Evidence based Change through Analytics


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Benchmarks und Dashboards sind nicht ausreichend, um einen kontinuierlichen Verbesserungs- und Optimierungsprozess zu institutionalisieren. Mittels statistischer Verfahren, wie Cluster- und Regressionsanalysen, werden Kausalmodelle aufgebaut und prognostizierende Analysen erstellt. Diese Präsentation geht auf Herausforderungen, Handlungsempfehlungen und Stolperfallen beim Aufbau von (HR) Analytics ein. Die Einbindung der sog. externen Evidenz, die Identifikation von Leading Indicators (Frühwarnindikatoren, steuerungsrelevanter Kennzahlen) und die Erstellung der Measurement Map sind nur drei Bestandteile des von uns entwickelten Vorgehens bei der Durchführung einer (HR) Analytics Initiative entlang von Reifegraden (Analytics Maturity).

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Evidence based Change through Analytics

  1. 1. (HR) Analytics Initiative: How to create evidence-based change Zürich, 30. Januar 2014
  2. 2. Range of Services Analyses & Workshops Strategy & Execution Webinars & Conferences Pers. Administration & Reporting Assessments & Exec. Coachings Surveys & Publications 2
  3. 3. Focus Topics 3
  4. 4. Fundamentals Common Challenges Getting buy-in from senior leaders and executives about the value of human capital analytics initiatives; Showing the impact of human capital analytics initiatives on business and the bottom line to “make the case” for analytics; Aggregating data into a single, centralized database with consistent, quality data; Developing the capabilities (systems, technology, skills and resources) to do the analytics; Using tangible measures to measure the intangibles; and Moving from the reactive to the predictive. Source: Human Capital Analytics, A Primer, The Conference Board 4
  5. 5. Fundamentals Guiding Principles Focus on the critical few Focus on getting a return on the analytics investment Develop actionable information Embrace predictive analytics Partner with other functions Aim for high-quality standards Rely on intuition when necessary Balance desire for accuracy with need for information Balance the quantitative with the qualitative Use meaningful metrics Communicate data effectively Develop capability throughout HR/HC Source: Human Capital Analytics, A Primer, The Conference Board 5
  6. 6. Fundamentals Myths about (Predictive HR) Analytics We (HR) have not matured enough to do predictive analytics. We don´t capture enough data to do predictive analytics. We need to make big investments in data technology to do predictive analytics. We can simply buy a predictive-modeling capability by investing in advanced HR business-intelligence solutions. We need to hire a group of statisticians before we can do predictive analytics. Predictive analytics produces „perfect“ predictions and are always the best technique. Predictive models are foolproof, i.e. good software tools implies good models. Predictive models always deliver business results. Can be built and forgotten. 6
  7. 7. Fundamentals Evidence-Based Management: Connect scientific coherences with company-specific procedures Identification of specific practices (instruments) Science Practice Metaanalyses Case study Controlled laboratory/field experiments Systematic Systematic reviews evaluation Comprehensive correlation studies Expert survey Systematic Follow-up internal evidence, organizationspecific facts based on systematically collected data external evidence, sound scientific knowledge, generalizable cause-effect relationships Identification of general causal relations (theories) the interaction creates a collective intelligence Based on: Brodbeck, F.; Woschée, R.: Grundlagen und Möglichkeiten eines evidenzbasierten Personalmanagements, 2013 7
  8. 8. Fundamentals Transformative HR Through Evidence-Based Change (1/2) Logic driven Analytics Do you have information overload or persuasive analytics? Applying proven business tools to talent (talent sourcing, surpluses and shortages Using logical frameworks (e.g. LAMP model) Knowing the business models Segmentation Where are your pivotal talent segments? Are you confident you know where your pivotal segments are? Do you know what investments will attract and engage them? Do you know what aspects of their performance provide the highest return? Risk Leverage Is Human Capital R-I-S-K a four-letter word? Does your HR department have processes to assess risk? Does HR have the confidence to distinguish between „good“ risks and „bad“ ones? It is reckless to ignore this issue when it is so much on the minds of boards and CEOs Source: Retooling HR, John W. Boudreau. Presentation 2012 8
  9. 9. Fundamentals Transformative HR Through Evidence-Based Change (2/2) Integration and Synergy Is your HR portfolio less than the sum of its parts? If your individual HR programs are good, but the function as a whole feels underpowered then it probably reveals a lack of integration and synergy. Synergy means finding ways to make 1+1=3. Too often programs, practices and organizational units are in silos (1+1=2) or actually in conflict (1+1=0). Optimization Spreading „peanut butter“ of making investments? Does HR have the courage and analytical rigor to optimize investments in the workforce? Do you invest more where ROIP is higher. Rather than investing in traditional areas where the ROIP may be lower? Source: Retooling HR, John W. Boudreau. Presentation 2012 9
  10. 10. Fundamentals Continuum of Human Capital Analytics Optimization Predictive Analysis Causation Correlations Benchmarks Anecdotes Scorecards & Dashboards Source: Human Capital Analytics. How to Harness the Potential of Your Organization´s Greatest Asset. Gene Pease, Boyce Byerly, Jac Fitz-enz. P. 17 10
  11. 11. Fundamentals The LAMP Framework A L „The Right Logic“ Rational Talent Strategy (Competitive Advantage, Talent Pivot Points) „The Right Analytics“ Valid Questions and Results (Information, Design, Statistics) HR Metrics and Analytics That Are A Force For Strategic Change P M „The Right Measures“ Sufficient Data (Timely, Reliable, Available) „The Right Process“ Effective Knowledge Management (Values, Culture, Influence) Source: Investing in People. Financial Impact of Human Resource Initiatives. Wayne Cascio and John Boudreau. P. 10. 11
  12. 12. Fundamentals HR Analytics Procedure Model analyze maturity EBM: Capital „E“ and small „e“ determine stakeholder requirements define HR research agenda create measurement map identify data & information sources identify leading indicators & KPIs assess situation (strat. analyses) gather data & information transform data & information assess internal and external environment find cause (domains) connections and trends define analytical approach invest & evaluate communicate & use intelligence results develop a prediction scenario(s) predict RoI launch & monitor progress report results Talent? Work process? … probability of future events make a list of metrics to determine the rate of success (cost, time cycle, quality, quantity, reaction, …) Is it related to Integrate results execute & optimize look for connections to business outcomes at leading indicators for solution clues (remark: this is a future-focused exercise) consider methodologies consistency project management … strive for high quality transparency credibility stakeholder input and buy-in … recycle the process Source 1+2 : HR Analytics Handbook; Laurie Bassi. Human Capital Analytics; Gene Pease, Boyce Byerly, Jac Fitz-enz Source 3 : TCB Research Report Human Capital Analytics: A Primer Source 4 : STRIM Unique Selling Proposition (proprietary development in co-operation with ) 12
  13. 13. Fundamentals HR Analytics Procedure Model: Situational Assessment Source: Human Capital Analytics, A Primer, p. 27. 13
  14. 14. Fundamentals HR Analytics Procedure Model: Maturity Levels Source: Human Capital Analytics, A Primer, p. 15. 14
  15. 15. Fundamentals HR Analytics Procedure Model: Measurement Map Investment Leading Indicators Business Results Strategic Goals # of Customer Contacts Selling Success Performance Objectives - Prospect for customers New Customer Sales Volume Appointments (# and %) Closing Ratio - Identify customer wants and needs Product Presentations (# and %) - Present and demonstrate the product Proposals Presented (# and %) Gross Profit per Sale - Manage customer expectations Increase Revenue Repeat and Referral Sales Volume Repeat Customers - Negotiate and close the deal Referral Business Total Gross Profits Gross Profit per Sale Customer Satisfaction Index Source: Human Capital Analytics. How to Harness the Potential of Your Organization´s Greatest Asset. Gene Pease, Boyce Byerly, Jac Fitz-enz. P. 64: Measurement Map for a Sales Training Initiative 15
  16. 16. Fundamentals HR Analytics Procedure Model: Indicators (1/2) Source: Human Capital Analytics, A Primer, p. 19. 16
  17. 17. Fundamentals HR Analytics Procedure Model: Indicators (2/2) 1 2 3 Human Capital Measures (HCMs): Employee engagement 69,2% 77,9% ! Leadership 38,5% 47,1% ! Employee commitment 36,5% 40,4% Readiness level 33,7% 44.2% ! Turnover (voluntary) 28,8% 94,2% ! Employee satisfaction 28,8% 64,4% Competence level 27,9% 36,5% Workforce diversity 24,0% 78,8% Training 21,2% 57,7% Promotion rate 17,3% 44,2% Executive stability (or chum) 17,3% 31,7% Workforce age 16,3% 65,4% Health and safety 14,4% 48,1% Span of control 8,7% 39,4% Depletion cost 5,8% 14,4% Other 4,8% 8,7% ! HR risk perspective Source: Jac Fitz-Enz: The New HR Analytics, 2010 % of HR professionals naming these HCMs as being leading ind. % of HR professionals naming these HCMs as being in use 17
  18. 18. Fundamentals Typical skill proficiency levels required for each of the four analyst types Quantitative Business knowledge and design Relationship and consulting Coaching and staff development Champion Professional Semi-professional Amateur Basic Foundational Intermediate Advanced Expert Source: HR Analytics Handbook. Laurie Bassi. P. 25 18
  19. 19. Fundamentals Benefits of Predictive Analytics in HR HR can redirect the money they spend today on the wrong employee initiatives to more beneficial employee initiatives. The investments that they decide to make that focus on employees will result in tangible outcomes that benefit shareholders, customers and employees themselves. The returns on such investments, via their impact on the top and/or bottom lines, can be quantified. HR departments can be held accountable for impacting the bottom-line the same way business or product leaders are held accountable. HR executives will be included in the conversation, because they can now quantify their numerous impacts on business outcomes. Source: Scott Mondore, Shane Douthitt and Maris Carson, Strategic Management Decisions: Maximizing the Impact and Effectiveness of HR Analytics to Drive Business Outcomes 19
  20. 20. Predictive HR Analytics Map of causalities (learning and growth perspective) Retention of Key People Managerial Leadership Alignment Risk Failure and Availability Risk Human Capital Integrity Risk Value Alignment Occupational Skill Risk Employee Engagement Employee Satisfaction Human Capital Effectiven. Relational Capital Structural Capital Training Business Performance Knowledge Generation Strategy Execution* Knowledge Integration Employee Motivation Motivation Risk Resignation Risk Knowledge Sharing Remark: Referring to Nick Bontis and Jac Fitz-Enz: Intellectual Capital ROI, 2010 * for further remarks: Mark A. Huselid, Brian E. Becker, Richard W. Beatty: The Workforce Scorecard, 2005 Human Capital Depletion 20
  21. 21. Would you like to know more? We invite you ... http://www.strimgroup.com/de/fachtagungen Talent Relationship Management: May 22 Talent Relationship Management : June 6 Talent Relationship Management : June 26-27 Human Capital Analytics: September 19 Human Capital Analytics : October 16 Human Capital Analytics : October 30 21
  22. 22. Your Personal Point of Contact Chairman and CEO at STRIMgroup AG, Zurich / CH Senior Fellow at The Conference Board in New York Lecturer in the Master's program in Human Capital Management at Lake Constance Business School / Germany Gütschstrasse 22 845 Third Avenue CH-8122 Binz (Zürich) New York, NY 10022-6600 Telefon: +41 (0)43 366 05 58 Telefon: +49 (0)172 7590 688 volker.mayer@strimgroup.com volker.mayer@conference-board.org 22