Laying the HR Analytics Foundation: The What and Why of HR Analytics. Presented by: Shane Douthitt, Ph.D.

Transcription

1 Laying the HR Analytics Foundation: The What and Why of HR Analytics Presented by: Shane Douthitt, Ph.D.

2 Submitting Questions Q&A icon Questions may be submitted at any time during the presentation. To submit a question: Click on the Question Mark icon (?) on the floating toolbar (as shown at the right). This will open the Q&A window on your system only. Type your question into the small dialog box and click the Send Button.

3 Work Group Speakers Shane Douthitt, PhD Managing Partner Scott Mondore, PhD Managing Partner Hannah Spell, PhD Director of Research and Analytics Matt Betts, PhD Consultant

4 Participating Organizations

5 Learning Objectives Definitions of basic analytics terminology Define Big Data, HR Analytics, Predictive Analytics, AI, and Machine Learning A refresher course on analytics methods Review the basics of statistical methods what they can and cannot tell you. Our predictions about the HR analytics market and future trends Explore where the HR analytics market is headed Practical recommendations for being a good consumer of HR analytics Examine practical ways of using HR analytics in organizations

6 Presenter To stay up-to-date on all our research and presentations, Join our LinkedIn page: Follow us on Shane Douthitt, Ph.D. Managing Partner smdhr.com/leadershipteam.html

7 THE BUSINESS CASE FOR ANALYTICS DEFINING THE BASICS METHODS 101 PREDICTIONS FOR THE MARKET BEING A GOOD CONSUMER

8 Bersin on Big Data & HR Analytics In most businesses payroll is 40% or more of total revenue How well do organizations truly understand what drives performance among their workforce? Do we know why one sales person outperforms his peers? Do we understand why certain leaders thrive and others don t? Can we accurately predict whether a candidate will really perform well in our organization? Companies are loaded with employee, HR, and performance data Are we using this data scientifically to make people decisions? Not yet

9 Walmart 2016 Annual Report What is the ROI expected on this $2.7 billion investment? How did they decide where to make the investment?

10 Why HR Analytics? People Investments Analytics Business Outcomes HR Analytics connect employee data to business outcomes HR Analytics prioritize where and how much to invest HR Analytics calculate impact/roi from people investments Why wouldn t organizations invest in HR Analytics???

11 CASE STUDY: Global Software Company OVERVIEW THE PROBLEM: The company needed to understand which of the knowledge, attitudes, behaviors, and personality traits of their sales force drive actual sales performance. THE GOAL: Determine what competencies and attitudes are driving performance. Use this to make data-driven decisions on future hiring, training, promotion, and reorganization decisions.

12 CASE STUDY: Global Software Company SALES FORCE DATA COLLECTION Assessment Instruments Culture Survey assessed employee attitudes on several key aspects of the organization s sales culture Knowledge assessment - level of understanding on several key aspects of the products and sales tactics Personality inventory - assessed several personality factors thought to be important for sales performance Behavioral Assessment - Sales manager ratings of sales force on a variety of behavioral competencies presumed to be important for sales performance

13 CASE STUDY: Global Software Company LINKING PEOPLE ASSESSMENTS TO BUSINESS OUTCOMES The company provided several measures of sales performance data that SMD linked to the assessment data in order to conduct predictive analytics. This enabled SMD to do the following: 1 23 related to the key drivers. Identify which factors from each assessment were key drivers of current sales rep performance such as new business revenue and quota attainment. Use key drivers of performance to developed a profile for a successful sales force to make data-based staffing and talent management decisions. Use current sales force scores to identify sales representatives that needed focused training

14 CASE STUDY: Global Software Company PROCESS STEPS Step 1: Assess the current Sales Team on knowledge, competencies, culture, and personality Step 2: Determine which factors are the strongest predictors (Key Drivers) of sales performance Step 3: Use the analytics to drive targeted development, identify high performers for promotion and placement decisions

15 Performance (Average Rating) CASE STUDY: Competency HeatMap 7.0 Key Drivers of Quota Attainment & New Business Revenue Maintain Promote Entrepreneurial Situational Know. Tools Sales Conv. Prob./Need Ident. Demand Creation Initiative Sales Process Value Creation Negotiating Collaboration Sales Present. Planning Capability Know. Compet. Selling Top-Performing Sales Rep. s achieved the following: +14% greater YTD Quota Attainment over peers +$672,000 USD YTD New Business Revenue over peers Critical Key Account Leader Competencies are prioritized for development 4.0 Monitor Social Media Level of Impact on Quota Attainment New Business Revenue (Higher numbers indicate a stronger impact based on a 0.0 to 1.0 scale) Focus

16 CASE STUDY: Behaviors that Impact Sales Performance Priority* Competency Behavior Competitive Selling Competitive Selling Planning Consistently influences a customer s existing, competitor-biased requirements. Disengages from opportunities where they are unable to reengineer a customer s existing, competitor-biased requirements. Achieves business goals defined in customer account plan. 4 Sales Presentations Identifies target audience and determines appropriate presentation content. 5 Capability Knowledge Viewed as a "problem solver" by customer because of the ability to help solve business issues. 6 Collaboration *Priorities determined based on the magnitude of the relationship with Sales Performance Builds and maintains strong relationships with key partners, both internal and external to the organization. Sales Reps with a score of 6 or greater on these 6 behaviors have attained their YTD Quota at a rate of more than 14% greater than their peers AND have closed on average $670k more in New Business.

17 CASE STUDY: Global Software Company ROI TARGETED TRAINING CURRICULM From the competencies and attitudes identified as key drivers in sales performance, the company developed a tailored training curriculum to target these specific skills and attitudes. After implementation of the new training curriculum, the company evaluated the performance of those that completed the training and those that had not. They were able to see an increase in sales revenue of $60 million and an increase in quota attainment of $100 million for the sales force that completed the training courses. $ $ $ $ $

18 THE BUSINESS CASE FOR ANALYTICS DEFINING THE BASICS METHODS 101 PREDICTIONS FOR THE MARKET BEING A GOOD CONSUMER

19 Big Data Definition: A collection of data sets so large and complex that it becomes difficult to process using traditional data-processing applications. Connection to Analytics: Underlying all the big data talk is the concept of analytics. Analytics provide the methods to harvest intelligence from the data. So, the HR analytics concept is much more important for HR professionals.

20 We Have Lots of Data Although many questions can be answered using one data source, more strategic questions often require data from two or more sources

21 Why Big Data Now? We are better at: Collecting Data (web-based applications with built in data collection and storage) Storing Data (e.g., the cloud) Analyzing Data (e.g., SEM) Size limitations continuously change

22 What is Big Data for HR? Technically speaking, most IT professionals would NOT consider our data sets as true big data However, all of the trends that we described previously apply to HR We have analyzed data sets with millions of data points Does this mean HR is off the hook with Big Data? Of course not Our data sets don t typically have the magnitude of data, but the big data principles still apply!

23 Our Definition of Big Data for HR Conducting HR analytics with the available people data in your organization to identify key drivers of business outcomes For our purposes in HR the application of the concept of Big Data really spotlights our need for stronger HR analytics with the data we already capture

24 Big Data Examples Bring together Employee Survey data with: Bring together competency ratings from performance reviews/360 s with: Financial data Productivity data Customer service data Financial data Productivity data Any important outcome data Just analyzing HR data in a silo is not impactful!

25 HR Analytics Definition: The analysis and application of a company s people data to uncover people insights/intelligence that informs HR strategies, process changes, and investments all with the goal of improving organizational performance (i.e., driving actual business outcomes). HR Analytics is NOT more reports, more slicing and dicing of data, prettier graphs, or simply more data.

26 Predictive Analytics Definition: The application of analytics to existing data sets to predict future outcomes and trends. HR Application: In HR, we are generally looking to use predictive analytics to understand the people causes or drivers of meaningful organizational outcomes. Example: For example, what employee experiences drive voluntary turnover or what competencies drive quota attainment for sales professionals? Based on the analytics, we can uncover the people levers to reduce turnover or drive quota attainment.

27 Why Analytics? Uses data, tests assumptions, & leads to better decisions and ROI when investing in talent Accumulation of data itself is not that interesting the real utility of big data comes when it is used in predictive analytics Big Data Predictive Analytics Better Talent Decisions

28 AI & Machine Learning DEFINITIONS Artificial Intelligence: AI is the movement toward smart machines and computing systems being able to carry out tasks the way that humans would, except much more efficiently think surgeryperforming robots, self-driving cars, even the filter that sends suspected junk mail to your spam folder. Machine Learning: An application of AI applied to the data analysis processes. Instead of a team of researchers collecting, coding, organizing, and analyzing data, a computer can learn what to look for and how to do it instead. Specifically, developers create algorithms (i.e., math equations) that can be applied to data to make smart decisions and arrive at specific conclusions. Lots of potential pitfalls with these approaches to analytics (i.e., one size fits all approach to predicting complex human behavior, methods limitations). Not the focus of this WG.

29 THE BUSINESS CASE FOR ANALYTICS DEFINING THE BASICS METHODS 101 PREDICTIONS FOR THE MARKET BEING A GOOD CONSUMER

30 Levels of Analytics

31 Predictive Myths & Misconceptions 1 Descriptive Analysis, Group Comparisons, & Data Visualization Defined - averaging across items; displaying counts or frequencies for a given topic. Trend visualization by charting averages/frequencies across time points. Group comparisons showing significant increases or decreases on a topic. Useful in helping to gain an understanding of an organization s progress on the given topic across time BUT, trends based on descriptives alone cannot be projected with accuracy into the future.

32 Predictive Myths & Misconceptions 2 Correlation & Simple Regression Defined - analytic methods used to identify the strength and direction of relationship between two variables. Useful in helping to gain an understanding of the relationship between two things BUT, Just because a relationship exists between two variables does not mean that one causes the other.

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34 Predictive Myths & Misconceptions 3 Multiple Regression Defined examines the relationship between multiple factors on one outcome and the identification of each factor s unique effects on that outcome. Useful closer to real-world modeling in that the effects of multiple variables are taken into account BUT, the identified relationships are not necessarily causal, limiting the usefulness and interpretation of results There IS a better way

35 Myth Buster: The 5 Most Common Predictive Analytic Myths Myth: As long as you have a model it s predictive Fact: The model must be testable and directional Myth: Predicting Engagement is good business practice Fact: Employee engagement is a by-product of a well run organization Myth: Trend lines are predictive Fact: Trend lines only track progress and are not predictive of an outcome Myth: Correlations are predictive Fact: Correlations show a relationship not a cause (e.g., ice cream sales & sharks) Myth: Regression analysis is the way to go Fact: While it can be predictive, regression doesn t show cause

36 The Real Way to Make Predictions Structural Equation Modeling The best method for making predictions in the HR space is an advanced modeling method called Structural Equation Modeling (SEM). 1. Multiple inputs or causes can be tested along with multiple outcomes concurrently. 2. An accurate assessment of ROI can be calculated. 3. It provides the ability to correct for measurement error. 4. Causation can be determined.

37 Big Picture - Method Comparisons

38 THE BUSINESS CASE FOR ANALYTICS DEFINING THE BASICS METHODS 101 PREDICTIONS FOR THE MARKET BEING A GOOD CONSUMER

39 Predictions 1. THE DATA BALANCING ACT 2. ADOPTION AND CHANGE WILL BE SLOW 3. A COMPETITIVE ADVANTAGE WITH ROI... FOR SOME 4. BAD ANALYTICS LEADS TO BAD BUSINESS 5. DEMAND FOR ANALYTICS SKILLS WILL CONTINUE TO GROW Predictive Analytics in HR: SMD s Crystal Ball Says

40 The Data Balancing Act As organizations look to harvest the power of Big Data and HR analytics, some will jump to the conclusion: We need more data! Many organizations will jump right into collecting more data without a clear strategy and plan More data vs. harvesting intelligence from the right data The need/opportunity is to harvest the power of data to identify drivers of business outcomes

41 More Data and More Measurement Several thought leaders are discussing the need for more data and more measurement in HR Generally speaking, we agree that many organizations can improve the quality and frequency of measurement However, we DO NOT recommend simply increasing the frequency for the sake of more measurement There must be a measurement strategy with specific benefits and outcomes Just because we can do something more often doesn t mean it will add value Great Blog on Pulse Surveys here

42 Adoption and Change Will Be Slow This may not seem like a bold prediction, but it s certainly worth discussing Most people didn t choose a career in HR because they love data and analytics HR professionals recognize the benefits of analytics but may not have the skills to effectively apply/consume analytics Eager to use but lack the Subject Matter Expertise

43 Predictions 1. THE DATA BALANCING ACT 2. ADOPTION AND CHANGE WILL BE SLOW 3. A COMPETITIVE ADVANTAGE WITH ROI... FOR SOME 4. BAD ANALYTICS LEADS TO BAD BUSINESS 5. DEMAND FOR ANALYTICS SKILLS WILL CONTINUE TO GROW

44 THE BUSINESS CASE FOR ANALYTICS DEFINING THE BASICS METHODS 101 PREDICTIONS FOR THE MARKET BEING A GOOD CONSUMER

45 Do You Need to Be a Statistician? No HR Business Partners do NOT need a Ph.D. in statistics But you must understand how to leverage analytics to gain people insights that drive results for your organization Have to be a good consumer of HR analytics How to use predictive analytics to drive decision-making and improve performance Ask the hard questions if a vendor says we have a predictive model, ask him/her what statistical method was used to build the model? What if they say it was based on a correlation? Things like adverse impact still matter. Our job is to still manager risk for the organization.

46 Practical Tips We have to change our mindset and approach: Move from reporting HR-focused metrics to identifying the drivers of business results Focus on Predictive Analytics that are linked to business outcomes The analytics are complicated but the story that emerges has to be simple Boil the story down to critical drivers and priorities Improve x (people measure) & y (people measure) and z (business outcome) will improve Start with one project and try it out before jumping all the way in. Ask yourself: Can I articulate why this really matters to the business? Can I articulate the business value of moving this number? Why would senior and front-line leaders care about this metric?

47 Recommendations Have a measurement strategy it can t be to just measure more often Harvest the intelligence from the data you already have, before blindly investing in more data Think about what you will do with the data to drive results Define the business problem first and then identify potential data sources that will likely provide intelligence Show the impact of the measurement process (ROI) and leaders will champion HR measurement.not question it

48 Questions and Comments Please use the chat function to submit a question Recorded meeting and PPT deck will be posted to the HR Analytics Work Group website On HR Analytics Work Group LinkedIn page you can ask the group and speakers additional questions after the meeting

49 Business Partner RoadMapTM

50 Next Meeting Using Analytics to Improve Retention (Case Study) Presented by Shane Douthitt, PhD June 28 th at 1:00pm EST Add to your calendar click here

51 SHRM Educational Luncheon Join SMD for an educational lunch featuring a presentation Using Analytics to Drive Business Relevance and Define the Future of Work by Mark Berry, Vice President of HR for CGB Enterprises. June 19 th 12:45-2:00pm Cochon Restaurant, New Orleans Register here:

52 Let s get in touch: Shane Douthitt, Ph.D. Managing Partner & Co-founder sdouthitt@smdhr.com smdhr.com