February 24th 2015

Wise Practitioner – Predictive Analytics Interview Series: Bob Bress of Visible World

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, TV Audience Targeting through Bob_BressPredictive Analytics at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Bob Bress, Sr. Director, Product Management & Analytics at Visible World, a few questions about his work in predictive analytics.

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

A:  We are in the business of making television advertising inventory more valuable with applications that support the intelligent buying/selling and allocation of advertisements using advanced data and algorithms.  In doing so, we have a heavy focus on using predictive models for forecasting TV viewership patterns for specific targeted audiences.     

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

A:  We have incorporated predictive analytics algorithms in many the of our products to support decisions around maximizing advertising revenue, allocating media across a schedule, and in the automation of generating proposed media plans.

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

A:  A recent change in a TV viewership predictive model to incorporate overall trends in households watching television resulted in an 8% improvement in overall viewership forecast accuracy.

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

A:   When placing media across broad timeframes, there is some predictability to when the inventory owner will place the ads which can help in predicting overall TV viewership of advertisements.

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

A:  We can make significant improvements in how efficiently media campaigns are run on TV by improving forecasting capabilities and reacting dynamically to forecast errors.

——————–

Don't miss Bob Bress’ presentation, TV Audience Targeting through Predictive Analytics, at Predictive Analytics World San Francisco, on Wednesday, April 1, 2015 from 11:15 am to 12:00 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

February 17th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller of Constellation Research, Inc.

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

In anticipation of his upcoming Predictive Analytics World for Workforce keynote Holger_Muellerpresentation, Why the Rapidly Changing World of Analytics Matters for both HR and Business, we interviewed Holger Mueller, Principal Analyst & VP at Constellation Research, Inc. View the Q-and-A below to see how Holger Mueller has incorporated predictive analytics into the workforce of Constellation Research. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: In your work with predictive analytics, what specific areas of the workforce are you focused on?

A: I work as an industry analyst – so I help enterprises to understand the HCM and next generation application space, which includes analytics and big data. While workforce productivity is important, we have seen almost all vendors looking at ‘flight risk’ and deliver models for that scenario. The current focus is mainly on finding the right talent to fill positions both internally and externally. With the upcoming retirement and overall skills challenge, the recruiting and succession management function need all the help they can find. Another area is that both vendors and enterprises are realizing that psychographic information is key for people’s success, and are in the process of adding these capabilities to HCM decisions.

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

A: I work as an industry analyst – so neither HR or LoB. But write, speak and advise to both. Analytics give power to users in the LoB to come to their own HCM decisions – often without involving HR professionals.

Q: What workforce outcomes do your models predict?

A: As analysts we don’t have our own models, but both vendors and customers are mainly focused on recruiting at the moment.

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

A: Saving time to the business user. Once they trust their analytic software, they will go with its (recommended) decisions quickly/easily.

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

A: There are many success stories for analytics in HR. It would be wrong to put a few on the pedestal.

Q: What area of the workforce do you think has seen (or will see) the greatest advances or ROI from the use of predictive analytics?

A: No specific area – certainly a few employees have seen better paycheck and retention measures based on the first wave of analytics apps that focused on ‘flight risk’. We now see a much broader use of analytics, so it cannot be associated with a single group. In the longer term the LOB user/executive will gain as more decisions can be made at greater speed and quality, which equates in time savings. What they will do with those remains to be seen.

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

A: To hear the latest state of analytics in HCM. 

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

A: Too many ‘false’ analytics are out there that do not take action or make recommendations. Many visualizations are (wrongly) called analytics.

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

A.  Understanding what real analytics are and how best practices are evolving.

Don’t miss Holger Mueller’s conference presentation, Why the Rapidly Changing World of Analytics Matters for both HR and Business, at PAW Workforce, on Tuesday, March 31, 2015, from 1:30-2:15 pm. Click here to register for attendance.

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

 

No Comments yet »

February 11th 2015

Q & A with Peter Morville

Peter_MorvilleBy Crystal Prag of Rising Media, Inc.

Peter is a pioneer of the fields of information architecture and user experience. His best-selling books include Information Architecture for the World Wide Web, Ambient Findability, Search Patterns and Intertwingled. He advises such clients as AT&T, Cisco, Harvard, IBM, Macy's, the Library of Congress, and the National Cancer Institute.

At Text Analytics World 2015 in San Francisco, Peter’s keynote is entitled “The Architecture of Understanding.” Catch an early glimpse of Peter’s expertise by reading the interview below and don’t forget to utilize early bird pricing now to secure your spot to learn from Peter in person at the best rate currently available.

Speaker Interview: Peter Morville, Semantic Studios

In your keynote, you talk about going deep. How do you see text analytics helping in going deep?

Peter Morville: If we hope to design better products, services, and systems, we must first understand the cultures of our users (or customers) and our stakeholders (and staff). Ethnography is one path to deep cultural insights. By observing and interviewing people in their natural habitats, we can learn a great deal about their goals, values, practices, and assumptions. Text analytics promises a different, complementary path to insight. What can we learn about the cultures of users and stakeholders by identifying their linguistic patterns and trends? By marrying high-touch ethnography and high-tech analytics, we can build towards a deeper understanding of the design elements necessary to ensure a lasting, bi-cultural fit.

In what ways do you think that text analytics has actively impacted information architecture?

Peter Morville: I’ve been interested in text analytics ever since I was in library school in the early 1990s, but I haven’t had much opportunity to use text analytics within the context of my information architecture work. I’ve worked with a few clients who use software to largely automate the process of tagging or classifying content, but most of the organizations I work with still rely on manual metadata. I’m hopeful this will change in the next few years, but only if the text analytics industry does a better job of demonstrating its value within the context of user experience. Given the growing popularity of faceted search from enterprise and ecommerce to social and mobile contexts, there’s a great opportunity to apply text analytics software to the creation and management of tags and taxonomies.

Where do you see the greatest potential for the combination of IA and text analytics?

Peter Morville: As an information architect, I’m often asked to create structural designs for massive content collections (e.g., a database of millions of scientific journal articles), so that users can find what they need. Of course, text analytics can help with findability, but I’d like to identify better ways to encourage discovery too. The intelligence community has been using text analytics to surface the “unknown unknowns” for years. Isn’t it time to leverage text analytics software to integrate findability and discovery into a wider range of applications?

What do you see as the most exciting potential for the field of IA?

Peter Morville: There are many ways to understand or define information architecture. The “polar bear book” (which we wrote in 1998) focused on organization and navigation for websites. Since then, our field has evolved to support mobile, social, and cross-channel user experiences. We are involved in planning and placemaking for ecosystems that span physical and digital contexts. This work requires that we serve as change agents and mapmakers. We help people to see differently by making the invisible visible. In today’s complex, fast-changing world, we have both an opportunity and a responsibility to serve as architects of understanding. That’s what I find most existing about the potential of the field of IA.

SNEAK PREVIEW: Please tell us a take-away that you will provide during your keynote presentation at Text Analytics World.

Peter Morville: Last September, I went hiking in the Grand Canyon, rim to rim, in a day. So, I’m walking along, thinking deep thoughts about the two thousand million year old rocks around me, when I hear a rattle. And, if you’d like to know how the story ends, and how it’s connected to everything from code to culture (and text analytics too), you’ll have to come to the keynote.

*****

Uncover what happened with Peter, the Grand Canyon, and text analytics by signing up to attend Text Analytics World. Sign up to attend by February 6th to enjoy early bird pricing.

 

No Comments yet »

February 10th 2015

How Predictive Analytics Reinvents These Six Industries

By: Eric Siegel, Ph.D., Founder, Predictive Analytics World
Originally published in Information Management

Predictive analytics is a game-changer — it's like "Moneyball" for… money. This article summarizes and links resources with late-breaking coverage of how predictive analytics reinvents six industries.

I'm going to break it to you gently. Despite all the advanced technology lining your pocket, car, home, workplace–and even the proverbial cloud floating virtually above your head–the world is a remarkably inefficient, wasteful place. The organizations that make the world go 'round, the companies, agencies, and hospitals that treat and serve us in every which way, constantly get it wrong. Marketing casts a wide net; junk mail is marketing money wasted and trees felled to print unread brochures. Institutions are blindsided by risky debtors and policyholders. Fraud goes undetected. And, critically, healthcare could use all the prognostication it can get. These are heavy costs that tax both you and I in various ways every day.

If only there were some way to run things better, to improve the effectiveness of the frontline operations that define a functional society.

Upgrading the World

Predictive analytics serves that very purpose by driving mass-scale processes empirically, guiding them with predictions generated from data. Millions of predictions a day improve decisions as to whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, and medicate.

In this way, predictive analytics reinvents how our world's primary functions are executed, across sectors. It boasts an intrinsic universality: A great, wide range of organizational activities can be improved with prediction–specifically, by way of predicting the behaviors and outcomes of people, the future of individual customers, debtors, patients, criminal suspects, employees, and voters. It's that generality that makes this technology so potent and ubiquitous.

Market Growth

So it comes as no surprise that predictive analytics is booming:

  • Number one on LinkedIn's "25 Hottest Skills That Got People Hired in 2014" is "statistical analysis and data mining," and number six is business intelligence. While most of the other skills listed there are forms of engineering/development (programming, etc.), the meat of the matter—the stuff of business—is what data itself tells us, rather than the infrastructures built to collect and store data.
  • Research firms project the predictive analytics market to reach $5.2 to $6.5 billion by 2018/2019 (MarketsandMarkets and Transparency Market Research).

Reinventing Industries

Prediction makes our planet rotate a bit more smoothly. Let's look at examples of this effect within six industries: Marketing, financial services, workforce management, healthcare, manufacturing, and government.

As the table of resources below reveals, a great deal of movement deploying predictive analytics is taking place within each of these industries, as enacted by various companies for various purposes—each case executed by way of predicting an outcome or behavior (e.g., click, buy, quit your job, default on a loan, or die), and using those predictions to drive operational/treatment decisions (e.g., remarket to, call, give a raise to, decline credit to, or apply a medical procedure on). Follow the links within this table to check out in detail the areas that interest you most.

Articles, videos, and events with late-breaking coverage of predictive analytics' deployment across six industries:

INDUSTRY: ARTICLES: VIDEOS ON DEMAND: EVENTS IN 2015:
Marketing predictive remarketing PAW Business Oct 14 *PAW Business (5 events)
Financial svcs
Credit risk
Insurance
Paychex, Chase
insurance study
PAW Business Oct 14
one on insurance
PAW Business (5 events)
(5 sessions on insurance)
Workforce
mgmt
Walmart
Wells Fargo
via Facebook data
talk: Talent Analytics CEO
case: call center
PAW Workforce (March)
Healthcare predictive medicine
why predict death
New book, Miner et al
PAW Healthcare 2014 PAW Healthcare (Sept)
PAW SF – March:
substance abuse
Chicago Dept Pub Health
intro training workshop
Manufacturing 4 predictive apps
big data improves mfg
predict mfg equip fail
car telematics for….
analytics in mfg PAW Manufacturing(June)
Government gov't apps—overview
IRS fraud detection
city of Chicago
disaster response
Siegel keynote (IBM) PAW Government (Oct)

 

*PAW stands for Predictive Analytics World (vendor-neutral conference series). In response to market growth, PAW has expanded to 9 annual events and has launched specialized events that focus on the specific industries listed above.

There's More: Innovative Predictive Applications

It does not stop there. Check out these other examples from the ever-widening range of industrial uses.

Recent articles covering innovative predictive applications:

… and as things warm up for the 2016 presidential election, speculation on the use of predictive analytics will emerge, given the way in which Obama for America 2012 used predictive analytics to target campaign activities.

Conclusions–The Predictive Game-Changer

As I put it to a relative over the holidays, predictive analytics is a game-changer. It's like Moneyball for… money.

As predictive analytics' adoption widens and deepens across sectors and across organizational functions, an inter-industry synergy emerges. Stories are shared between sectors–the lessons learned and proof-of-concepts viewed from neighboring industries inspire and catalyze growth. There's a cyclic effect.

And that is what the "big" in big data really means–big excitement and big impact across industries.


Some Extra Bits

Resources with which to explore advanced and emerging methods:

PA Times ImageGetting-started resources for newcomers:
The Predictive Analytics Times Executive Breakfast
The Predictive Analytics Guide
Infographic: Predictive Analytics World by the numbers

 

Eric Siegel, Ph.D. is the founder of Predictive Analytics World coming in 2015 to San Francisco, Chicago, Boston, Washington D.C., London, and Berlin the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, and executive editor of the Predictive Analytics Times. For more information about predictive analytics, see the Predictive Analytics Guide.

 

No Comments yet »

February 3rd 2015

Wise Practitioner – Predictive Analytics Interview Series: Richard Boire of Boire Filler Group

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting Extreme Behavior to Richard_Boireimprove the rating structure for Travel Insurance, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Richard Boire, Founding Partner of Boire Filler Group a few questions about his work in predictive analytics.

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

A: Before even building a predictive analytics solution, we work with the business stakeholders to identify the exact business problem or challenge.

Once this has been identified, we then determine the behaviors that need to be optimized or minimized to solve the challenge/problem.

Our experience and breadth of work has resulted in the following type of model being built:

  • Acquisition
  • Upsell
  • Cross-sell
  • Attrition
  • Fraud
  • Credit risk
  • Claim risk

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

A: In building marketing response type models, the ROI is significantly improved as lower costs are achieved to attain the same level of revenue that would otherwise be attained without modelling.

Marketing decisions can then be made on which customers to select based on their ROI.

In building automobile claim risk predictive analytics solutions, we are predicting the loss cost of a given vehicle. By integrating this information with premium, we can then determine which vehicles are overpriced vs. those which are underpriced.

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

A: Listed below is the case of a property risk model that we built for an insurance company.

The green line represents our predicted analytics solutions while the red line represents the current premium pricing practice of the insurance company. The company’s current pricing practices are delivering value as seen by observing the area under the curve between the red line and the straight line. Yet, predictive analytics solutions built by our company are delivering even further value as depicted by the area under the curve between the green line and the redline.

graph R Boire

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

A:  In some cases, we have been able to use the data to identify a major change in someone’s life. A good example of this is identifying an individual that has just moved by observing that the address data has now changed for that individual. The ability to identify movers vs. non movers has led marketers to develop unique communication strategies towards this segment.

Another good example is tenure. We often find that customer behavior is often U-shaped with this variable which implies that newer and longer-tenured customers tend to be loyal, while the middle group or more average tenure type customers tend to be less loyal.

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

A: In building predictive risk solutions to estimate the loss amount for this travel insurance company, we encounter huge outliers that often distort or mitigate the overall impact of the model. In this particular case study, we examine an approach that allowed us to in effect produce a better model thereby reducing the impact of outlier events. The key takeaway was that we used a very pragmatic approach in solving the problem. This pragmatism enabled us to combine our domain knowledge of the business along with our data science knowledge which allowed us to arrive at a better overall solution.

Q: What has been the biggest challenge in building models over the course of your career?

A: The biggest challenge in creating predictive analytics solutions is ensuring that the target variable is created correctly. This is easier said than done as the practitioner has to do the following:

  • Identify information to be used in creating target variable
  • Creation of analytical file with pre-period where all information represents potential model variable inputs to model and post period where the only information is the actual target variable.
  • Many problems arise when information in the pre-period represents a portion of the target variable or the pre-period overlaps with the post period. These kinds of seemingly simplistic data issues represent the lion’s share of problems when it comes to overstatement of model results.

————–

Don’t miss Richard Boire’s presentation, Predicting Extreme Behavior to improve the rating structure for Travel Insurance, at Predictive Analytics World San Francisco, on April 1, 2015 from 4:40-5:00 pm. Click here to register for attendance.

Richard Boire will also be providing his new book, Data Mining for Managers: How to use Data (Big and Small) to Solve Business Challenges, to those who attend his session at PAW San Francisco. His book provides streamlined insights chock-full of engaging stories, case studies, and techniques for making the most of the masses of information and mining techniques that technology has enabled.

Enjoy his session and his book by attending PAW San Francisco.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

January 29th 2015

Wise Practitioner – Predictive Analytics Interview Series: Sarah Holder of Duke Energy

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, It’s Not a Black Box! Explaining Sarah_HolderPredictive Models to the Masses, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Sarah Holder, Senior Market Research Analyst at Duke Energy a few questions about her work in predictive analytics.

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

A:  Besides being a utility, Duke Energy also offers Warranty and Energy Efficiency Programs.  In the Marketing Analytics Group, we currently use Predictive Analytics to target Direct Marketing offers through Direct Mail, Email, and our Call Center.

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

A:  Predictive Models drive our “Smart Window.”  This Window pops up when a Call Center Representative pulls a Customer’s information.  A customer may be calling in to star service at an account, or to inquire about a change in their bill amount.  In the Smart Window, there is a list of top products for which the customer qualifies and star ratings to signify the probability of the customer’s interest.  Because the representatives have information on the customer, it helps them make a knowledgeable sales pitch.

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

A:  Duke Energy previously used external vendors for Targeting Direct Mail lists.  Once we moved the process to our internal Analytics group and used Logistic Regression to predict the best customers, our Load Control program’s direct mail response rates increased by 261%!

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

A:  Contrary to a previous belief, we found that households with two occupants tend to use more energy as a whole when compared to households with 3 or more occupants.  The number of people in the household may be a clue towards the life stage of the occupants.  When there are children present, the home may not be occupied as frequently during the day, leading to lower energy use overall.

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

A:  I will briefly explain the success story of predictive modeling within our marketing department.  I will also review some tips for communicating the benefits of predictive models verses purchased segmentation systems for targeting customer direct marketing lists.

————

Don’t miss Sarah Holder’s conference presentation, It’s Not a Black Box! Explaining Predictive Models to the Masses, at Predictive Analytics World San Francisco, on March 31, 2015, from 3:55-4:15 pm. Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

January 20th 2015

Wise Practitioner – Predictive Analytics Interview Series: Mohamad Khatib of Nielsen

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Pizza Analytics & Optimization, at Mohamad_KhatibPredictive Analytics World San Francisco, March 29-April 2, 2015, we asked Mohamad Khatib, Sr. Project Manager at Nielsen, a few questions about his work in predictive analytics.

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

A: The work I have done focused on predicting customer purchases, in response to targeted advertisements and marketing promotional offerings. The aim is to help manufacturers optimize their product marketing promotional spend by predicting customer responses to different promotions.

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

A: By utilizing predictive analytics, the small business we worked with (pizza shop: manufacturer and retailer) were able to better schedule promotional campaigns that will yield the best results, and attract targeted customers.

Having informed predictions enables these pizza outlets to optimize their inventories of promoted products. In addition, they guide production staffing plans so that pizza outlet managers can schedule resources appropriately to meet the changing demands. This will help to ensure effective delivery of these products to clients.

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

A: By applying predictive analytics, the engaged pizza shops were able to effectively plan for their staffing needs, and align that with targeted promotions carried out to clients.

As predicted, one pizza shop was prepared to respond to increased demand in response to the post card promotions. As responses exceeded initial projected rates in some cases, additional staffing plans were in place to meet the increased demands and carry out the required service levels.

Also, they were able to compare effectiveness of types of promotional campaigns as applied to their targeted demographics.

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

A: Even though we utilized simplified models to provide predictive analytics, the correlation between promotional activities and client responses was easily visible and measurable. This ease of quantification of results in the pizza business produces great benefits in directing promotional activities to maximize returns on spends.

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

A: A simplified predictive analytics model can be easily developed and applied to a small business. Such limited efforts and investment will bring positive returns to manufacturers.

This simplification does not trivialize the approach, and has proven to produce valuable results and insights to predict responses to targeted pizza promotions.

In addition, it is clear that successful deployment of predictive analytics will have cross-functional impact throughout the organization. This impact applies to large as well as small organizations. Respective business processes will be impacted accordingly.

—————

Don’t miss Mohamad Khatib’s conference presentation, Pizza Analytics & Optimization, at Predictive Analytics World San Francisco, on Wednesday, April 1, 2015 from 10:25-10:45 am. Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

January 16th 2015

Wise Practitioner – Predictive Analytics Interview Series: Dominic Fortin of TD Insurance

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, A Success Story: Sales and Revenue Forecasting through Predictive Analytics, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Dominic Fortin of TD Insurance a few questions about his work in predictive analytics.

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

A:  The models are predicting sales, cancellations, renewals for general insurance.  Units and premiums are forecasted, as well.  Twenty-three variables are predicted in 92 variances (region, product, sales channel, insurer, etc…), each level with its own specificities, creating 2,116 different models.  The models encompass all the changes to the business (rate change, new project, marketing investments). The innovation was that we were able to create generalized models without having to create 2,116 models independently.

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

A:  It allows us to not only to do our sales & revenue budget, to forecast our upcoming results but also to do scenarios on initiatives (e.g. effect of a rate change, etc…).  This capability helps the company to make sound decisions on business initiatives.

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

A:  We had models in the past based on excel, they were doing "the job" on predicting our sales & revenue.  The new models add more flexibility and rapidity and lower the risks associated with a manual excel based forecast.

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

A:  There was no real surprise.  We mastered our data well, it is more the capability to forecast at a more granular level that allows us to be more precise rather than trying to predict at high level.

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

A:  A take-away is the assistance to think about how this development method could be used to develop a large number of models for your business domain.

Don't miss Dominic Fortin’s conference presentation, A Success Story: Sales and Revenue Forecasting through Predictive Analytics, at Predictive Analytics World San Francisco, on April 1, 2015 at 3:55-4:15 pm. Click here to register for attendance.

 

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

January 6th 2015

Wise Practitioner – Predictive Analytics Interview Series: Pasha Roberts at Talent Analytics

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

 

In anticipation of his upcoming conference presentation, A Transaction-Based Approach to Understand Sales Representative Growth, Performance, and Gaming, at Predictive Analytics World for Workforce, we interviewed Pasha Roberts, Co-Founder and Chief Scientist at Talent Analytics Corporation. View the Q-and-A below to see how Pasha has incorporated predictive analytics into the workforce of Talent Analytics Corporation. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: In your work with predictive analytics, what specific areas of the workforce are you focused on?

A: We work with clients to solve workforce problems, using a data science approach to predict the ways that an employee will behave and perform. Only rarely are the problems neatly defined, but it usually comes down to estimating tenure or performance factors. We develop and deploy these models in the cloud to inform candidate selection and internal operations.

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

A: Occasionally we find an HR department that is in sync with the vast potential of predictive analytics. These people are wonderful, and are earning their place at the future of their business; several are speaking at PAW for Workforce this year. Most HR departments have not crossed this chasm; they feel that their scope is more limited.

The line-of-business tends to actually feel the actual pain, and is typically more facile with data. We often work with the Sales, Service, or Call Center Lines of Business. These operations managers are on the front line and are accountable.

Q: What workforce outcomes do your models predict?

A: We create models that predict tangible outcomes – such as dollar sales, or calls per day, or hours worked, or error rates, or tenure/survival. The business leaders decide what is important to success in a specific role, and then we build models for those factors. For example, we may model the likelihood of a job candidate to achieve a top sales level within 6 months.

I am not a big believer in working with intermediate variables, such as engagement or job satisfaction. You can’t eat engagement. People may assume that engagement drives business performance, but that link needs to be proven case by case. At that point, you might as well predict the business performance directly.

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

A: We recently deployed a model to reduce bank teller attrition at a large bank. The model predicts the Cox proportional hazard for survival in the role, based on our aptitude tool metrics.

Advisor, our deployment platform, displays the probability of a job candidate to be on the job in one year. Recruiters use this number (actually cutoff levels of this number) to move candidates forward, or not, during hiring.

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

A: The AUC of the attrition model above is quite good, well over 0.70, with lift over 2.0 in the important regions.

The business benefit of this is two-fold – (1) fewer bad fits, which means less wasted training due to early attrition, and (2) more good fits, which means longer employee runs and higher lifetime value. The total benefit of this incremental change will be over half a million dollars.

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

A: Cluster analysis of a recent sales assignment revealed a clear group of sales reps who were succeeding largely by cheating. They were “poaching” sales from regions assigned to other reps, pulling low-hanging fruit away from others. Reps in this cluster barely worked any hours, but had very high performance numbers.

It just goes to show that you can’t use just one KPI. And yes, we had great lift in predicting job candidates who were likely to fall into this poaching cluster.

Q: What area of the workforce do you think has seen (or will see) the greatest advances or ROI from the use of predictive analytics?

A: The best predictive analytics can be done with largest sample sizes and quantified, quality output variables. We see the most of this in high volume roles, such as call centers, sales, retail banking, and insurance.

The costs and benefits of attrition/under-performance drive the ROI. In high volume situations, even a small increment can make millions of dollars of difference.

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

A: We need business leaders who understand what predictive analytics is, what it can do, and how to leverage the methods. It requires thinking about the world in a slightly different way, in terms of probability. This is not as hard as it may sound, and it is the key to unlocking a whole new level of corporate quality and performance.

Analytics practitioners should come to PAW for Workforce to learn – not only methods and technology, but to the sometimes-harder problem of business application and deployment. It’s the difference between solving equations and solving word problems.

Q: Do you feel any urgency you want to pass along to your fellow HR and Business Executives to implement predictive analytics to help solve employee challenges? Why?

A: I would like practitioners to realize the fact that there is a rare opportunity in employee analytics. Analytics can be so much more effective in hiring, because we directly choose employees, therefore predictions can directly drive results. This is a luxury that most other forms of analytics do not have – we don’t choose our customers, for example, so our ability to drive marketing results is indirect at best.

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

A: We predict patterns that tend to happen over time. We don’t predict what will happen in each specific case at each moment.

For example, if we predict that an employee has a 36% chance of staying on the job for one year; it’s still possible that they will last on the job for years. This doesn’t mean we’re wrong, because other employees with the same pattern may compensate.

As a machine learning geek, I love being “wrong” in this way, and love it more when managers disobey the models, because these variances only make new models stronger on the next iteration.

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

A: So far, very involved. The impact of our work boils down to understanding risk and tradeoffs, which is something senior managers often understand better than line managers.

You just have to speak their language, instead of going on about your latest foray into conditional random forest algorithms or how many Hadoop nodes were used.

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

A: I am working with millions of geo-located sales transactions to discover and predict patterns in how they learn to sell. It is a fascinating dataset that chronicles thousands of reps as they sink or swim, including some who cheat their way to the top. I hope to deliver insights into how employees learn, as well as techniques to analyze large unaggregated data sets.

 

Don't miss Pasha Roberts’ conference presentation, A Transaction-Based Approach to Understand Sales Representative Growth, Performance, and Gaming, at PAW Workforce, on Wednesday, April 1, 2015, from 11:15 am – 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.

 

No Comments yet »

December 31st 2014

Wise Practitioner – Predictive Analytics Interview Series: Bryan Guenther of RightShip

By: Eric Siegel, Founder, Predictive Analytics World

 

In anticipation of his upcoming conference presentation,

The Impact of Predictive Analytics on Maritime Safety and Efficiency, at Predictive Analytics World San Francisco, March 29-April 2, 2015, we asked Bryan Guenther, Qi Program Manager at RightShip, a few questions about his work in predictive analytics.

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

A: In essence our models predict the likelihood of an incident at sea. We are also developing other models to predict specific kinds of shipping accidents, e.g., accidents that cause pollution, ships running aground, etc.; as well as models that drive efficiency – e.g., in loading/unloading at port.

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

A: As we work in the services industry, our customers – who use our models for their vessel selection – are the ones that ultimately get the value from the models (which they then pay us for).

Predictive analytics provides us with the ability to identify vessels that may have an incident, and therefore remove them from our customers’ supply chain – so indirectly we are reducing the likelihood of a vessel having an accident. As such it’s not just about money: the value our analytics provides is about limiting risk, saving lives, and reducing environmental damage.

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

A: Each time our customer avoids an unsafe vessel it’s a ROI, and a success, so the prediction actually does this. In 2013 alone we removed over 950 vessels from customer supply chains – so we see that as potentially 950+ incidents that were avoided.

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

A: Analytics showed us the complexity of the relationships between various factors. If you take age as an example, we previously had assumed this to be a fairly linear factor that operated independently; however predictive analytics showed us this is not the case. We discovered that the way age affects a vessel is dependent on a lot of other factors such as tonnage, past casualty history, owner, manager, parent, flag, class, crew, etc.  There are many complex relationships and interactions.

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

A: I’ll provide some practical examples of how predictive modelling has made a real difference. Our next predictive model may save someone’s life, keep oil out of the water & birds in the air! I must say that although our prescriptive model has done a fantastic job so far in reducing casualties; predictive modelling will take us to the next level. This transition is being driven through technology – we can’t move on and improve with old technology.

Don't miss Bryan Guenther’s conference presentation, The Impact of Predictive Analytics on Maritime Safety and Efficiency, at Predictive Analytics World San Francisco, on Tuesday, March 31, 2014 from 11:45 am-12:05 pm.  Click here to register for attendance.

By: Eric Siegel, Founder, Predictive Analytics World

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating. He is the author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. 

No Comments yet »

Next »

  • Subscribe Via Email

    Get a daily digest of new posts delivered to your inbox:

  • Predictive Analytics Book: The Power to Predict Who Will Click, Buy, Lie, or Die
    BOOK AWARD:
  • Recent Posts