The Winning Game of People Analytics
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Presentation from #LarissaRabbiosi at the event Four Drivers For Competitiveness, or in Danish: Fire bud på de næste vækstbølger which took place at Copenhagen Business School on 22 April. bitly.com/CBSVækstbølger Presentation from #JulianaHsuan and #ThomasFrandsenfrom the event Four Drivers For Competitiveness or in Danish: Fire bud på de næste vækstbølger which took place at Copenhagen Business School on 22 April. bitly.com/CBSVækstbølger Human Capital Analytics Group: http://www.cbs.dk/hc-analytics
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The Winning Game of People Analytics
- 5. Example 1 Sysco (global‐food service company): • Challenge: How to improve delivery associates’ retention rate • Analysis: Workforce analysis for each operating unit on work climate, employee satisfaction, productivity and retention to identify actions with the greatest impact on the business • Value added: In 6 years, retention rate of delivery associates improved from 65% to 85% Such employee retention saved about $50 million in hiring and training costs for new associates 5
- 6. Example 2 Best Buy (consumer electronics): • Challenge: How does employee management affect our sales? • Analysis: Employee engagement survey at store‐level • Results: Engaged employees are more likely to please the customer and more willing or better able to maintain store efficiency ratios; Store managers play a key role to engage employees • Actions: Store managers’ action plan to respond to their employees' concerns Employee engagement surveys held on a quarterly basis as compared to annually • Value added: Best Buy quantifies that on average the value of 0.1% increase in employee engagement is about $100.000 6
- 8. Example 3 (HCA group elaborations) AAA MNC (name disguised): Main results • An increase of one standard deviation from the mean of Age diversity associates with an increase of about 62% in team employee engagement Age diversity is lower for female leaders (vs. male), younger leaders (vs. older), non‐expatriate leaders (vs. expatriates). Also leaders’ nationality plays a role. • An increase of one standard deviation from the mean of Job skill diversity associates with an increase in team employee engagement of about 34% Job skill diversity is lower for non‐expatriate leaders (vs. expatriates), younger leaders (vs. older) 9
- 11. What does better analytics imply? • Better managerial involvement • Better data quality and data organization • Better analytical tools and skills • …don’t forget decision implementation 12
- 12. Managerial involvement • Managers do not see the need for human capital analytics • Managers are often short of Analytics Motivation: Not willing (fear of unknown, protection of own individual competitive advantage, inertia, previous stereotypes of HR, do not believe the data coming out of HR) Analytics Ability: do not have knowledge, skills and abilities to consume the analytics, not trained in their management education (university and on the job), Wrong assumptions: “I am not an expert of statistics”…managers do not have to be stats expert to work with analytics ◦ Analytics Opportunity: Were never given an opportunity to observe a power of analytics 13
- 13. How HCA should be done 1. Frame the specific decision or business problem • What your business experience/intuition is suggesting − Our scientists’ patent productivity is decreasing and is lower than our competitors − Team‐level engagement is positively associated with team performance. How can we increase team‐level engagement? − Sales of our stores grow too slowly. Store grow rate should increase of 10% over the next 3 years − … 14
- 14. How HCA should be done 2. Expand your “binary” question into a why question • Why our scientists are producing less patents than our competitors? How does coordination of and communication between scientists affect their performance? Why certain scientists are more productive? Is it just because they are smarter OR… good relationship with team leader, empowered, working with the “right” teammates, better paid, … Is the recent restructure of the organization responsible of the decline in scientists’ productivity? 15
- 16. Data gathering: problems • Data “adapted” to the specific business need • Data dispersion − Duplication of information − Proliferation of different labels for same information − Different coding criteria, different standards • ID (“matching”) variables not univocally identified 18
- 17. Data gathering: what to do • Coordination of data collection Centralized definition of ID variables Centralized/coordinated definition of variables • Data mapping • Reiteration of the same data collection(s) over time • Mapping of organizational changes (e.g., BU reorganization) 19
- 18. How HCA should be done 3. Model your problem and collect the data • Use the “why question” to identify which variables you need How should these variables be operationalized? Should I use objective measures or perceptional measures? At which level of analysis should I collect the data? Where I can find the information I need? How should the data be collected to avoid selection biases which will make my model invalid? What and how data should be collected to run analyses which could establish causality more clearly? … 20
- 20. Analytical tools • Most common: Excel • Less common: specialized software (e.g., STATA, SAS, Matlab, R, etc.) • More sophisticated software: higher productivity lower error rate greater flexibility (e.g., modeling, managing complexity) 22
- 25. So what? …about individual level analysis • The unit of analysis is the individual: that is the business challenge deals with individual level issues − Under which conditions individual team members are likely to under‐ perform/over‐perform? − How does employee mobility across teams affect individual employees’ performance and/or team performance? − How do different categories of team members (e.g., women, older, expatriates, etc.) respond to organizational changes, empowerment, mobility, etc. − …. • N.B. Individual‐level analysis: − It is not about the single individual − Individuals are studied in aggregated fashion 29
- 27. How HCA should be done 4. Analyze the data • Get your smart quants running their models but don’t forget to challenge what they are doing Check for alternative explanations Question the level of analysis Check if the assumptions behind the analysis make sense (in the real world) Are there outliers? Why this analytical approach and not an alternative one? Are we testing correlations or we can claim causality? … 31
- 28. The risk of implying causality: Example • How does the reorganization of R&D teams after acquisitions affect individual inventors’ productivity (e.g., number of patents produced)? • Team reorganization (mixing inventors of the acquired firm with inventors of the acquiring firm) is needed to enhance knowledge transfer and realization of synergies • Consider the number of inventor productivity during the 5 years before the acquisition and 5 years after the acquisition 32
- 29. Establishing causality: Example 33 Inventor's patent counts Coef. Std. Err. z P>z R&D team reorganization 0.42 0.15 2.80 0.01 Divestiture 0.48 0.09 5.16 0.00 Launch of new R&D projects 0.06 0.07 0.92 0.36 Change of R&D top manager ‐0.03 0.11 ‐0.30 0.76 High tech industry 0.46 0.16 2.84 0.01 Cross border deal ‐0.66 0.10 ‐6.37 0.00 Hostile deal ‐0.69 0.12 ‐5.90 0.00 Technological motivations ‐0.13 0.05 ‐2.40 0.02 Previous technological links ‐0.10 0.17 ‐0.60 0.55 Technological similarity 2.51 0.22 11.42 0.00 Relative size 0.00 0.00 ‐0.81 0.42 Constant ‐0.34 0.13 ‐2.62 0.01
- 30. Establishing causality: Example • The coefficient of R&D team reorganization is positive and significant suggesting that the productivity of inventors after the acquisition will increase if we reorganize teams mixing together inventors of the acquiring and acquired firms • However, there are several aspects (e.g., characteristics of the individuals) that we cannot observe and can affect our results: the so called ENDOGENEITY • Endogeneity can bias our results and the consequent decision‐making process 34
- 31. Establishing causality: Example 35 Pre-acquisition Post-acquisition Treatment Group Subsample mean: Subsample mean: First difference (row): (Acquisition with R&D team Reorganization) Patent Count= 3.039 Patent Count= 0.913 Patent Count= -2.126 (N= 1,857) (0.086) (N= 1,857) (0.086) (N= 3,714) Control Group Subsample mean: Subsample mean: First difference (row): (Acquisition with no R&D team Reorganization) Patent Count= 2.954 Patent Count= 1.586 Patent Count= -1.368 (N=1,836) (0.087) (N=1,836) (0.087) (N= 4,136) Differences First difference (Column) First difference (Column) Difference-in-differences: Patent Count= 0.085 Patent Count= -0.673*** Patent Count= -0.758*** (N= 3,693) (0.123) (N= 3,693) (0.123) (0.173) *** p<0.01; ** p<0.05; * p<0.10 • Applying advanced econometric techniques (e.g., difference‐in‐differences setup and matching sample) we find that the direct effect of R&D team reorganization on inventor productivity is negative! • Further analyses show that the damages associated with R&D team reorganization can be alleviated implementing additional organizational changes (e.g., changing the acquired R&D top manager)
- 33. The last mile 5. Present and act on the results • Communicate the results to other executives telling a story Ask your quants to “quantify” the estimated effects Show simulations of the effects in different scenarios Accept unexpected results Link the results to the why question to indicate actions to implement Present the results to the people involved in the entire process … 37
- 34. The last mile 6. Follow up • Collect data about the actions that have been taken How should these actions be operationalized? When should I repeat the analysis to verify the effectiveness of the implemented actions? … 38
- 37. What brings YOU to the next level? • Start with the business challenge • Become a consumer of analytics • Get a buddy • Business buddy: “analytics are not about the math; they are about the relationships” • Quant buddy: “Look for the quants who stare at your shoes, instead of their own, when you engage them in conversation” • Academic buddy: bridging and bonding •Take the responsibility 41
- 40. Sign up for “Connecting People and Performance” at CBS Executive Summer School ‐ 1 ‐ 3 June, 2015 “We have been saying it for years: we need to talk with data in HR. As a HR Partner, I felt inspired on ways to use the available HR data, to help the business make the right decisions. The class was hands on in its approach, which I personally enjoyed immensely.” – Bodil Klink Larsen, Lundbeck “It was, for me, the right course to attend at the right time. Our team is in the midst of evolving and expanding services, from offering primarily HR ‘reporting’ to include also ‘analytics’. The course helped me reflect and gain important insight about the analytics value proposition, where to focus our efforts, and which capabilities are needed to execute on our future plans.” ‐Pete Jaworski, Director, Global Mobility & HR Analytics, Novo Nordisk A/S 44