The Winning Game of People Analytics

“The Winning Game of People Analytics”

Larissa RabbiosiDep. International Economics and Management, CBSCBS Human Capital Analytics Group

April 22, 2015CBS Competitiveness Platform

Aim

To make opportunities and threats of human capital analytics

comprehensible and closer to practice

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What HCA should be

Part of your “business philosophy”:Be hypothesis‐driven and use data and

analytics to SOLVE company problems and ANTICIPATE business challenges, in synthesis,

to ADD VALUE to business decisions

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Definition

Human Capital Analytics (HCA) identifies areas for improvement in the organization and through the study of human capital data develops analytical decisions for

business problem‐solving and competitive advantage

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Example 1Sysco (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

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Example 2Best 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

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Example 3 (HCA group elaborations)AAA MNC (name disguised):• Challenge: Given the positive correlation between team‐level employee engagement and performance, how can we change team‐level employee engagement? • Analysis: What explains team employee engagement: employees’ individual characteristics, team diversity, team leader characteristics, etc.

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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)

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It’s about better ANALYTICS

Source: Bersin by Deloitte, High‐Impact Talent Analytics: Building a World‐Class HR Measurement and Analytics Function, 2013

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HCA: pitfalls

What makes a good HC analytics

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What does better analytics imply?

• Better managerial involvement • Better data quality and data organization• Better analytical tools and skills• …don’t forget decision implementation

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Managerial involvement• Managers do not see the need for human capital analytics• Managers are often short ofAnalytics 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

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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 − …

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2. Expand your “binary” question into a whyquestion

• 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?

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Formulate your decision making problem

Variable definition and data collection

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

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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)

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How HCA should be done3. 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? …

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Analyze the data

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)

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Analytical skills

It is not only about having the right “quant guy” good knowledge of stat/econometrics is a necessary but not sufficient condition quant knowledge should be paired with “business problem” knowledge

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Analytical skills: Example

Analyzing the role of team diversity for team employee engagement and team performance E.g., gender diversity: one team has 3 women and 4 men◦ Is this a diverse group?

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So what?How is diversity associated with engagement?

Is about “being diverse” or about “being different”?◦What are the differences between an individual and all other individuals in the team on a specific attribute?

◦ Isolation/“being too different” from team members will increase turnover, lower value index and decrease job satisfaction

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Within‐team diversity index

Harrison & Klein, 2007 AMR

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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

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Analyze the data

How HCA should be done4. 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? …

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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

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Establishing causality: Example

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Inventor's patent counts Coef. Std. Err. z P>z

R&D team reorganization 0.42 0.15 2.80 0.01Divestiture 0.48 0.09 5.16 0.00Launch of new R&D projects 0.06 0.07 0.92 0.36Change of R&D top manager ‐0.03 0.11 ‐0.30 0.76High tech industry 0.46 0.16 2.84 0.01Cross border deal ‐0.66 0.10 ‐6.37 0.00Hostile deal ‐0.69 0.12 ‐5.90 0.00Technological motivations ‐0.13 0.05 ‐2.40 0.02Previous technological links ‐0.10 0.17 ‐0.60 0.55Technological similarity 2.51 0.22 11.42 0.00Relative size 0.00 0.00 ‐0.81 0.42Constant ‐0.34 0.13 ‐2.62 0.01

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

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Establishing causality: Example

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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)


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Analyze the data

Act upon the results

Follow up

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 …

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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? …

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Analyze the data

Act upon the results

Follow up

How HCA is more often done

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The challenge “of the moment” is formulated

Data collected on “general” expectations

Data analyzed implying causality

When implemented, action is rarely followed up

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

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For more informationContact UsHuman Capital Analytics Group Copenhagen Business SchoolKilevej 14, Office 2.74a2000 Frederiksberg

E‐mail: hc‐[email protected]

www.cbs.dk/hc‐analytics

LinkedIn Group: “Human Capital Analytics Group”

“We were able to identify business critical challenges where the HCA group at CBS could support us on our learning journey in putting big data through rigorous analysis, with the ultimate goal of making evidence based decisions. It was very inspiring to see that this can be done."

‐Wicky Skarelokke, HR Business Partner, Automatic Controls & Danfoss Refrigeration, HCA Workshop Participant

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Working with CBS HCA Group

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Curious BeginnerAccess educational and inspirational HR analytics resources

Analytics EnthusiastAddress your company’s business‐critical questions by engaging HCA group on a research and/or educational project

Research PartnerJoin an exclusive network (limited to 5 members) of leading companies and access a variety of tailored research projects, trainings, and educational resources

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

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The Winning Game of People Analytics

Business

Transcript of The Winning Game of People Analytics