Predictive Analytics Times
Predictive Analytics Times
EXCLUSIVE HIGHLIGHTS
Wise Practitioner – Predictive Analytics Interview Series: Stephen Morse, Advisor at Neudata
 In anticipation of his upcoming conference presentation, Leveraging Alternative...
Wise Practitioner – Predictive Analytics Interview Series: Dongyang Fu and Wen Shi at Concord Advice
 In anticipation of their upcoming conference presentation, Improving Credit...
Wise Practitioner – Predictive Analytics Interview Series: Ron Cowan at Snowforce Data
 In anticipation of his upcoming conference presentation, Using Mileage...
The Harvard Business Review Video Interview: Eric Siegel on Predictive Analytics
 Originally published by Harvard Business Review Prediction is reinventing...
New-Age Machine Learning Algorithms in Retail Lending
 Originally published in KDNuggets.com          ...
Machine Learning Tip: Nested Cross Validation – When (Simple) Cross Validation Isn’t Enough
 Several scientific disciplines have been rocked by a crisis...
Wise Practitioner – Predictive Analytics Interview Series: Anna Kondic at Merck
 In anticipation of her upcoming conference presentation at Predictive...
Automation and Its Impact on Predictive Analytics – The Increasing Importance of the Hybrid-Part 3
 In my last article, I discussed the increasing impact...
Ten Things Everyone Should Know About Machine Learning
 This article originally appeared as an answer on Quora....
Wise Practitioner – Predictive Analytics Interview Series: Lukas Vermeer at Booking.com
 In anticipation of his upcoming conference presentation, Data Alchemy...
15 Steps for Selecting a Talent Assessment Solution that Predicts Business Performance
 Over the past 30+ years, businesses have spent billions...
Wise Practitioner – Predictive Analytics Interview Series: Feras Batarseh at George Mason University – George Washington University
 In anticipation of his upcoming conference presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Anasse Bari, New York University
  Wall Street and the New Data Paradigm In...
Improved Customer Marketing with Multiple Models
 Data miners employ a variety of techniques to develop...
Data Science: Screening by Religion a Blunt Instrument for Security
 This commentary first appeared in the San Francisco Chronicle....
Wise Practitioner – Predictive Analytics Interview Series: Steve Weiss, at LinkedIn
In anticipation of his upcoming conference presentation, The Sprint...
Wise Practitioner – Predictive Analytics Interview Series: Emilie Lavoie-Charland at The Co-operators
 In anticipation of her upcoming conference presentation, Which Predictive...
Doppelganger Discovery: How Baseball Sabermetrics Inspires Predictive Analytics
 This author will present at Predictive Analytics World, Oct 29 –...
Predicting Fraud: Another Not So Easy Task
 As I have stated in previous articles, the most...
Are You Practicing “Bad Data Science” with your Pre-Hire Talent Assessments?
 Talent Analytics uses data gathered from our own proprietary...
Wise Practitioner – Predictive Analytics Interview Series: Leslie Barrett at Bloomberg L.P.
 In anticipation of her upcoming conference presentation, Crowd-Sourcing and...
Why Data Science Argues against a Muslim Ban
 From the perspective of data science, a Muslim ban...
Wise Practitioner – Predictive Analytics Interview Series: Andrew Burt at Immuta
 In anticipation of his upcoming conference presentation, Regulating Opacity:...
Wise Practitioner – Predictive Analytics Interview Series: Feyzi Bagirov at Becker College
 In anticipation of his upcoming conference presentation, Acquisition Funnel...
Wise Practitioner – Predictive Analytics Interview Series: Jack Levis at UPS
 In anticipation of his upcoming keynote conference presentation, UPS’...
Employee Life Time Value and Cost Modeling – Part 3
 Employee Tenure in a “Survival Analytics” Framework With a...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Richard Semmes at Siemens PLM
 In anticipation of his upcoming Predictive Analytics World Manufacturing Chicago,...
Wise Practitioner – Predictive Analytics Interview Series: Edward Shihadeh at Auspice Analytics, LLC
 In anticipation of his upcoming conference presentation, How to...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Emily Pelosi at CenturyLink
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Holly Lyke-Ho-Gland and Michael Sims at APQC
 In anticipation of their upcoming conference co-presentation, Change Management...
Automation and its impact on Predictive Analytics-Creating the Analytical File
 In my last article, I discussed the increasing impact...
Wise Practitioner – Predictive Analytics Interview Series: Natasha Balac at Data Insight Discovery, Inc.
 In anticipation of her upcoming conference co-presentation, Identifying Unique...
Wise Practitioner – Predictive Analytics Interview Series: Bryan Bennett at Northwestern University
 In anticipation of his upcoming conference presentation, Cross-Enterprise Deployment: ...
Wise Practitioner – Predictive Analytics Interview Series: David Talby at Atigeo
 In anticipation of his upcoming conference presentation, Semantic Natural...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive Analytics World for...
Book Review: Weapons of Math Destruction by Cathy O’Neil
 Originally published in Analytics Magazine Book: Weapons of Math...
Wise Practitioner – Predictive Analytics Interview Series: Angel Evan at Angel Evan, Inc.
In anticipation of his upcoming conference co-presentation, Identifying Unique...
Wise Practitioner – Predictive Analytics Interview Series: Paul Speaker at The Dow Chemical Company
 In anticipation of his upcoming conference presentation, Creating an...
Wise Practitioner – Predictive Analytics Interview Series: George Iordanescu at Microsoft
 In anticipation of his upcoming conference presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Afsheen Alam at Allstate Insurance
 In anticipation of her upcoming conference presentation, Our Success...
Wise Practitioner – Predictive Analytics Interview Series: Jennifer Bertero at CA Technologies
 In anticipation of her upcoming conference presentation, Redefining Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Michael Dessauer at The Dow Chemical Company
 In anticipation of his upcoming conference presentation, Listening Down...
Wise Practitioner – Predictive Analytics Interview Series: Steven Ulinski at Health Care Service Corporation
 In anticipation of his upcoming conference presentation, Challenges of...
Wise Practitioner – Predictive Analytics Interview Series: Lauren Haynes at The University of Chicago
 In anticipation of her upcoming conference presentation, Data Science...
Wise Practitioner – Predictive Analytics Interview Series: Daqing Zhao at Macy’s
 In anticipation of his upcoming conference presentation, Macy’s Advanced...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference presentation, Combining Inferential...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kevin Zhan at The Advisory Board
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Halim Abbas at Cognoa
 In anticipation of his upcoming conference presentation, Early Screening...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Taylor at HireVue
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Employee Life Time Value and Cost Modeling
 Understanding the Most Expensive Asset Practically every business shares...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Andrew Marritt at OrganizationView GmbH
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Interview with Eric Siegel: Popularizing Predictive Analytics with Song and Dance
  Originally published in l’ADN (in French) Hilarious consultant,...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Sue Lam at Shell
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Case Study: Hotel Occupancy Forecasting’s Big Payoff
 This Predictive Analytics story started with a question as...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Mike Rosenbaum at Arena
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Darryl Humphrey at Alberta Blue Cross
 In anticipation of his upcoming conference presentation, Claim Pattern...
The Evolving State of Retail Analytics in CRM
 The Traditional State The world of retail has undergone...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Feyzi Bagirov at 592 LLC and Harrisburg University of Science and Technology
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Craig Soules at Natero
 In anticipation of his upcoming conference presentation, Using Predictive...
Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall
  In this excerpt from the updated edition of...
Wise Practitioner – Predictive Analytics Interview Series: Ashish Bansal and John Schlerf from Capital One
 In anticipation of their upcoming conference co-presentation, The Quest...
Wise Practitioner – Predictive Analytics Interview Series: Kristina Pototska at TriggMine
 In anticipation of her upcoming conference presentation, 7 Examples...
Wise Practitioner – Predictive Analytics Interview Series: Frédérick Guillot at The Co-operators General Insurance Company
 In anticipation of his upcoming conference presentation, Defining Optimal...
Predictive Analytics vs. Prescriptive Analytics
 We have all heard and seen the diagrams that...
Interview with Prof. Dr. Wil van der Aalst, Eindhoven University of Technology
 Exclusive interview with Prof. Wil van der Aalst who...
Data Story Telling: Bringing Life to Your Data
 There is no doubt that a successful Data Scientist...
Contextual Experience Innovation
  [Title Image Abbreviations: CRM – Customer Relationship Management,...
Are Random Variables a Fact of Life in Predictive Models?
 In some of the more recent literature, discussion has...
Managing Shifting Priorities in Exploratory Data Science Projects
 After working with a client’s data for over three...
Breaking into Analytics: 5 “Musts” for your Career Transition
 In our data-rich society, corporations of all types and...
How Predictive Analytics Can Fuel Innovation for Manufacturing
 Industry leaders like to use the term “culture” to...
Rexer Analytics Data Science Survey – Highlights (New)
  White Paper with 2015 survey results available now....
How Can Predictive Analytics Help Your Bank or Fintech Company?
 Predictive analytics encompasses a powerful set of methods that...
The Role of Feature Engineering in a Machine Learning World
 Artificial Intelligence(AI) continues to be the next great topic...
The Expansive Deployment of Predictive Analytics: 22 Examples
  The future is the ultimate unknown. It’s everything...
Nine Bizarre and Surprising Predictive Insights from Data Science
  Data is the world’s most potent, flourishing unnatural...
The Trick to Predictive Analytics: How to Bridge the Quant/Business Culture Gap
  This article is excerpted from Eric Siegel’s foreword...
Wise Practitioner – Predictive Analytics Interview Series: Robin Thottungal at U.S. Environmental Protection Agency
 In anticipation of his upcoming conference keynote presentation, 21st...
How Hillary for America Is (Almost Certainly) Using Uplift Modeling
  In this article, I provide evidence that Hillary...
Wise Practitioner – Predictive Analytics Interview Series: Miguel Castillo at U.S. Commodity Futures Trading Commission
 In anticipation of his upcoming conference co-presentation, Words that...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions
 In anticipation of his upcoming keynote co-presentation, Picking the...
Exploring the Toolkits of Predictive Analytics Practitioners — Part 2
 Continuing on our discussion from last month on toolkits...
The Danger of Playing It Safe
 Research shows that people tend to be overly risk...
Manufacturing Operations: Machine Learning to Separate Actionable Trends from False Alarms
 Predictive analytics is increasingly becoming the object of value...
Predictive Analytics Basics: Six Introductory Terms and The Five Effects
 Here are six key definitions—and The Five Effects of...
Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management
 In anticipation of his upcoming keynote co-presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming conference presentation, Risk Management...
The Real Reason the NSA Wants Your Data: Predictive Law Enforcement
 The NSA can leverage bulk data collection with predictive...
Wise Practitioner – Predictive Analytics Interview Series: Scott Zoldi at FICO
 In anticipation of his upcoming conference keynote presentation, Fraud...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Klein at Miles & More GMbH
 In anticipation of his upcoming conference co-presentation, Using Predictive...
Book Review: Predictive Analytics for Newcomers and Nontechnical Readers
 The book reviewed in the article, Predictive Analytics: The...
Wise Practitioner – Predictive Analytics Interview Series: Meina Zhou at Bitly
 In anticipation of her upcoming conference presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Shantanu Agrawal at Centers for Medicare & Medicaid Services
 In anticipation of his upcoming conference keynote presentation, Implementing...
Infographic – Discover Predictive Analytics World for Business 2016
 Predictive Analytics World continues to grow – take a...
Exploring the Tool kits of Predictive Analytics Practitioners — Part 1
 Tools, tools, and more tools continue to explode in...
The Power of Data Science for Predictive Maintenance is Only Just Being Tapped
 Future of Automotive Servicing and Preventive Maintenance Several months...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference presentation, Best Practices...
Need a Data Scientist? Try Building a ‘DataScienceStein’
 Organizations are finding that hiring qualified Data Scientists is...
Wise Practitioner – Predictive Analytics Interview Series: Sanjay Gupta at PNC Bank
 In anticipation of his upcoming conference co-presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Brian Reich, Former Director at The Hive
 In anticipation of his upcoming conference presentation, The Data...
AnalyticOps: A New Organizational Role So Your Company Can Monetize Analytics
 There is no doubt that data science–and predictive analytics–...
Wise Practitioner – Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming conference presentation, Predicting Behavior...
Getting Started with Predictive Analytics – an Interview with Eric Siegel
 Data science and predictive analytics are top of mind...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Sarmila Basu at Microsoft Corporation
 In anticipation of her upcoming conference presentation, Predictive &...
Are Pre-hire Talent Assessments Part of a Predictive Talent Acquisition Strategy?
  Over the past 30+ years, businesses have spent...
Wise Practitioner – Predictive Analytics Interview Series: Dae Park and Vijay D’Souza at Government Accountability Office (GAO)
 In anticipation of their upcoming conference co-presentation, Characteristics for...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of SmarterHQ
 In anticipation of his upcoming conference presentation, The Revolution...
Opportunities and Challenges: Predictive Analytics for IoT
 There is a clear sense in the marketplace today...
Feature Engineering Within the Predictive Analytics Process — Part Two
 In the last article, I discussed the concept of...
HBO Teaches You How to Avoid Bad Science
 Do you know what p-hacking is? John Oliver –...
Jim Sterne’s Book Review of “Predictive Analytics” by Eric Siegel
 Book review originally published in the journal Applied Marketing...
The Big Picture: Today’s Data Analytics Stack
 Enterprises are inundated with data from social, mobile, IoT...
Taking Action on Technical Success: A Fable of Data Science and Consequences
 Note: This story is fiction, but it is based...
Analytics is (often) a Faith-Based Business
 If you follow data science topics in various social...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Chris Labbe at Seagate Technology
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation
 In anticipation of his upcoming conference co-presentation, Personalized Medicine...
Wise Practitioner – Text Analytics Interview Series: Michael Dessauer and Justin Kauhl at The Dow Chemical Company
 In anticipation of their upcoming conference co-presentation, Understanding our...
Women in Data Science
 The field of Data Science is booming, yet comparatively...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Boosting Performance of Machine Learning Models
  People often get stuck when they are asked...
Wise Practitioner – Predictive Analytics Interview Series: Tanay Chowdhury at Zurich North America
 In anticipation of his upcoming conference presentation, Deep Learning...
Feature Engineering within the Predictive Analytics Process — Part One
 What is Feature Engineering One of the growing discussions...
The Executive’s Guide to Employee Attrition
 Much has been written about customer churn – predicting...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference presentation, Data Driven...
Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories
 In anticipation of their upcoming conference co-presentation, Enhancing search...
Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology
 In anticipation of his upcoming conference presentation, Tips and...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference presentation, Using Predictive...
Ghosts in the Data, Constructing Data Entities
 Data Entities are seldom discussed concepts that primarily hide...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Dr. Matteo Bellucci at General Electric
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
HR’s First Predictive Project? Pre-hire Candidate Screening
 Corp recruiters have a very important and difficult job....
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Banks at The Applied Research Laboratory at The Pennsylvania State University
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Text Analytics Interview Series: Frédérick Guillot at Co-operators General Insurance Company
 In anticipation of his upcoming conference presentation, Leveraging Hands...
Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech
 In anticipation of her upcoming conference co-presentation, Utilizing Advanced...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC
 In anticipation of his upcoming Predictive Analytics World for Manufacturing...
5 Common Mistakes Multi-Channel Retailers Make, and How to Avoid Them
  Multi-channel retailers are often finding themselves stuck in...
Three Critical Definitions You Need Before Building Your First Predictive Model
 Portions excerpted from Chapter 2 of his book Applied...
Measurement and Validation: An Often Underrated Aspect within the Predictive Analytics Discipline
 In our Big Data world, software applications and programming...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Improving Word Clouds as Tool for Text Analytics Data Visualization
 Rich Lanza will present Using Letter Analytic Techniques to...
Dr. Data’s Music Video: The Predictive Analytics Rap
 With today’s release of “Predict This!” – the rap...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Mid-Life Journey to Data Science
 Data Science has been hailed as the sexiest job...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
 In anticipation of his upcoming keynote conference presentation, Buy...
What are you Predicting in Customer Retention?
 Customer Retention models are arguably the most valuable models...
Wise Practitioner – Predictive Analytics Interview Series: Ken Elliott at Hewlett Packard Enterprise
 In anticipation of his upcoming conference presentation, Operationalizing Analytics:...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller at Constellation Research
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference presentation, Predicting the...
Hey FinTech, What’s Your Strategy for Leveraging Unstructured Data?
 Financial technology has sparked a global wave of startups...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Raffael Devigus at F. Hoffmann-La Roche AG
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Rebecca Pang at CIBC
 In anticipation of her upcoming conference presentation, Driving the...
Employee Engagement – a Tricky Metric for Predictive Analytics
 Our work focuses on using predictive analytics to decrease...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Daniil Shash at Eleks
 In anticipation of his upcoming Predictive Analytics World for...
The Information Age’s Latest Move: Four Predictive Analytics Developments for 2016
 Originally published in Big Think Prediction is in the...
Why Do We Stop Asking Why?
 I’ve lived through this phenomenon first hand. The environment...
Predictive Analytics and the Internet of Things
 As technology continues to empower our ability to conduct...
Wise Practitioner – Predictive Analytics Interview Series: Mario Vinasco at Facebook
 In anticipation of his upcoming conference presentation, Advanced Experimentation...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Vishwa Kolla at John Hancock Insurance
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence, Part II
 In my last post, “Coefficients are not the same...
Wise Practitioner – Predictive Workforce Analytics Interview Series: John Lee at Equifax Workforce Solutions
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Peter Bull at DrivenData
 In anticipation of his upcoming conference presentation, Predicting Restaurant...
The “Predictive Analytics” FAQ — What’s New in the Updated Edition and Who’s The Book for?
 This is the preface to Eric Siegel’s newly-released Revised...
Wise Practitioner – Predictive Analytics Interview Series: Matt Bentley at CanIRank.com
 In anticipation of his upcoming conference presentation, Predicting Online...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Lisa Disselkamp and Tristan Aubert at Deloitte
 In anticipation of their upcoming Predictive Analytics World for Workforce conference...
The Data Scientist’s Dilemma: Does Skipping Breakfast Kill You?
 Would skipping breakfast kill you? Not necessarily—but confusing correlation and causation...
Predictive Analytics Can Help with the Challenges Facing Manufacturing in the 21st Century
 Historically, data and analytics have been key to the...
Wise Practitioner – Predictive Analytics Interview Series: Nate Watson at Contemporary Analysis
 In anticipation of his upcoming conference presentation, Predictive Sales...
Customer Experience Predictions for 2016
 As we look ahead and see 2016 unfurling in...
Predictive Analytics Book Excerpt: Hands-On Guide—Resources for Further Learning
 Here is the Hands-On Guide that appears at the...
Wise Practitioner – Predictive Analytics Interview Series: Hans Wolters at Microsoft
 In anticipation of his upcoming conference presentation, Predicting User...
Machine Learning: Not Necessarily a New Phenomenon in Predictive Analytics
 One of the more recent topics gaining traction in...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Netflix, Dark Knowledge, and Why Simpler Can Be Better
 Weary from an all-night coding effort, and rushed by...
The Case Against Quick Wins in Predictive Analytics Projects
 When beginning a new predictive analytics project, the client...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jason Noriega at Chevron
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Matthew Pietrzykowski at General Electric
 In anticipation of his upcoming conference co- presentation, Advanced Analytics...
B2B Predictive Analytics: An Untapped Sector
 Much work in predictive analytics and data science has...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Greg Tanaka at Percolata
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Michael Li at The Data Incubator
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Four Ways Data Science Goes Wrong and How Test-Driven Data Analysis Can Help
 If, as Niels Bohr maintained, an expert is a...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence
 When we build predictive models, we often want to...
Oracle’s Ten Enterprise Big Data Predictions for 2016
 Companies big and small are finding new ways to...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part III: The Expert
 So you have gathered your data and completed your...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Mobile Analytics-Mining the Visit Experience of the Customer
 Mobile technology as part of the Big Data discussion...
The Devil’s Data Dictionary – Making Fun of Big Data
 Buy it on Amazon When Stéphane Hamel coined the...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Waber of Humanyze
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
The Quest for Unicorns
 Will there be enough data scientists in the future?...
Most Swans are White: Living in a Predictive Society
 In anticipation of the forthcoming Revised and Updated, paperback...
Hiring? Approving Mortgages? It’s the Same Thing
 Imagine that Chris wants to buy a house and...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part II: The Skeptic
 So you have gathered your data and completed your...
5 Types of Analytics in Business: One to Go After and One to Avoid
 I have been lucky enough to work in some...
The Beginner’s Guide to Predictive Workforce Analytics
 Human Resources Feels Pressure to Begin Using Predictive Analytics...
Predictive Modeling Forensics: Identifying Data Problems
 Excerpted and modified from Chapters 3 and 4 of...
Five Wins for Retail with Predictive Analytics
 We’ve heard a lot about how big data is...
Faster Credit Scoring Dev With Specialized Binning Code – R Package
 Introduction One of the main concerns in a credit...
Good Predictions != Good Decisions
 A Fateful Tale Ted is having a rough week...
Using Predictive Analytics to Bring Retailers Closer to Their Customers
 Based on the amount of retailers that have been...
Visualization: Panacea for Building Analytics Solutions?
 Data,data,data everywhere and what do I do with it....
Five Challenges in Using Predictive Analytics to Improve Patient Outcomes
 In the increasingly patient-centric world of healthcare, predictive analytics...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part I: The Early Adopter
 So you have gathered your data and completed your...
Five Ways Predictive Analytics Will Shape the Future of Advertising
 Predictive analytics sounds almost mystical, and in a way,...
Five Ways Predictive Analytics Can Improve Patient Outcomes
 The use of analytics in healthcare is gaining momentum...
Wise Practitioner – Predictive Analytics Interview Series: Scott Lancaster at State Street Corp.
 In anticipation of his upcoming conference presentation, Predictive Analytics...
Can Employee Development Lead to Business Mediocrity?
 Our predictive workforce assignments yield staggering results; saving /...
Empathy and Data Science: A Fable of Near-Success
 Editor’s Note: While the story is fiction, the events...
Wise Practitioner – Predictive Analytics Interview Series: Jeff Butler at IRS Research, Analysis, and Statistics organization
 In anticipation of his upcoming conference presentation, The Changing...
A Look at How Big Data is Changing Sports on the Field and in the Press Box
 While major rules rarely change, everything else about professional...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Satyam Priyadarshy at Halliburton
 In anticipation of his upcoming conference presentation, Challenges in...
Automation: Friend or Foe to the Predictive Analytics Practitioner
 Technologies and Big Data continue to bombard our working...
Winning Roles: Moneyball 2.0, for your Hiring and Succession Planning Processes
 Business can learn a lot from sports in terms...
Wise Practitioner – Predictive Analytics Interview Series: Werner Britz at RCS Group
 In anticipation of his upcoming conference presentation, Recoveries: External...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Michael Dulin, Carolinas Healthcare System
 In anticipation of his upcoming keynote conference presentation at...
Wise Practitioner – Predictive Analytics Interview Series: Benjamin Uminsky, Los Angeles County
 In anticipation of his upcoming conference presentation, Mining the...
Wise Practitioner – Predictive Analytics Interview Series: Jessica Taylor of St. Joseph Healthcare
 In anticipation of her upcoming conference co-presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: COL William Saxon, Department of the Army
 In anticipation of his upcoming conference presentation, From Wisdom...
Defensive Data Science: What we can Learn from Software Engineers
  To view this content OR subscribe for free...
Wise Practitioner – Predictive Analytics Interview Series: Patty Larsen, Co-Director, National Insider Threat Task Force
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Wise Practitioner – Predictive Analytics Interview Series: Bin Mu at MetLife
 In anticipation of his upcoming conference presentation, Establishing Value:...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor
 In anticipation of his upcoming conference presentation, Picking the...
Wise Practitioner – Predictive Analytics Interview Series: Catherine Templeton, PAWGOV Keynote Speaker
 In anticipation of her upcoming keynote conference presentation, Reforming...
Wise Practitioner – Predictive Analytics Interview Series: William Wood of St. Joseph Healthcare
 In anticipation of his upcoming conference co-presentation at Predictive...
The Key to Modelling Success-The Variable Selection Process (Part 2)
 Last month, I discussed the importance of variable selection...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference presentation, Predicting Behavioral...
Wise Practitioner – Predictive Analytics Interview Series: Scott Jelinsky of Pfizer, Inc.
 In anticipation of his upcoming conference presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Chris Franciskovich at OSF Healthcare System
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Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien at Paychex
  To view this content OR subscribe for free...
Wise Practitioner – Predictive Analytics for Healthcare Interview Series: Daniel Chertok at NorthShore University HealthSystem
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5 months ago
Employee Life Time Value and Cost Modeling – Part 3

 

Employee Tenure in a “Survival Analytics” Framework

With a cumulative cost curve in hand, we now turn to evaluate attrition. We hope to illustrate a far more intuitive and useful visualization than the popular business metric, annual attrition. Annual attrition is but a single point on this easy-to-understand business tool.

Survival Analytics

Employee attrition falls into the same class of “survival” problem as machine failure rates or medical research. This domain has brought us solid statistical innovations, including a visualization known as the “survival curve.” The risk of a particular employee terminating from a job is much the same a figuring when an industrial machine is going to stop working, or how long a patient may live with a given disease. In all cases, we are estimating when an event will happen while we’re in the middle of normal operations.

Calculation

In measuring employee attrition or any survival analytics problem, there is a complication. We don’t know when current employees will terminate – it could be tomorrow or years. Since current employees are a significant part of our sample, we have to compensate.

It is not statistically correct to simply average or regress employment tenures, for this reason. Likewise, it is technically wrong to tally or predict terminations without regard to tenure or staffing level. There is a subtle interplay between tenure and termination that must be handled properly.

This adjustment is done with the Kaplan-Meier Estimator, which mathematically removes current employees from calculations beyond their tenure. Born decades ago from medical research, the Kaplan-Meier calculation is standard in many fields. The function is built into most modern statistical software, such as the “survival” package in R. It can even be done in Excel, with a bit of setup.

The Hazard Curve

Once Kaplan-Meier has been properly adjusted, we can show attrition in at least two ways. The first is called a “Hazard Curve,” which is the chance (hazard) that the event (termination) will occur on any given day in tenure. In Figure 9 we see hazard curves for two hypothetical locations, Chicago and New York. We see that early termination is more likely in New York.

Figure 9. Hazard Curves – Daily Probability of Termination

We will use these Hazard Curve values later for risk-weighting, but it is not the best visual tool.

The Survival Curve

The best visualization of employee attrition is the Survival Curve. It is a sum of the probabilities in the Hazard Curve, up to each point in tenure. It shows nuances, bulges, trends, and differences far better than any simple attrition figure. We have found that the Survival Curve is accessible for most business users, and hope someday to see it on BI Dashboards everywhere.

Figure 10. Survival Curves – Probability of Reaching Tenure

Across the horizontal ‘X’ axis we see tenure, from zero to many years. On the vertical ‘Y’ axis we see the probability of an employee surviving to that tenure. In fact some people don’t show up to their first day of work – but on day 1, survival is near 100%. Statisticians will recognize the Survival Curve as (1 – CDF) of the Hazard Curve.

Attrition for All Periods

The business-familiar annual attrition number is here – at the one-year mark, we see 86% survival for the Chicago curve, which subtracts from the top for 14% annual attrition. Likewise, the New York curve shows 58% survival at one year, which subtracts to 42% annual attrition.

But, one year is somewhat arbitrary. Remember that our breakeven point is 2 years – arguably this is a more useful threshold. At this 24-month breakeven point, we see 65% remaining from Chicago, but only 21% from New York.

One of the great strengths of survival curves is that we can compare multiple groupings. On one chart, we can compare geographic regions, as in the chart above. Likewise, we can compare managers, or multiple job roles. In predictive hiring work, we compare survival curves before intervention vs. after intervention.

Rolling it Up: Employee Lifetime Value

One important application of these curves is to obtain the lifetime value of an employee in the role.

We can look up the cumulative value at an arbitrary date, say 5 years. The chart shows that an employee at that tenure is worth $78,851. However, not all employees last 5 years – only 18% from Chicago and 0.2% from New York.

Risk Weighting

Borrowing a technique from finance, we will risk-weight the cost curve to give our answer. A 5% chance of getting $100 is effectively a $5 “expected value.” In a similar manner, we will multiply every probability in the Hazard Curve, Figure 9, with the matching dollar value in the Cumulative Value Curve, Figure 7.

Then, we sum these expected values into a single number. This is called a dot-product, available in Excel as “DOTPRODUCT” or in R as simple multiplication. This dot-product gives us a proper risk-weighted lifetime value.

Be careful to use the Hazard Curve, the daily probability of termination, and not the Survival Curve, the cumulative probability of survival. Also, take care to use the entire Hazard Curve, extending far into the future – the sum of probabilities under that curve should equal 1.

The Lifetime Value of an Employee

Figure 11. Comparison of Employee Lifetime Value

In this sample, a Chicago employee’s lifetime value is $31,487, while the New Yorker is a loss with -$9,343. A far cry from the potential $78,851. These groups have the same cost curve, but different survival curves – which makes a significant difference.

This is more like the lifetime value of a new employee in a given role, than the lifetime value of a specific employee. We are not attempting to predict whether a specific person will someday rise to become President of the company, or if they will take a lateral move to another department. When they enter a new role, they enter a new set of calculations.

A Useful Rollup Number

The Lifetime Value figure is sensitive to changes in attrition as well as costs or performance. It encompasses most foreseeable financial aspects of employees in that role, and allows fair comparisons of value.

While these numbers may never show up on a financial balance sheet, they are a strong estimate of how a set of employees will play out into the future.

Big Data Approaches to Employee Development

The above approaches assume that all employees are the same, and average performance results. This is not entirely unreasonable, since we often have no prediction of what kind of candidate is joining the company. But, certain roles give us enough data that we can evaluate the many paths to success, and the relative value of each. We can even begin to predict which of several performance paths a new candidate may take.

Rather than building a monolithic example of sales based on averages, we can use large-scale tools like Hadoop or SAP HANA to do better. Consider a sales example, in which we have data for every single transaction sold by every rep for 10 years. Consider that we sort through these millions of transactions, with thousands of sales reps.

Then, we evaluate the sales that each rep made from their first day – how did they do? Instead of one performance curve, we have thousands.

  • Did they start strong, and plateau?
  • Did they slowly grow and learn to sell over time?
  • Did they start strong, fizzle out, and terminate?
  • Did they never get it and terminate early?
  • Or something else?

We can use clustering algorithms to find groupings of sales rep performance – that is, how different reps ramped up. Now, instead of thousands of performance curves, we may have 3-7 well-traveled clusters or patterns.

In the sections below, we would calculate different breakeven points and different lifetime values, for each of these types of sales reps. Does the business prefer a slow-learner over a strong-start-then-plateau sales rep? The data will tell us – at the simplest level we just need to compare Lifetime Values of each curve type. There are strategic and teamwork considerations as well, and a complete comparison would move into Monte Carlo simulation.

It also moves us far beyond the scope of this chapter. This big data analysis lays the ground for predictive work to identify the propensity for candidates to follow one of these sales performance paths. More advanced applications will span multiple performance variables. This is a simple, single-performance-variable example of what is possible with transactional data and a good amount of effort.

Practical Applications of Employee Metrics

After all of this work, we are left with several useful assets for the role:

  • Daily Cost and Performance (The Quantitative Scissors)
  • Cumulative Net Value (The Hockey Stick)
  • Replacement Cost
  • Daily and Cumulative Breakeven points
  • The Survival Curve
  • Employee Lifetime Value

Here are a variety of business applications of these metrics:

Measuring the Cost of Attrition

Everyone knows that attrition is expensive, but what is the actual cost? The relevant number is the amount that the business must spend to handle the problem, i.e. the Replacement Cost.

We find the number of attrition-related hires per year – for example, say a role with 1,000 agents that has 40% annual turnover, and a replacement cost of $11,000. This implies 400 agents need to be replaced per year, if the center is holding steady or growing. Simply multiply 400 agents by the Replacement Cost for the annual attrition cost.

In this case, attrition costs $4.4 million a year. If the company halved its attrition rate, they would save $2.2 million.

Scenario Planning

Employment, and business in general, is not a laboratory environment. We don’t get do-overs for failed scenarios, and our ability to “try things out” is limited. Customer analytics is slightly more amenable to A/B testing, just because the relationship is thinner, and there are many customers.

With this model of lifetime value, we can simulate the impact of programs. What happens if we:

  • Increase wages by x%, and assume it will shift survival curves up by y%?
  • Reduce wages by x%, and assume it will shift survival curves down by y%?
  • Spend $x more on training, and assume it will accelerate learning by y%?
  • Spend $x more on coaching, and assume it will help New Yorkers stay y% longer?
  • Hire x% more employees in Chicago?

Any of these business changes would impact any or all of the underlying three curves. The outcome of these scenarios can be estimated, compared and prioritized.

Consider a change in training:

  • More training would increase the first part of the cost curve.
  • More training would (hopefully) speed up the ramp-up period on the Performance Curve.
  • More training may decrease attrition, moving the Survival Curve up a bit.

The combined effect of these changes would be seen in two ways:

  • A different cumulative breakeven date – hopefully lower, if the ramp effect overpowers the increased cost
  • An increased lifetime value – with a higher Survival Curve and more ultimate Performance

If the three underlying curves are modeled properly, they will be sensitive to any operational change.

Impact of Hiring Changes

Hiring changes are more complex. Consider a new hiring program to find candidates with lower attrition. This means that the new program would find new candidates with higher Survival Curves. The Cost and Performance curves would remain the same, but the lifetime value of the new hires would be higher, due to a lower risk of attrition.

Two calculable outcomes would bring value to the company:

  • Fewer early terminations – fewer of the new hires would terminate while still “in the red” on the Cumulative Value Curve
  • More good candidates – the new hires would tend to last longer past the breakeven point, accruing more lifetime value.

These numbers can be calculated and tested. In the first case, of fewer early terminations, the models would estimate a new turnover rate, implying perhaps 40 fewer pre-breakeven terminations. As before, say the Replacement cost is $11,000. So, the company would save 40 times $11,000, or $440,000 in the first year as it transitions to a lower attrition rate.

In the second case, we compare Lifetime Value in the role, before and after the change. We multiply the increase in LTV by the number of new hires, to show an increase in the stock of employee value in the role. Perhaps the LTV increased by $3,100, and we hired 400 agents; therefore we will increase the value of employees in this role by $1.24 million.

Predictive Analytics Thresholds Tuning

Models can be designed to score predictions of future employee attrition or performance, even before someone is hired. Such models are commonly deployed as part of the hiring process to find better candidates. This advanced form of analytics raises employee models from rough averages to a very granular, individual-based view of employment.

All of the above calculations feed directly into such a modeling exercise, and become the method by which we judge the success of a model. If we want to increase performance, we expect to see an increase in the Performance Curve. If we expect to decrease attrition, we would see an increase in the Survival Curve. A successful model will bring higher LTV.

Attrition-based models often use survival curves directly, and aim to shift the curves up. All predictive models create a “score” – your credit score is an example. The Survival Curve becomes the basis for a range of acceptance thresholds for the model. This plot is an example of a survival curve with multiple predictive bands:

Figure 12. Predictive Thresholds in a Survival Model

Employee Costs, Survival, and BI Dasboards

Most of these figures are prime candidates for monitoring in a Business Intelligence framework, particularly interactive dashboards. Imagine a few possibilities:

Survival Curve Dashboard

Consider a dashboard with Survival Curves for every major role in the company. Users could drill in to compare attrition across departments, regions, managers, and roles. Managers could find the areas with the most pain, as measured by turnover. Researchers could identify and discover outliers.

KPIs and alerts could be implemented to keep groups on track. New hires and the results of predictive hiring could be tracked in real time.

Costs, Performance, and Lifetime Value across the Enterprise

Likewise, the single-role Cost Curves and Performance Curves could be combined, drilled into, and split up across the enterprise. Users could compare training costs between geographical regions. Researchers could investigate impact of training or on-boarding changes. Breakeven points and lifetime value could be into directly compared. Scenarios could be tested directly in the BI framework.

Conclusion

We believe that survival curves, along with Cost and Performance curves, are three vital tools that should be produced by every HR or Finance department for every high-traffic role. These three numbers capture the essence of the role’s impact on the company, and are the basis of powerful calculations.

Human Resources is known for having inward-facing metrics, like Cost per Hire or Time to Fill. The metrics that we have introduced here – Cumulative Employee Value, Survival and Employee Lifetime Value – go beyond HR to serve enterprise goals. They are key indicators of how employees – the people that HR hired – are lasting and performing.

These metrics are especially valuable for high-volume, high turnover roles. They are the measuring stick for improvements to hiring selection, engagement efforts, and performance improvements. Is it time for your organization to begin using them?

Author Bio

Pasha Roberts is chief scientist at Talent Analytics Corp., a company that uses data science to model and optimize employee performance in areas such as call center staff, sales organizations and analytics professionals. He wrote the first implementation of the company’s software over a decade ago and continues to drive new features and platforms for the company. He holds a bachelor’s degree in economics and Russian studies from The College of William and Mary, and a master of science degree in financial engineering from the MIT Sloan School of Management.

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