Mercer Short Duration Fixed Income Fund Class Y-3 · NASDAQ:MSDYX
Mercer Short Duration Fixed Income Fund Class Y-3 — Mercer — Investor Presentation (March 2013)
40 slides from Mercer Short Duration Fixed Income Fund Class Y-3's investor presentation, Mar 27, 2013
Slide 1: As HR Joins the Metrics and Analytics Revolution, Where is Compensation?
MERCER WEBCAST As HR Joins the Metrics and Analytics Revolution, Where is Compensation? March 27th, 2013 Jeanie Adkins – Partner, Mercer Haig Nalbantian – Senior Partner, Mercer CONFIDENTIAL This presentation contains proprietary methodologies and tools that remain the property of Mercer. © 2012, Me
Slide 2: Today's Speakers
Today’s Speakers Jeanie Adkins QUESTIONS? Partner Mercer Human Capital US Rewards Segment Please type your questions in the Q&A section of the toolbar and we Co-Leader will do our best to answer it. jeanie.adkins@mercer.com +1 502 561 8944 While in full-screen mode, simply use the Q&A button on the
Slide 3: Objectives of today's presentation
Objectives of today’s presentation • Introduction • Current state of rewards analytics • A look at the measurement continuum • Tackling the metrics and analytics “wish list” • Tangible steps you can take • Q&A MERCER WEBCAST 2
Slide 4: HR evolution
HR is asking new questions What are the implications for rewards professionals? How do analytics support a company’s reward strategy? Do we tha hav y tn ep n t pa ntion ee a r e d t y co u r cur rt rete ob m o o t? e a plia Do s supp talen dd nc ram e best The era of res e r g se isks pro of th HR e
Slide 5: Current State of Rewards Analytics
Current State of Rewards Analytics MERCER WEBCAST
Slide 6: WorldatWork/Mercer survey results at a glance
WorldatWork/Mercer survey results at a glance Basic analytics remain more common MERCER WEBCAST 5
Slide 7: Results at a glance: Metrics and analytics wish list
Results at a glance Metrics and analytics wish list “What would you like to explore through analytics that you are unable to do today?” • The majority of respondents said they would like to learn which elements of their rewards strategy most effectively motivate their best performing employees MERCE
Slide 8: Participant Poll
Participant Poll What would you like to explore through analytics that you are unable to do today? A. Critical drivers of employee retention in your organization B. Whether current sources of talent will fulfill your future business needs C. Where you can reduce or reallocate business costs without
Slide 9: Participant Results
Participant Results What would you like to explore through analytics that you are unable to do today? How you can effectively segment your workforce to identify critical Critical drivers of How your rewards talent segments, 6% employee retention strategy needs to in your adjust to changing organizat
Slide 10: A Look at the Measurement Continuum
A Look at the Measurement Continuum MERCER WEBCAST
Slide 11: First, some perspective: Mercer's thinking about rewards
First, some perspective Mercer’s thinking about rewards • Successful firms have unique business designs, which are supported by unique people strategies • Rewards influence both who is in your workforce and how they behave; therefore, rewards are a key element of developing a unique talent strategy
Slide 12: Careers are an increasingly critical part of rewards
Careers are an increasingly critical part of rewards In a tightening economy, career opportunity provides critical – often less costly – leverage with employees. In tightening global labor markets, careers have become a top management concern. Cultures & environments MERCER WEBCAST 11
Slide 13: Analyzing career rewards
Analyzing career rewards Internal labor market talent flows and rewards Hires Exits Level 8 11 168 38 17 16 7 72 517 81 49 81 6 Lateral 186 moves 125 640 123 Career 129 level 5 190 830 341 184 134 4 312 963 234 227 Promotions 85 3 116 45 383 86 MERCER WEBCAST 12
Slide 14: Companies moving up the analytics curve
Companies moving up the analytics curve In the broader HR community, companies are moving up the curve…but where is compensation? Most powerful Advances in information technology and analytic methods are making it Predictions Simulations possible for organizations to manage and fore- investments in
Slide 15: Analytics Value Curve: Ongoing Reports
Analytics Value Curve Ongoing Reports – sample metrics MERCER WEBCAST 14
Slide 16: Analytics Value Curve: External Benchmarking
Analytics Value Curve External Benchmarking • Annual or periodic comparisons/reviews of base pay, total cash, total direct compensation, and total remuneration competitiveness • Assessments of market prevalence or trends for specific policies, practices, program design features • Assessments of empl
Slide 17: Analytics Value Curve: Correlations/Projections
Analytics Value Curve Correlations/Projections • Total rewards cost modeling; growth and shifts Global Structure Analysis/Design in mix, under different company performance $250K scenarios • Correlations of internal measures of job $200K size/scope and external value—projections of y = 8661.5x - 332
Slide 18: Moving from correlation to causal analysis
Moving from correlation to causal analysis requires isolating and measuring the impact of a given practice Three conditions must be met to show that a human capital factor drives a workforce/business outcome: 1. CORRELATION 2. TIME (Directionality) 3. ISOLATION (Controls) The factors are related. On
Slide 19: Analytics Value Curve: Causal Analysis
Analytics Value Curve Causal Analysis • Engagement driver analysis (what is Turnover Driver Analysis the influence of pay and other rewards on employee engagement and productivity) • Pay / Reward driver analysis (what employee and organizational variables determine pay—base and total cash—in a given
Slide 20: Pay/Reward Driver Analysis
Pay/Reward Driver Analysis Understanding what your organization really rewards High Higher performance rating Agency / Newspaper West Coast More prior jobs Younger International experience Earnings growth Education in Tech field Past promotion Education Prior industry experience Higher pay level Occ
Slide 21: Analytics Value Curve: Simulations and Forecasting
Analytics Value Curve Simulations and Forecasting (Predictive Modeling) • Multivariate pay equity analysis • Modeling impact on profitability or other financial/business outcomes, based on changes in compensation or other HR programs • Modeling impact of turnover for targeted populations based on ch
Slide 22: What people SAY and what they DO
Moving up the analytics curve requires assessing both what people SAY and what they DO SAY DO What employees and employers How employees and employers say as measured through actually behave as measured through Focus groups Individual employee records Leadership and HR interviews Employee turnover E
Slide 23: Conjoint analysis of employee reward preferences
Some companies rely on what people say; others use conjoint analysis to elicit employee reward preferences from what they SAY 400 These Thesethree threeareas areasare areconsistently consistentlyrated ratedas as 350 the thetop topthree threeareas areasof ofimportance importancebyby virtually virtual
Slide 24: Analyzing what employees actually DO
Others rely on analyzing what employees actually DO, like this company. . . Results from statistical analysis of drivers of actual turnover Likelihood to quit More likely Less likely -30% -20% -10% 0% 10% 20% 30% 40% Bonus participation Broader job experience Received technical training Pension vest
Slide 25: Tackling the Metrics and Analytics Wish List
Tackling the Metrics and Analytics “Wish List” MERCER WEBCAST
Slide 26: Can we tell how reward elements affect the best employees?
Can we tell how different elements of our reward strategy affect the best employees? Impact of increasing pay differentiation on performance 0% Market Market share A variety of tools in share (all accounts) combination (e.g., (primary accounts) engagement analysis, Impact on . . . Net regression ana
Slide 27: Pay equity modeling can reveal demographic disparities
Pay equity modeling can reveal demographic disparities in various components of pay Overall disparities per gender and ethnicity Female vs. Male Full-time equivalent base pay Total compensation Total cash compensation Growth in full-time equivalent base pay Promotion likelihood African American vs.
Slide 28: Participant Poll: pay equity analytics
Participant Poll What kind of analytics are you using for pay equity analysis? A. Ongoing reporting B. External benchmarks C. Internal benchmarks D. Cohort analysis E. Correlations F. Controlled statistical modeling MERCER WEBCAST 27
Slide 29: Participant Results
Participant Results What would you like to explore through analytics that you are unable to do today? Controlled statistical modeling, Correlations, 4% 3% Ongoing reports, 12% Cohort analysis, 5% External Internal benchmarkts, 23% benchmarkts, 53% MERCER WEBCAST 28
Slide 30: How can we identify critical talent segments?
How can we identify critical talent segments? Importance for the future Low Criticality, High Proficiency 100% High Criticality, High Proficiency Executive Current Proficiency survey Discipline & Drafting skills Comp. specialists analytics like the Human Current Proficiency Level Dev. & engine Capit
Slide 31: Tangible Steps You Can Take
Tangible Steps You Can Take MERCER WEBCAST
Slide 32: Consider an analytics Center of Expertise
Consider an analytics Center of Expertise Owned by the business, driven and facilitated by HR Strategic guidance Process facilitation • Give strategic guidance and • Facilitate workforce context analytics & planning process with business • Business scenarios and managers related workforce solutions
Slide 33: Move toward causal modeling for pay equity analysis
Move toward causal modeling for pay equity analysis where it is still a standard (necessary but not sufficient) Data gathering Analysis Assessment Employee Characteristics Age Systemic Tenure Experience Evaluation Level Performance Statistical Pay Nature of Work & Business Modeling Drivers Pay Revie
Slide 34: Move one step up the curve toward more powerful analytics
Move one step up the curve toward more powerful analytics For example, if you are focused on benchmarking…pilot total rewards Most powerful cost modeling or critical talent 3-5 Simulations year total direct pay trending and Predictions and fore- comparisons (compared to average casting performers)…
Slide 35: Questions Answers
Questions Answers MERCER WEBCAST
Slide 36: For more information…
For more information… • Related interviews – Patterns of Use and Value Survey 2012 interview – Workforce analytics: Critical role in organizational success interview • Mercer “be in the know’ blog – Mercer subject matter experts and guests will help you discover tips and trends, and will offer advic
Slide 37: Resources Available – Mercer Workshops
Resources Available – Mercer Workshops AMA/Mercer Workshops – http://www.mercer.com/mercer-amaworkshops Date City Topic 4/22 NYC Moving from Data Smog to Cloud 9: Assembling a Relevant Data Engine 4/23 NYC Workforce Analytics and Planning Execution: Building a Center of Expertise 5/13 Chicago Workfo
Slide 38: Speaker Biographies – Jeanie Adkins
Speaker Biographies – Jeanie Adkins Jeanie is a Partner in Mercer's Louisville office and a U.S. Rewards Practice Co-Leader. She works with clients in developing rewards strategies, and designing and implementing plan designs which are consistent with organizational culture and reward strategy. Rece
Slide 39: Speaker Biographies – Haig Nalbantian
Speaker Biographies – Haig Nalbantian Haig R. Nalbantian is a Senior Partner and Co-founder/Leader of Mercer’s Workforce Sciences Institute. A labor /organizational economist, he has been instrumental in developing Mercer's unique capability to measure the economic impact of human capital practices,
Slide 40: Mercer
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