maersk drilling - the use and usefulness of employee engagement surveys: myths and realities

The Use and Usefulness of Employee Engagement Surveys – An HR Analytics Approach This session will take the participants through how The Maersk Group is using HR Analytics to work with Employee Engagement Survey data.

(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent

Name Peter V.W. Hartmann, M.Sc. & Ph.D. Business Intelligence Expert Responsibilities in Maersk Currently: Responsible for HR Analytics/HR Business Intelligence in Maersk Drilling Previously: Responsible for Assessment Tool & HR Analytics in Maersk Group HR Background Research in Personality & Cognitive Ability Psychometrics & development of Assessment Tools Contact information [email protected] Phone: +45 63 36 18 22

Personal data


mailto:[email protected]

The Maersk Group What do we do?


The Maersk Group The A.P. Moller - Maersk Group is a diversified conglomerate with 89,000 employees in over 130 countries We serve customers worldwide mainly in the shipping & energy area- operating in five business segments: Container shipping Terminal operation Tankers, supply, towage & logistics Oil and gas activities Offshore Drilling


Who is Maersk Drilling?

2.102.000.000 Financial turnover 2014 (USD)

903.000.000 EBITDA 2013 (USD)

478.000.000

Net Operating Profit After Tax 2014 (USD)

1972 Founded

3.899 FTE (2015)

Core business: Fleet in operation 2015

22 (+1 coming in 2016)

Marketing & Distribution

Refining Storage Transportation Exploration & Development

Production

Upstream Midstream Downstream

Maersk Drilling provides offshore drilling services to the upstream segment. To large international customers – e.g. ExxonMobil, Statoil, Shell, BP, ConocoPhillips.


http://www.maerskdrilling.com/Pages/default.aspx

http://www.facebook.com/MaerskDrilling

http://www.youtube.com/playlist?list=PL31DCF25F6FA9C557&feature=plcp

http://www.linkedin.com/company/maerskdrilling

https://twitter.com/maerskdrilling

HR Analytics at Maersk How do we see it?


HR Analytics at Maersk The practical application of metrics, statistics and research methodology to provide valid information for better decision making

Descriptive

To use simple statistics to display

standardized key metrics in a user-

friendly format for the purpose of

tracking progress

Linkage To use statistics and research methodology to generate new insights and translate these into recommendations

Predictive To use statistics and research methodology to predict future events and translate these into recommendations


HR Analytics at Maersk General Framework & Working Model

Communicate results

Display information in intuitive fashion

Construct easy understandable story illustrated with own data

Provide recommendations on actions

Analyze internal data

What data do we have? What does our data say? What does our ”existing knowledge”

say about this?

Consult existing knowledge to build model

What do the theories say? What does the research suggest? What Maersk experts do we have?

Identifying the key areas of interest

What is our strategy focusing on? What is critical to the business? Who internally can we partner with?


HR Analytics at Maersk What have we worked on & how?


Descriptive HR Analytics To use simple statistics to display standardized key metrics in a user-friendly format for the purpose of tracking progress

Descriptive




tracking progress


Survey facts

Number of people invited

2014: Approx. 84.500 2015: Approx. 87.000

Survey Types 2014: Paper = 23,500 (27%)/

Web = 62,500 (73%) 2015: Transition to fully digital...

Languages 2015

22 Languages

5 point rating scale strongly disagree

disagree neutral agree strongly agree

N/A

Responses rates

2013: 87% 2014: 90%

Vendor & start date

Vendor = IBM Started 2006

2015 EES key dates

Survey opens

26 Aug

Survey closes

02 Oct.

Results available to managers

04 November

http://www.google.dk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=EkHxDzxEGvTE_M&tbnid=JY0zA4972ttCLM:&ved=0CAUQjRw&url=http://larriershomeschool.blogspot.com/2011/01/adult-computer-class.html&ei=TWJRUe2cEeeY1AXu7YDwDw&bvm=bv.44158598,d.bGE&psig=AFQjCNESRdM-lfzP-rVQXwsZ_f2FY8bJLQ&ust=1364374467184270

Employee Engagement Trend Four core elements of engagement at The Maersk Group: Satisfaction with the company

Pride in working for the company Willingness to recommend company to others Intention to stay at the company

64

66 66 67

69

75 76

72 73

58

60

62

64

66

68

70

72

74

76

78

2006 2007 2008 2009 2010 2011 2012 2013 2014

Engagement External Top 25% Benchmark

External Top 25% Benchmark is derived from 400+ organisations surveying in 200+ countries. On average, the distribution of blue-collar and white-collar respondents is 60/40 compared to Maersk Group’s 41/59.

Manager and Business Unit reports

•Each manager receives a detailed report on his/her direct reports and indirect reports (for leader of leaders) IF he/she has more than 5 respondents (to preserve anonymity)

•Reports provide information on item and scale level with comparison to

a) External benchmark (if any);

b) Maersk Group results;

c) Last year´s result

•Group HR provides “tool boxes” for

a) HR partners, on how best to facilitate the managers´ use of survey data

b) Managers, how to discuss results with team and decide on actions

c) Managers and HR Partners on how to address low engagement scores through focused follow up

•Engagement survey results can be on individual managers scorecard

Linkage HR Analytics To use statistics and research methodology to generate new insights and translate these into recommendations



Linkage HR Analytics Example: Looking at external research for Engagement to guide analytics

Employee Engagement

Safety

(-0.22)

Performance

(+0.14/+0.30)

Turnover

(-0.23)

Absenteeism

(-0.26)

Customer Satisfaction

(+0.30)

Dispositional drivers

(e.g. Personality)

+0.24/+0.46

Situational & organisational drivers

+0.3/+0.5


Linkage HR Analytics Example: Linking Engagement and Performance

Performance -> Engagement

Engagement –> Performance

Performance yr1 –> Performance yr2

Engagement yr1 –> Engagement yr2 There is a strong link between

a team’s Engagement from one

year to the next

There is a moderate link

between Performance from one

year to the next

There is a small but relevant

link between Engagement and

Performance

There is a small but relevant

link between Performance and

Engagement.


Linkage HR Analytics Example: Linking Engagement and Business Unit Performance

Lo

ng

Term

P

erfo

rm

an

ce

Long Term Engagement

BU´s Trend line

External research (e.g. Judge et al, 2001; Harter, Schmidt & Killham, 2003) suggests a moderate relationship between Engagement and performance. Internal Maersk data shows the same


Linkage HR Analytics Example: Addressing the issue of cause and effect in engagement

Willingness to put in a little extra effort

Individual and team

performance

Observing that discretional

effort matters

Feelings of success and

accomplishment

Increased optimism, joy &

team-spirit

Jury is still out on the true causality. Review of available External research as well as internal Maersk data suggests that it is likely to be subtle long term virtuous or vicious cycle where engagement feeds into performance and vice versa.


“The Morale of the troops is vital for winning the battle...”

Engagement is simply a practical way to

utilize that knowledge in a commercial

setting

The Engagement link to Customer

Satisfaction depends on how close the

business and customers are:

BU Distal BU Average BU Close

Engagement-

Customer

Satisfaction

Link


Linkage HR Analytics Example: Linking engagement and customer satisfaction

Linkage HR Analytics Example: Linking Engagement and Absenteeism


Linkage HR Analytics Example: Looking at the “spill-over effect” of the ability to engage employees

Ability to engage across levels

Team Leader of Others

Leader of Leaders

r=xy

Ability to engage across teams

Therefore, the

manager plays a crucial role

in engaging his / her team, over and above the teams individual

perception/circumstances

r=xy

Therefore, the

Manager´s manager plays a role

in engaging his / her organization; but

Good managers engage their employees irrespective of their

own conditions

Team A Team B


Drives Engagement

Drives Performance

Energetic

Sense of urgency; Flexible;

Results driven

Sociable

Self confident; Persuasive;

Team worker

Optimistic

Takes calculated risks; See

challenges instead of problems

Respect

Fair; Respectful; Inclusive

Conscientious

Predictable; Clarifies roles &

responsibilities

Trust

Honesty; Trustworthy; Truthful

We find high

performing leaders

with

ALL kind of

personality profiles,

as well as leaders that

can drive engagement.

However, some

trends

emerged when

analysing leaders

personality profiles


Linkage HR Analytics Example: Linking personality profiles to ability to drive engagement and performance

Linkage HR Analytics Example: Leadership training impact through the Kirkpatrick model

Reaction

•What is the participant´s initial reaction to the training course?

•Can be divided into affective (”I like this”) and utility (”I can use this”)

•Important for “face value” and to avoid scaring people off. The felt utility seems to a better prediction for later effects

Learning

•What knowledge & skills have been acquired?

•Can be divided into what has been learned, what is initially retained and what can the be reproduced – all in the training situation (can be further separated into content e.g. cognitive, interpersonal, psychomotor, etc.)

•Important for determining the “input” the participants are leaving with and potentially bringing back to the office

Behavior

•What “on-the-job” effect is there on behavior, performance, etc.?

•What “learnings” have been transferred e.g. actually brought back to the office and given the opportunity to use/display (e.g. manager support, opportunity to change and implement, etc.)

•Important for the actual impact on how the job is done and what can be directly observed

Results

•What impact does the training have on the overall “hard-core business metrics”?

•Ultimately, overall profitability, but can be broken into customer satisfaction, overall productivity, etc.

•Important for the final evaluation of the return on investment. Training cost vs. increased profitability

External research suggest a moderate effect size of d = 0,6 (but varies) for training for each of the four levels. However, the relationships between levels are weak (small correlation) and the spill-over is diminishing the more distal they become; e.g. Learning (0,66d)=>Behavior (0,44d). So one is not a ”guarantee” for the other, due to several factor influencing the ”flow between levels”

???

!!!

$


Linkage HR Analytics Example: Testing Leadership training impact through longitudinal approach with control for sampling

2011

Q3

Collection of EES2011

2012

Q1

Appraisal scores for 2011

consolidated

2012

Q1-Q2

Possible attendance

2012

Q3

Collection of EES2012

2013

Q1

Appraisal scores for 2012

consolidated

2013

Q2

Data consolidated and analysed

Gain for not-trained

Gain for trained

Training effect

Potential intervention


Predictive HR Analytics To use statistics and research methodology to predict future events and translate these into recommendations



Insert department name via

‘View/Header and Footer…’

Unknown Never been in the bottom in the past 2 years

Been in the bottom last year

Been in the bottom the last 2

years

1 in 4 1 in 8 2 in 5 1 in 2

21-23% 12% 39% 50%

Linkage & Predictive HR Analytics Example: Assessing risk of managers ending in the external bottom 30%

We need to intervene, otherwise the risk is very high for managers to end up being

in the bottom 30% again...

As a manager, what are your chances of falling in the bottom 30% on

Engagement next time?


Insert department name via

‘View/Header and Footer…’

Linkage & Predictive HR Analytics Example: Looking a stability of managers ability to drive engagement

2011

2012

2011

2012

2011

2012

2011

2012

Engagement Manager

Effectiveness Values Index

Performance

Enablement

• Engagement, MEI, VI & Performance

Enablement remains fairly stable from

one year to the next.

• This year’s EES team results are

strong predictors for next year’s EES.

• That means that a team will remain

as Engaged or Unengaged as they

have always been,

unless something is done

about it...

What is the Engagement like for teams where

action has been taken since last year

EI MEI VI

2012

2011

We are unlikely to see a change in EES results, unless something is done about it...


r=xy r=xy r=xy r=xy

Linkage & Predictive HR Analytics Example: Looking at drivers of engagement

= EI

+1

Focus area A

+2

Focus area B +1,5

Focus area C

+ 2,5 Focus area D

+1,75

Focus area E

+ 3

How much do we need to improve on the 5 focus areas to increase Engagement by one point?


Incidents

With injury:

LTI & fatalities

Without injury:

Observations, ”near-misses”,

etc.

Predictors

Training

Engagement

Linkage & Predictive HR Analytics Example: Linking Engagement and Safety in a BU => Lead to recommendation on how many LTI could be prevented by raising lower engaged entities to the BU average

Injuries:

LTI & fatalities

Safety Climate:

Organizational commitment to

safety

Personal Safety Attributes:

Knowledge & Motivation

Safety Performance:

Compliance & Participation

Safety violations:

“Near-misses” & Observations

The scientific model The actual model


Drivers

HSE

Performance Customer Satisfaction

HSE

– Material & Personnel

Training Safety culture

Maintenance of

equipment

Engagement & Managerial effectiveness

Operational performance

Retention

Linkage & Predictive HR Analytics Example: Looking at drivers of operational performance and customer satisfaction

Safety is *one of the causes of operation performance and customer satisfaction *a consequence of performance excellence drivers


Lessons learned


HR Analytics

Data quality

Data knowledge

Data availability

HR Governance

HR IT Systems

HR Processes

First lesson learned: Know your data and where it comes from Knowing your

data will increase the

validity of your analytics

All data comes from

somewhere


Second lesson learned: Analytics requires several iterations

Descriptive




tracking progress



An iterative process

*Metrics & models need

to be constantly refined

*Insights need to fuel

adjustments or new

metrics

A long term investment

* A marathon, not a sprint

* Managing expectations

and communication

* No quick fixes


Knowing the data

*Systems, processes

& governance

* Availability & quality

Third lesson learned: Success through relevancy, mind set & collaboration

The right

question

The right

stake-holders

The right SMEs

Success

It is easier when HR Analytics is truly an organizational mind set characterized by

open to discussion and willingness to investigate and change rather than just an

analytics function


All are these factors are necessary but not sufficient

to ensure success

Managing communication

Realistic expectations

Probabilities

A part of the answer

Takes time

Fixed expectations

Quick fix

One clear answer

Fuzzy expectations

Some insights

Cannot say

Managing expectations

Fourth lesson learned: Managing expectations & communication is essential

Effective communication

Likelihood scenarios

e-(β*X + α) ≈ 42%

Recommendations

Technical communication

p = e-(β*X + α)

Complex communication

All details, assumptions & disclaimers


Fifth lesson learned: Analytics needs to be actionable

Actionable

• Ensuring that our insight are easy to understand and communicate as well as being actionable in order to support the business

Accuracy

• With inspiration from external knowledge, we strive to apply research methodology to generate actual insights and to reduce the margin of error of the recommendations


Thank you! Questions?


maersk drilling - the use and usefulness of employee engagement surveys: myths and realities

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