February 1st 2016

Wise Practitioner – Predictive Analytics Interview Series: Mario Vinasco at Facebook

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Advanced Experimentation in Social Networks at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Mario Mario V imageVinasco, Marketing Analytics Data Scientist at Facebook, a few questions about his work in predictive analytics.

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

A:  We use predictive models for several different purposes mainly in the context of advertising campaigns and sentiment measurement.

  • To identify lookalikes: these models take demographics and behaviors and identify users with similar characteristics.
  • To partition the social graph into clusters of connected users to run network experiments
  • To attribute product usage to a campaign: there are many sources of bias in experiments, for example the Ad optimizer bias the treated or exposed group while the control group remains intact; we use inverse propensity and regression models to rebalance.
  • To rebalance survey responses between tests and control groups due to biases induced by the Ad delivery engine.

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

A: We can accurately attribute and quantify campaign investment to outcomes and can create target audiences where we can increase either product usage or sentiment.

We have invested in markets and hero products accordingly.

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 network based experiment, we were able to attribute the ROI of a very large ad campaign whose message went viral, and because of these network effects a traditional A|B test was not possible.

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

A: It has been very interesting to see that the law of long tails apply over and over; it can be product usage, likes received on a pots, etc., and this illustrates how deceiving simple averages can be.

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

A:  People will understand better some of the dynamics of social networks, especially experimentation set up.

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Don't miss Mario’s conference presentation, Advanced Experimentation in Social Networks, on Monday, April 4, 2016 at 4:45 to 5:30 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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January 29th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Vishwa Kolla at John Hancock Insurance

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference Vishwa Kolla imagepresentation, Embedding Advanced Analytics into Acquiring, Nurturing and Retaining Talent, we interviewed Vishwa Kolla, AVP, Head of Advanced Analytics at John Hancock Insurance. View the Q-and-A below to see how Vishwa Kolla has incorporated predictive analytics into the workforce of John Hancock Insurance. 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: My function embeds Advanced Analytics (AA) into every decision making – be it to identify good prospects from the US population, be it to streamline acquisition (i.e. to reduce cycle time from 45 days to 1 day) or to be it to deliver on the promise of customer centricity (i.e., be prescriptive with their needs). Advanced Analytics journey takes about 9 – 12 months to develop, integrate, test, monitor and refine. We follow a 5 step maturity process to embed AA into everything that we do. We started out with one BU (Insurance) and the process transcends BUs and functions.

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

A: We will tackle the employee retention problem first. The success of a firm depends heavily on the number, distribution and longevity of star performers. Star performer productivity in a firm increases exponentially with time as they build relationships and learn processes in the firm. After solving the problem of keeping the stars, we will tackle the problem of getting stars in and then follow-up by nurturing them.

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

A: We are ready now. Any PA problem has two dimensions – the inputs (data) and how these inputs are combined (model). Our PA process involves variable selection prior to model build. After this step, we build two classes of models – truly predictive and explanatory. Since the starting variable set is the same, the truly predictive model is used to get most lift and the explanatory models are used to describe their predictive power. We always augment model build with reason codes (for individual scores). These codes are critical to driving model adoption.

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

A: A model is only as useful as its adoption. As data scientists, knowing what the data means, understanding the limitations (data collection, quality, transformation, sufficiency, completeness, modeling, interpretation, action-ability) and finally building visuals to explain these limitations will help with obtaining confidence of Business and Operational users.

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

A: This aspect is covered in the first question.

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

A: It all starts with educating HR and its clients – PA primarily helps with bringing objectivity to decision making. It then starts with being as transparent as possible and giving a look under the hood. It is very important to talk about the limitations and the separation of the signal from the noise. To drive meaningful adoption, it is important for PA function to collaborate and not compete with HR. It is also important to understand that this is a journey and not a one and done deal.

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

A:  The first step of any effective PA process is to build a business case. This involves a) identifying the business problem (retention say for example), quantifying the cost / benefits (costs of acquisition, productivity losses (direct and indirect), opportunity) and quantifying the impact of intended consequences (how much of this problem can be solved using PA, the costs of false positives and false negatives) and being open to unintended consequences. Depending on the problem, a 15 – 20% ROI should be used as a reasonable threshold when deciding between competing PA projects. The actual dollar impact will be a function of the size of the problem being tackled.

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Don't miss Vishwa’s conference presentation, Embedding Advanced Analytics into Acquiring, Nurturing and Retaining Talent, at PAW Workforce, on Monday, April 4, 2016, from 2:40 to 3:25 pm. Click here to register for attendance

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

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January 27th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: John Lee at Equifax Workforce Solutions

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference co-presentation, Using Predictive Analytics to Reduce Unemployment Insurance Costs, we John Lee imageinterviewed John Lee, Statistical Consultant at Equifax Workforce Solutions. View the Q-and-A below to see how John Lee has incorporated predictive analytics into the workforce of Equifax Workforce Solutions. 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: My solutions have been deployed in a variety of settings including marketing and HR.  In some situations, these solutions are deployed into an automated production environment.  In other areas, reports can be refreshed on an ad hoc basis.  But in both situations end-users receive a report that helps them make intelligent business decisions.

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

A: I think you have to start simple.  The tendency I’ve noticed is to overcomplicate things.  If HR were 100% ready (and at some companies HR is ready in this way) I would look to marry together different sources of data and build solutions that offer value for companies (e.g. people analytics, compensation analytics).

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 businesses are ready now.  The argument that a random forest or neural network is more “black box” than a logit (for instance) is somewhat overstated in my opinion.  For example, I think logit coefficients are confusing for most audiences and I always represent these estimates as changes in probabilities.  A random forest or neural network is more challenging to interpret but in general you can create the same insights (e.g. predicted probability for a binary outcome).

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

A: Focus on simple insights because business users don’t care about the complexity of your method, they care about the value your work brings to the company.  So, for example, whether a data scientist is using a logit or random forest they should always produce simple graphics or tables that illustrate how businesses can improve their bottom line or the state of their workforce.

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

A: Quite a few businesses already use churn models to understand why their employees leave.  As an extension, some companies use predictive analytics to prevent the churn of top performers.

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Don't miss John’s conference co-presentation, Using Predictive Analytics to Reduce Unemployment Insurance Costs, at PAW Workforce, on Monday, April 4, 2016, from 3:55-4:40 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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January 25th 2016

Wise Practitioner – Predictive Analytics Interview Series: Peter Bull at DrivenData

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting Restaurant Violations via Yelp Reviews: Crowdsourcing for Social Good at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Peter Bull, Co-founder at DrivenData, a few questions about his Peter Bull imagework in predictive analytics.

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

A: Our models predict the count of hygiene violations of different severities for any given restaurant in Boston on any given day. These can be used by the Public Health department to prioritize which restaurants they inspect.

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 can allow cities to be more effective in serving their citizens. One of the best use cases is targeting city resources where they can make the most difference. In this example, it takes the form of sending inspectors to restaurants that are likely to have the most severe violations in order to protect public health.

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

A: Using the same number of inspectional resources, the City of Boston could cite 40% more violations by effectively targeting which restaurants would be inspected. 

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

A: Public-private data sharing partnerships can have a great impact on the quality of services that cities provide their citizens.

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

A: By layering on new, non-traditional data sources—and using modern analysis techniques, such as sentiment analysis—cities can change how they operate for the better.

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Don't miss Peter’s conference presentation, Predicting Restaurant Violations via Yelp Reviews: Crowdsourcing for Social Good on Monday, April 4, 2016 at 4:45 to 5:30 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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January 18th 2016

Wise Practitioner – Predictive Analytics Interview Series: Matt Bentley at CanIRank.com

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting Online Marketing Success: Five Lessons Learned at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Matt Bentley, Founder of CanIRank.com, a few questions about his work in Matt Bentley imagepredictive analytics.

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

A: We’re using predictive analytics in several places in our SEO software CanIRank.com:

  • predicting whether or not a customer’s website is competitive for a given keyword
  • classifying web pages as blogs, stores, social media, directories, etc.
  • predicting whether or not a customer might be able to earn a link from a given web page.

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

A: Online marketing has experienced an explosion in the availability of data within a relatively short period of time — social, behavioral, mobile, etc. In the blink of an eye we went from basically groping in the dark to having more data than it’s feasible for humans to process. By leveraging predictive analytics, our software can cull the important, high level actionable opportunities from all of the noise, so marketers can focus on actually getting things done, rather than spending all their time analyzing. SEO is a competitive field, so gaining 5 or 10% productivity over your competitors can make the difference between getting all the clicks and languishing in obscurity on page 2.

Prior to CanIRank’s predictions, SEOs would often choose to go after keywords that were simply too competitive for their website, wasting months of effort or worse, desperation leading them into “aggressive” black hat SEO strategies that could cause their site to get penalized. With CanIRank, SEOs can focus on targeting keywords where they have a high likelihood of ranking, hitting many more home runs in the same number of at-bats. Thanks to predictive analytics, the battle is won before it is even fought (to paraphrase Sun Tzu).

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

A: Since our software is used by a variety of clients the ROI depends upon what their previous processes were, but I can say that we were able to nearly double the accuracy of the best legacy SEO tools.

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

A: The web is a messy, chaotic place for a rational data scientist, and it seems that once your sample size becomes large enough, the probability of impossibility occurring approaches 1 :).  Deriving insights from this kind of noisy, error-prone, and semi-structured (at best) data requires some interesting jungle-style data cleaning guided by a hefty dose of domain expertise.  I’m not sure my professors would approve.  

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

A: Predictive analytics for startups: how to ensure customers appreciate the stunning brilliance of your algorithms:

  • How to work with messy web data;
  • Sometimes greater transparency is worth more than performance;
  • How to deploy predictive analytics models in real time web app environments.

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Don't miss Matt’s conference presentation, Predicting Online Marketing Success: Five Lessons Learned on Monday, April 4, 2016 at 11:45 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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January 15th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Lisa Disselkamp and Tristan Aubert at Deloitte

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

In anticipation of their upcoming Predictive Analytics World for Workforce conference co-presentation, Predictive Analytics Unlocks Sustainable Cost Reduction In Hourly Workforce, we interviewed Lisa Disselkamp, Director at Deloitte, and Tristan Aubert, Senior Lisa Disslekamp imageConsultant – Advanced Analytics & Modeling at Deloitte. View the Q-and-A below to see how Lisa and Tristan have incorporated predictive analytics into the workforce of Deloitte. 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?Tristan Aubert image

A:  [Lisa Disselkamp] Our tool is being used to evaluate operational and financial impact of regulatory changes to exempt labor status by finance, HR and operations. (This is really for 2016.)

Our tool is being used by finance and HR to evaluate compensation policy costs based on operational issues of unique units in their business. They are looking at the necessity of pay programs based on what drives labor demand and employee supply. Based on the analysis, even high cost pay programs may be acceptable based on operational requirements, and low cost programs may stop being overlooked because operationally they are unnecessary. 

Decision makers are leveraging the tool to evaluate when and what to make changes to job assignments to reduce cost and maintain necessary front line activities at an acceptable level given the specific situation.

In 2016 our tools will be instrumental in responding to changes in the salary threshold for exempt employees resulting from proposed changes from the Department of Labor. Employers of all types will need to predict the potential cost of converting employees to hourly and the capacity to provide adequate labor hours under more constrained conditions to run their business. 

A:  [Tristan Aubert] The businesses would use the tool to identify units/teams with higher than expected overtime, understand the drivers of overtime, and make use of this data to inform the actions they would like to take to address this issue before it occurs.

The tool itself does not provide recommendations, rather it provides additional clarity into the causes of overtime and predicts – based on past history – which units are most likely to be prone to overtime and why. For this tool to be used most effectively it needs to be paired with strong domain knowledge into how to best mitigate the drivers that cause the issue and with the business knowledge to determine what areas are in need of attention. Some units may naturally be more prone to overtime – (provide example) – though this does not necessarily mean the functioning should be changed.

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

A:  [Lisa Disselkamp] HR would be able to forecast how pay policies and schedules are driving labor cost, productivity and revenue. People drive the business and the bottom line and having a more clear picture of how compensation motivates workers and how schedules drive attendance, recruitment and retention would enable them  to position workers based on schedule fit, worker skills and cost. They would also help managers influence the daily situations where pay policy and work opportunity converge and potentially inflate labor spending or upset employees.

A:  [Tristan Aubert] The right data would mean finding the best approach to manage a person based on their skillset and place in their career to match them with the best opportunities within a firm. I think it’s fairly safe to say that if people enjoy what they do, they will be more productive, motivated and generate better long-term results for their employers. The right data would help define what employees do enjoy about their work situation – how many hours they work, when they work, the predictability and stability of the hours they work, work that includes the activities they want to perform and skills they want to build, all go into job satisfaction. So a comprehensive approach to aligning people to what they most enjoy and are suited for would be the boldest data-science creation.

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

A:  [Tristan Aubert] When businesses have understood the implications of using black-box models and determine where it is appropriate to use and where it is not. There are no major technical hurdles to creating black-box models however, human resource challenges are not easily ‘optimized’ in the manner that engineering challenges are. Furthermore, it must be appreciated by end users that models are not infallible or necessarily fair –they tend to reflect pre-existing biases – and there is risk involved in this, doubly-so with black-box models where the mechanics are not well understood.

A:  [Lisa Disselkamp] It’s going to take time and practice. Organizations are going to have to lay out a plan that includes incrementally changing the way organizations operate. It could take many rounds of change to build up to these methods.  Readiness will come when they have developed the confidence and ability to incorporate predictive analytics into their decision making, not use it to replace human decision making. They also need to be skilled in modeling and testing to validate these intelligent systems are leading them to the proper conclusions. Readiness is not just about conversion but about using tools such as these to strengthen business processes and decision making. The decision making processes and the data must be sound before these tools are deployed. Readiness may involve developing or hiring for the right skill sets which include knowing how the business will be impacted by these predictive tools and managing the transformation to a data driven model.  

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

A:  [Lisa Disselkamp] Take the time to show them how using the wrong data or not asking the right questions can give a false answer; map out how visualizations are produced so that they are not fooled by charts and graphs and understand what must go on behind the scenes with the mathematical modeling.

A:  [Tristan Aubert] If you can’t explain it simply, you don’t understand it well enough – Einstein’s maxim is extremely applicable in this case. A good starting point is to always try and establish a clear link between what you are doing as and the problem at hand. Usually, it is advisable not to dive into the technicalities of the work you are doing but rather to explain – in jargon-free terms – why a particular activity is necessary to arrive to the solution.

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

A:  [Lisa Disselkamp] Making decisions based on data isn’t new. What is new is that predictive analytics gives leaders more confidence to make not only bold moves, but measured moves that are based on solid data and give leaders greater confidence in sustainability once a decision is made. Making a change to an outdated pay policy is easy, getting the outcome you desire and having that “stick” are where predictive analytics can refine and bolster decision making.

A:  [Tristan Aubert] Workforce retention models are actively used by analytics-savvy firms to improve on their ability to retain their at-risk talent. As a result of being able identify which segments of the workforce are most at risk of departing, they are able to make informed decisions on how to pursue those individuals deemed critical to the workforce.

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

A:  [Tristan Aubert] A few things need to happen. First, data generating systems need to be designed with analytics in mind – without good data no insights can be uncovered. Second, business culture needs to understand how to make use of the insights driven by analytics – this means both determining where analytics fits in their day-to-day decision process as well as understanding the nuances and limitations of analytics. Finally, businesses need to develop an imagination of the types of questions that can be addressed by analytics – this will mean going beyond known problems and starting to try to uncover unknown-unknowns.

A:  [Lisa Disselkamp] To do that, HR leadership needs to be willing to go beyond superficial changes and have the courage to fundamentally change how their organization operates. Systems, processes and policies may need to be completely abandoned in the new world of work. HR needs to accept that behavior will be changed most effectively when predictive analytics are used to their full potential. Resist the temptation to do what you know, embrace disruption and recognize where you do not have sufficient skills. 

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Don't miss Lisa and Tristan’s conference co-presentation, Predictive Analytics Unlocks Sustainable Cost Reduction In Hourly Workforce, 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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January 12th 2016

Free audiobook – Revised & Updated “Predictive Analytics”

Predictive Analytics Book

 



Announcing the Revised & Updated Edition of
Predictive Analytics by Eric Siegel

 

 

Breaking news: January 11 marks the release of the Revised and Updated Edition of Eric Siegel’s acclaimed Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Free audiobook with order of paperback or e-book – immediate access

Award-winning | Used by over 30 universities | Translated into 9 languages

An introduction for everyone. Learn how predictive analytics reinvents industries and runs the world. Rather than a “how to” for hands-on practice, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Read on to discover how it combats risk, boosts sales, fortifies healthcare, optimizes social networks, toughens crime fighting, and wins elections.

WHAT’S NEW:

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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

 

 

“A mesmerizing and fascinating study.”
— The Seattle Post-Intelligencer

“The Freakonomics of big data.”
— Stein Kretsinger, founding executive of Advertising.com

“Eric Siegel's Predictive Analytics is the most readable (for we laymen) 'big data' book I've come across. By far. Great vignettes/stories.”
—Tom Peters, co-author, In Search of Excellence

“What Nate Silver did for poker and politics, this does for everything else. A broad, well-written book easily accessible to non-nerd readers.”
—David Leinweber, author, Nerds on Wall Street

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January 11th 2016

Wise Practitioner – Predictive Analytics Interview Series: Nate Watson at Contemporary Analysis

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predictive Sales Targeting in the Energy Industry, at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Nate Nate_Watson imageWatson, President at Contemporary Analysis, a few questions about his work in predictive analytics.

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

A: We have been lucky enough to work with a number of companies in a number of verticals. This year we have been asked to predict the price of transportation for a grain trader 90 days in advance, which potential voters would most likely favor a gambling initiative on the ballot, which potential customers would be interested in a product and what message would motivate them, which current customers would be interested in a second product, and which companies are tapped out of resources in their communities and their expansion is going to require a move or at least building a division in another city (What we are presenting on at PAW). 

We have also seen an increase in the number of companies asking us to help implement the ideology of predictive analytics into their organizations. This is less of a project based relationship and more of a human resource based relationship. It has actually caused us to rethink how we might be able to interact with clients and help build their own predictive analytics capabilities. In 2016, CAN is going to offer the ability to purchase a part time data scientist and then help them manage the change management that happens when companies switch from reactionary to proactive in their decision making. It is really going to make a difference for companies going forward. They won’t have to jump into the deep end to get a data scientist. We are going to offer "toe dipping".

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

A: It fundamentally changes your culture to one of innovation and "anything is possible". Because we aren't a software vendor and instead are entrusted with the hardest problems a company has been grappling with for years, we get to hire the smartest out of the box thinkers we can find. Everything is possible and solutions are just something we have to figure out. It is something we call "less wrong". We don’t have to have a 100% correct solution for it to be correct. As long as it is an improvement over what we were doing, we think it a success. It sounds rudimentary, but in most companies this is not the case. They have to have a large jump in "correctness" for something to be valuable. Because of our mentality and our lack of fear in implementation, we have implemented models into all kinds of things we do here at CAN.

Our newest success is using predictive models to find and managing talent. We found that resumes are not good predictors of cultural success or skill sets desired. Just because a data scientist has 7 years of experience doesn't mean they actually have more modeling experience as someone with only 3 years of experience. The quality of that experience has to be measured somehow. So we built predictive models measuring everything from actual skills to culture fit and have used them to find, train, and hire our own talent. It has also allowed us to manage talent better as I now understand who to put on projects, who has which actual skill set, and which data scientist will best fit what we are trying to do with a client. It's actually something that has worked so well, we are going to deploy it as a product in 2016. 

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

A: In September, our grain trading client asked us to solve a problem called freight variance. Freight Variance is when they actually purchase the right to grain but do not take ownership of that grain until later. In the initial contract, they have to predict what it will cost them to transport their grain at time of purchase to their facility and lock in that rate. That rate can vary widely upon actual time of delivery. The predicted rate versus the actual rate is called Freight Variance. 

Our client was only using gut feel as their way of predicting price–sometimes leading to large variances and a loss of profit. CAN built a Proof of Concept model for their corn trading that used not only their data, but also weather, price of fuel, price of rubber, and a few other secret ingredients to lower their freight variance by over 35% or $3.94 Million dollars per year. This year, our goal is to improve their model, operationalize the model into their bidding process, and add their other ingredients to increase their cost savings to more than $10 Million.

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

A: When we started 9 years ago, we used to be shocked at the lack of data when companies told us they had a lot of data to analyze. Now, in 2015, we are shocked at the number of companies that have implemented Big Data or BI initiatives, have loads of data, but have little to no understanding of what to do with it. Last year, we spent a lot of time educating companies that their customer data probably contains really important and potentially money saving ideas in it. They just need to employ a data scientist (either internally or hire a consultant) to turn their data from noise to information. 

It is one of the reasons we implemented the Staff Augmentation division of our company. We saw a need beyond doing projects and being an external vendor. Companies need help just understanding what they have, if it is useful, and what they can do with it. We see the need for companies to embrace the ideology behind what predictive analytics is and does before the implementation of a project will have any real impact to the bottom line. We see the need for a lot of beginner companies to be having a data scientist really spend time in the data with no agenda. It is only then a real proof of concept has meaning and value can be devised and implemented. 

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

A: I want people to understand how valuable using predictive analytics and data science is in their organizations. All parts of business, not just sales and marketing, can be improved by being proactive instead of reactionary in their management. I hope to show people and open up their minds to other parts of their business that need to be revolutionized including many parts of operations and HR. Hopefully they can take back some ideas to their own businesses and lead other parts of their businesses to adopt the philosophy of proactive management through the implementation of predictive analytics. 

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Don't miss Nate’s conference presentation, Predictive Sales Targeting in the Energy Industry on Tuesday, April 5, 2016 at 4:15 to 5:00 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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January 6th 2016

Wise Practitioner – Predictive Analytics Interview Series: Hans Wolters at Microsoft

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting User and Device Upgrade Issues Moving to Windows as a Service, at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Hans Wolters, Principal Data Scientist, Windows and Devices Group at Hans Wolters imageMicrosoft, 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: I have worked in a diverse set of domains.  The predictions ranged from the very serious –  an impending flare for patients suffering from Lupus (SLE) to the more mundane – predicting if players are about to churn out of a social game. At Microsoft, we work on variety of problems, churn prediction is one of them, but my talk at PAW will focus on predicting the Win 10 upgrade experience

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 delivers values in many ways. It helps with planning and allocating engineering resources and feature prioritization.  It also influences supply chains.

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

A: Often it is hard to quantify this, since you cannot run a true A/B experiment once the model is in production, hence an observed lift can, strictly spoken, not be credited to your model with absolute certainty. I did however in one case observe a 20% decrease in churn by accurately flagging the users at risk for churn  and having a willing partner that implemented retention strategies.

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

A: I am not sure if it is surprising, but when reasoning about user behavior it is most powerful to look at longitudinal data and then encode changes over time in an appropriate way. The other insight I found is that what users say they do and what you actually observe them doing is most often not that correlated.

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

A: One  take-away will be that relying on an insider  population for receiving early signals works well for some aspects and can be very misleading for  others.

Q: What are the most common misconceptions people have about predictive analytics?

A: I encounter most often the perception that building predictive models involves predominantly the application of the latest and greatest ML algorithms. In reality the most effort is spent on data cleaning, imputing missing values and then the engineering of great features. Training an algorithm is the least of our worries.

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Don't miss Han’s conference presentation, Predicting User and Device Upgrade Issues Moving to Windows as a Service, on Monday, April 4, 2016 at 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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December 30th 2015

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

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Balancing Privacy with Powerful Employee Churn Predictions, we interviewed Frank Fiorille imageFrank Fiorille, Senior Director of Risk Management at Paychex, Inc. View the Q-and-A below to see how Frank Fiorille has incorporated predictive analytics into the workforce of Paychex, 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: The model being presented is deployed to senior level management at Paychex across the country to identify hotspots for turnover in the next six months.  Key locations and variables are then analyzed to provide added insight for how the data interacts with current retention strategies. 

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

A: I think predicting employee engagement would be a groundbreaking model.  The topic has such an interest today and being able to tell a business unit or employer “Your engagement is going to trend up (or down) in the next sixth months because of X.” would be a really exciting way to positively impact employees at work.

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

A: In my experience some businesses and business units are there.  There are several people I have come across that trust the science and trust the experience of our team that they would deploy the best model we selected.  When you develop that level of trust with your partners, it really gives you the freedom to stretch the limits of your skills and talents.   

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

A: My advice would be to listen first.  Understanding your internal or external customer’s needs should define your actions and goals in your model.  They will listen more readily to the complexities of your process once they are sure that you’ve heard them and are responding to their needs.  On the flip side, don’t feel you need to share every complex detail with them, either.  Sometimes sharing too much information is as bad as not sharing enough. 

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

A: We presented previously on our national retention model that is being used to proactively retain clients that are at a high risk for leaving our services.  Simply going after these clients before they entertain the decision to switch to a competitor greatly impacts your ability to influence that decision.   

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

A: I think the biggest thing, and I think it is happening already today, is when we start using data to make decisions, specifically when it contradicts our gut or instinct.  I believe we are comfortable using data when it reinforces our intuition, but will quickly discard it when the two don’t agree.  I think the key evolution is when we start trusting data over intuition, not blindly or exclusively, but at certain times when our trust is such that we can let the data prove our experience wrong. 

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

A: We do have results that we will share in the presentation.  Because of the new model deployment and the unique methodology used, the results are promising but harder to quantify. 

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Don't miss Frank’s conference presentation, Balancing Privacy with Powerful Employee Churn Predictions, at PAW Workforce, on Tuesday, April 5, 2016 from 11:15 am to 12:00 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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