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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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, 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:

RELATED ARTICLES:

 

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

More endorsements

Buy the book:

Amazon  Barnes & Noble  Audible

BAM  800ceoread  iTunes  

Free Audiobook graphic
www.thepredictionbook.com

  

   

 

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