Predictive workforce analytics is still in early stages of development at many companies. From both the business and HR side, it will take patience and commitment to realize the value of investing in this critical initiative. People analytics teams will have to collaborate with HR and business customers to develop a greater level of comfort among them in using data-driven insights to make better decisions.

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Don't miss Geetanjali’s conference presentation, Employee Attrition in the Knowledge Economy – The Value lies in the Details, at PAW Workforce, on Tuesday, April 5, 2016, from 3:30 to 4:15 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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February 29th 2016

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 San Francisco, April 3-7, 2016, we asked Dr. Patrick Surry, Chief Data Scientist at Hopper, a few questions about Patrick_Surryhis 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, April 4, 2016 from 1:30 to 2:15 pm, and Patrick’s conference presentation, Applying Next Generation Uplift Modeling to Optimize Customer Retention Programs, on Monday, April 4, 2016 from 11:20 am to 12:05 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 26th 2016

Wise Practitioner – Predictive Analytics Interview Series: Ken Elliott at Hewlett Packard Enterprise

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Operationalizing Analytics:  10 Key Process Areas for Embedding Predictive Analytics into Business Operations, Applications and Machines, at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Ken Ken ElliottElliott, Global Director of Analytics at Hewlett Packard Enterprise, 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: Being part of Hewlett Packard Enterprise Services, we are fortunate to work with customers across a wide variety of industries and use cases. As a result, our predictive models are pointed at numerous business outcomes.  Some of the most common are predicting machine failure, application downtime, security breaches, financial risk, supply constraints, optimal pricing, next best offer, customer loyalty and many more.  As most of our clients have made progress on their big data journey many are now focusing on gaining value from these investments.  They are turning to analytics to help.  This has led to the growth in predictive analytics over the past few years.

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

A: Auto manufacturers face tremendous costs of recalls as a result of product failures. Not to mention the potential public safety risk.  Early detection of part failures can save hundreds of millions of dollars.  Recently, we worked with an auto manufacturer to develop predictive models that bring together product service events, manufacturing quality records and linguistic analysis of warranty service records to identify previously unknown and emerging part failures.  Using predictive models allowed for identification if emerging issues much earlier than before. This enables the after sales manager to quickly identify potential issues, assess the risk and determine the proper course of action.  In this case predictive analytics has been recognized as a critical technique for reducing overall cost and risk.

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

A: On any given day there are over 150,000 active marketing leads at Hewlett Packard Inc. This represents everything from customers who register to download a whitepaper, register for a seminar, or contact our sales offices.  HP Marketing has the task to sort, scrub, review, assess and decide how to handle each lead.  Ultimately, deciding which leads to pass to Sales.  This challenge was growing given the number of digital channels HP has to interact with customers and prospects.  We worked with HP Marketing to develop a predictive model that leveraged multiple data sources to score each lead with the likelihood to buy, potential deal value, best offer and best response channel.  The output of the predictive models were then fed into a decision management framework which automated the actions based on business defined rules and thresholds.  This process led to an increased lift of 24% in conversion over the previous manual efforts which translated into a 30% increase in response conversion.  HP Marketing was able to reduce the cost of this business process by 60%.  More importantly, HP customers received quicker and more relevant responses.

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

A: We recently analyzed 100s of sensors from a specially outfitted Volkswagen Touareg as part of the multi-day Cape-to-Cape world record race.  We had all types of data, including temperature, pressure, telemetry, steering, braking and more.  With no prior knowledge, we asked our data science team to run cluster analysis to see if they could identify how many drivers there were and when drivers switched, based only on sensor data.  Our models predicted “3” drivers, profiled them based on optimal driving behavior and plotted the predicted driver on the route.  The model worked extremely well.  There were in fact 3 drivers; the route had a high match rate to the driver logs.  One interesting result was that one driver seemed particularly different from the others. We later learned that there were actually two professional drivers and the cameraman took shifts for parts of the journey.  It was surreal to see three world-record-holding drivers having a good laugh over a cluster scatterplot.

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

A: The power of predictive analytics is undeniable.  Especially now as data volumes are exploding, traditional business intelligence techniques are unable to keep up and find insights in this growing data.  However, we are hearing over and over again from customers who have internal analysts building models, that there is chasm between developing a predictive model and putting that model to use across the organization.  We call this Operationalizing Analytics.  Based on our work with customers, we have identified 10 process areas that are important to crossing this chasm from developing predictive model to putting those models into action for scalable and sustainable business impact.

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Don't miss Ken’s conference presentation, Operationalizing Analytics:  10 Key Process Areas for Embedding Predictive Analytics into Business Operations, Applications and Machines, on Monday, April 4, 2016 from 3:05 to 3:25 pm, at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 24th 2016

Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller at Constellation Research

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of his upcoming Predictive Analytics World for Workforce conference Holger_Muellerpresentation, The State of Predictive Analytics for Workforce in Enterprise Applications, we interviewed Holger Mueller, Principal Analyst & Vice President at Constellation Research, Inc.  View the Q-and-A below to see how Holger has incorporated predictive analytics into the workforce of Constellation Research, Inc. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: Enterprises use predictive analytics all over workforce decisions – recruiting, performance management, learning, compensation, career planning are the most prominent use cases.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: Hands down workforce planning.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: Never – business users will just consume analytics solutions that work for them and not care about the technology and analytical magic sauce behind them…

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: If you had the guarantee to win the lottery 1 out of 10 times – you would not ask how it works. You would play the lottery! If a predictive algorithm makes an HCM decision better – why ask and try to understand why it is better? Just use it. 

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

A: Making more objective decisions than humans, and saving precious time.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: Time pressure will make business professionals adopt predictive analytics in droves, with HR liking, supporting it – or not.

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Don't miss Holger’s conference presentation, The State of Predictive Analytics for Workforce in Enterprise Applications, at PAW Workforce, on Tuesday, April 5, 2016 from 1:30 to 2:15 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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February 22nd 2016

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

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting the Extent and Cost of Online Attacks to Help Sell Security Software, at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Lawrence Cowan, Partner at Cicero Group, a few questions about Lawrence Cowan imagehis work in predictive analytics.

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

A: A good portion of the advanced analytics work we do at Cicero deals with 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 or operations?

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 quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: In a recent effort to help a financial institution optimize its marketing campaigns, we developed a model that identified customers who were more likely to respond to a CD campaign, and who were more likely to bring “new money” to the CD (opposed to simply shifting money from another account at the financial institution).  The results were very impressive, including the following metrics: 14x higher response rate, 4x increase in average deposits, 60% in “new money” compared to just 3% “new money” in the control group.

Q: What surprising discovery or insight 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: I’ll share a real-world example of turning a predictive model into an online sales tool.

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Don't miss Lawrence’s conference presentation, Predicting the Extent and Cost of Online Attacks to Help Sell Security Software, on Tuesday, April 5, 2016, from 11:20 to 11:40 am, at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 15th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Raffael Devigus at F. Hoffmann-La Roche AG

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of his upcoming Predictive Analytics World for Workforce conference co-presentation, The Predictive Workforce Analytics Journey at F. Hoffmann-La Roche, we Raffael Devigus imageinterviewed Raffael Devigus, Management Reporting Analyst at F. Hoffmann-La Roche AG. View the Q-and-A below to see how Raffael Devigus has incorporated predictive analytics into the workforce of F. Hoffmann-La Roche AG. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: In an ideal world, tracking the engagement, performance and potential of employees across the entire employee-lifecycle would allow us to see which processes and policies contribute most to these three key areas and where there are areas of improvement. In exchange, the employees would be able to receive feedback continuously, containing for example customized recommendations on how they can achieve the biggest developmental impact on their careers. Additionally, managers would have an unbiased, fair, and less time-consuming way to rate their employees' performance, meaning we could even solve the infamous performance management puzzle.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: I think they already are. The reason I say this, is that I believe there to be predominantly three kinds of non-data scientists: Firstly, there are the people who trust that the data and work performed on it are solid and are therefore only interested in the results and recommended next steps. Naturally, it is very easy to convince these people of using "black box" models, which generally have the advantage of higher accuracies. The second group contains people who are interested in the chosen approach and how one arrived at the result. The advantage when dealing with these kinds of people is, that they are curious and actively try to understand how one arrived at a result.

If one can explain things well to them (e.g., using analogies and examples) and answer their questions, they are usually also willing to be convinced of the strengths of "black box" models. The third groups are the skeptics, who are critical of any kind of data-driven methods. From my experience, it usually does not even matter to them, whether a method is "black-box" or not. Fortunately, in my experience there are only very few people that belong to this group, while most of the people seem to belong to the two groups, which can easily be convinced of the advantages of "black box" approaches.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: Usually I try to avoid complexity whenever possible. The reason being, that most of the stakeholders I deal with in my everyday work are focused primarily on the results of an analysis. For them, the key is to get clear recommendations, which support them in a decision-making progress. Additionally, information on the quality of both underlying data and evidence found is oftentimes also important. However, the way in which one arrived at a result exactly, is rarely requested. This certainly can be a paradigm shift for someone with an academic background, where the chosen approach is at least as important as the result. However, in the rare case I do need to explain something complicated related to data science, I always try to find good analogies to do so.

Despite the obvious examples encountered in Stats 101 courses, a source of inspiration are famous intro-level analyses you would find on sites such as Kaggle. A retention analysis is ultimately the same as the famous Titanic Challenge, with the only exception being, that the predicted binary outcome is less grim. For challenges like this, one can find loads of different approaches online, which are often times explained and visualized in easy to understand ways and hence can be adapted directly.

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

A: In my experience, predictive analytics does not replace managerial decision-making but serves as a tool to guide it. While you can and should make recommendations based on data, it is the customers on the business side that should come up with final decisions. This way you can also avoid the perception of decisions being made in the "data science ivory tower", thus increasing their acceptance in the organization. What is also important to note, is that decisions are rarely made at the time of a results presentation. Usually a presentation of results triggers a lot of conversations which in turn generate many follow-up questions, which again result in follow-up analyses and new discussions. This process can repeat several times until different concerns and opinions have been addressed and the majority can agree on an ideal decision.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: I think it is very important that analytics do not just happen between senior leaders and an analytics team "behind locked doors", transparency is key. I believe that despite potentially slowing down analytics initiatives initially, transparency will pay off in the long run. I also believe that every company should first convince their HR Business Partners to start using more data-driven approaches in their daily job, which would again spread this mindest even further. Based on my own experience, sharing the results of an analytics project, even if only in an aggregated matter, creates a huge word-of-mouth marketing inside the organization with many areas of the business wanting to do something similar, thus spreading the usage of data-driven approaches.

Another big trend that I see currently happening is the growing amounts of unstructured data, which HR processes create (mainly from the collection of employee feedback). In my opinion, it is often underestimated, that when analyzed well, this data contains valuable information on the employees’ satisfaction and a range of other metrics, which can sometimes even help to explain phenomena structured data cannot. Additionally, Millennials are always said to love providing feedback, giving them a chance to do so therefore should prove as a win-win situation. After all, listening to your employees is considered a crucial skill for leaders, why then shouldn't this also apply to the entire company?

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Don't miss Raffael’s conference co-presentation, The Predictive Workforce Analytics Journey at F. Hoffmann-La Roche, at PAW Workforce, on Tuesday, April 5, 2016, from 9:50 to 10:35 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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February 12th 2016

Wise Practitioner – Predictive Analytics Interview Series: Rebecca Pang at CIBC

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Driving the Omnichannel Experience with Predictive Analytics at Predictive Analytics World San Francisco, April 3-7, 2016, we Rebecca Pang imageasked Rebecca Pang, Senior Director, Channel Strategy & Analytics at CIBC, 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: With customer delivery and communication channels expanding, it is increasingly important for banks to engage the customers better and more efficiently by creating the kind of omni-channel experience that fit customers’ needs. We use predictive analytics (when combined with test and control) to predict client’s transaction behavior and financial implications by varying a number of levers to see which lever is most impactful.

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

A: Making changes in a network of >1000 branches with thousands of frontline staff can be costly. Using predictive analytics, we are able to test ideas rapidly and efficiently, evaluate results, pin-point success drivers, revise initiatives and predict results on a wider rollout. We have been using predictive analytics and test & control on initiatives covering new sales role, new channel design and functionalities, product campaign, staff training and incentives.

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

A: In a particular pilot, we have achieved certain % of sales increase within a couple of months of conducting a number of sales and marketing levers. By segmenting what areas and what types of branches reacting or performing the best (say sales increase), we were able to estimate the impact would be for a wider rollout or how we should prioritize the roll out.

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

A: Often times we launch a pilot with an expectation that certain drivers (e.g., certain customer segments, certain branch characteristics, certain demographics factors) will react better than others based on common sense or conventional wisdom. Through a well-designed test, we were able to uncover surprising drivers (some are casual and some are likely not). With such discovery, we were able to refine the model objectively without relying on gut-feel.

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

A: The importance of developing the analytic muscle and test and control culture with internal information and online activity to understand behaviors and profiles of customers with a true 360 view (not only who they are, and what they do).

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Don't miss Rebecca’s conference presentation, Driving the Omnichannel Experience with Predictive Analytics on Monday, April 4, 2016 at 2:40 to 3:25 pm at Predictive Analytics World San Francisco. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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February 10th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Daniil Shash at Eleks

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, How Eleks is Building a Career Advisor Tool Based on Predictive Analytics, we Daniish Shashinterviewed Daniil Shash, Head of Data Science at Eleks. View the Q-and-A below to see how Daniil Shash has incorporated predictive analytics into the workforce of Eleks. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: We’re into software development business, so our operations are project based teams, that consist of different employees – developers of different seniority levels and technology stacks, project managers, UI/UX experts, tester, product managers and others. Project durations, team sizes and compositions are mostly different. Projects may last from 6 months to 20+ years, as long as our company exists! At the same time team size may vary from 3-4 employees to 100+ employees in one team.

What we were interested in was to understand what projects are successful and how does combination of skills, experiences, trainings and other team member individual data influence project overall performance. We’ve been looking for correlations and causations between different project attributes and team and individual attributes.

This is what we wanted to understand predict – project performance. Actually, we’re interested in moving from predictive to prescriptive analytics, so what we want is not only to predict project performance but to be able to influence it – understand what we need to do to improve performance in terms of specialists involved.

The users of our solution is talent staffing office, as we call it – department responsible for assembling teams for different projects. So our goal is to help them build teams that are capable of delivering highest quality for projects with specific attributes. By using the tool they get support in deciding which employee would be a good candidate for a team and which would not. We’re still working on the project and we realize that where we are now is just the first step. Complete solution not only would recommend available specialists for specific projects but would help our company understand which skills bring more value and so develop them internally or bring from outside of the company.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: Businesses are using those methods for other purposes, including customer segmentation, image recognition and recommender engines, so generally the business is ready now. The question is when will this trend reach workforce management? Now, why are businesses ready to trust “black box” methods in advertisement and marketing but are not in talent and workforce management? What is needed for business to trust such methods in workforce management? I believe the answers are on the surface – proved and predictable performance improvement. Once we will be able to predict and prove performance improvement of using “black box” or any other predictive method – then we will have business trusting and investing in workforce predictive solutions. Stakes are much higher with talent decisions then they are with another targeted audience advertisement, so business leaders want to be sure they are making the right decisions, especially with “black box” methods.

Ability to understand and predict measurable business impact of the workforce related decisions actually opens a whole new set of opportunities for HR leaders. You can only be a real partner to business when you can influence business results and predictive analytics is something that makes it possible. Talent (workforce, employees, specialists) is what makes a difference for business nowadays, all the innovative and game-changing decisions are made by humans, disruptive products are made by humans, human capital is the core business asset (well, maybe not for oil and gas industry, which is not in their best days now). So imagine that you can help business understand how to significantly improve and strengthen their core business asset? This, in my opinion, is the key opportunity for HR leaders in predictive workforce analytics: Opportunity to drive the business forward rather than support its needs.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: The main goal for predictive workforce analytics in my opinion is to help in taking better workforce decisions with predictable business impact. In practice, it means that HR leaders will need to work closely with other company executives on strategy and business goals and transform them to workforce and talent related decisions; from training and development to new jobs profiles creation. What does it mean in terms of business culture changes? It means that HR leaders need to be even closer to business and business processes, understand and work on company development strategy, understand financials and be a part of a board for some organizations. At the same time, business needs to stop treating HR as a supporting function but start realizing that HR, as we call it now is something that is as a key

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Don't miss Daniil’s conference presentation, How Eleks is Building a Career Advisor Tool Based on Predictive Analytics, at PAW Workforce, on Monday, April 4, 2016, from 3:55 to 4:40 pm. Click here to register for attendance

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

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