@CIPD_Events | #HRanalytics17

Sponsored by:

Using People Analytics to Solve

Complex Business Issues

Jordan Pettman

Nestle

CHF 89.5 billion in sales in 2016

328,000 employees in over 150 countries

418 factories in 86 countries

Over 2,000 brands

1 billion Nestlé products sold every day

Nestlé at a glance

16.11.2017

Enhancing quality of life

and contributing to

a healthier future.

Our purpose

16.11.2017

The Nestlé story

2011

2012 2010 1990s 1970s 1947 1929

2000s 1960s 1980s

1867

Henri Nestlé

1905

1866

George H. Page Anglo-Swiss Condensed

Milk Company

2014 1938

16.11.2017

Nestlé

Globally Managed

Businesses

Zones

Markets

SIMPLE COMPLEXITY

16.11.2017

Ensure that:

1. All Nestlé businesses have access to and the capability to work with People Analytics to drive better people decisions

2. Analytics are useful and actionable in the business

3. Any Nestlé business can engage with People Analytics

OUR CHALLENGE

PEOPLE ANALYTICS @ NESTLÉ

Supporting the execution of the people

strategy through tools and frameworks

incl. Strategic and Operational People

Planning

Measuring the impact of HR intervention

and megatrends

Using statistical models and reasoning to

address top workforce concerns

Measuring progress against strategic

priorities through dashboards and

automation.

Managing the compliance with and

governance of change process to Nestlé’s

People Data Standards

16.11.2017

Global Standard People Dashbaords

Globally (191 countries) available self

service People Dashboard Catalogue

• 3 Reporting structures (Global,

Geographical Zone & Market)

• X 5 Categories: Headcount, Diversity &

Inclusion, Talent Management, Hiring and

Turnover.

= 105 Dashboards

Standard Dashboards are available by

Dimensions:

• Location (and type), Business, Function,

Job Level and Managerial Status,

Employee Group and Tenure

16.11.2017

SAP WebBI:

Illustrative Charts

PMEI is a research-based index based on

findings from our engagement survey,

leadership competencies, and external

research.

PMEI highlights actionable behaviors that

positively impact our workforce on

enablement, performance, and retention.

People Manager Effectiveness Index

Linking Engagement and Business Data for leadership development in Nestlé USA

The Legacy of Leaders Program is a peer-to-

peer discovery process for managers led by 2-

3 high performing leaders from NUSA.

This PMEI-based program showcases best

practices from NUSA’s high performing

leaders around team culture, managing

change, coaching, and performance

management.

STRATEGIC IMPERATIVE

Developing leaders at all levels is critical to our success at Nestlé USA and as

such is a key element of our HR operational strategy.

Legacy of Leaders

Statistical relevance of PMEI behaviors

Following the validation work done

by NUSA, Nestlé Global

Headquarters conducted an analysis

with:

• 700 leaders from 189 countries

representing all businesses and

categories

• 2016 completely anonymized

engagement survey data

• Outcome variables from 2012-2016

Validation of PMEI

This study showed that the highest

scoring leaders outperform their peers in

many important areas, including:

• Business growth and financial performance

• Gender diversity

• Development of key talent, more likely to

be on succession plan

• Retention

Key Findings

What this means for Nestlé: We can tailor leadership development programs based on a

targeted and specific set of behaviors that are correlated with business objectives.

Nestlé’s Reward Journey to Maturity

Creation of

Operational

C&B (2009)

Creation of Total

Rewards Policy (2012)

Creation of Operational Manual

to help C&B Managers to

Interpret TRP (2012)

Creation of Maturity

Profile (2011)

Creation Induction

Program to Teach New

C&B Managers (2010)

Evolution of Nestle

Salary Review Mgmt

Tool NSRM (2013)

Argumentation for

better PE

Distribution (2013)

Creation of Total

Rewards Toolkit (2014)

Living

Wage

Project (2014-16)

Nestle Total

Rewards System 2015-2016

Creation of

TR

Capabilities

(2016-2017)

Pay Analytics Toolkit

(2017)

Total

Rewards as

Strategy

Topic (2015-

Present)

Creation of

Teaching Site

for Managers

(2014-2016)

Measuring Reward Maturity

• Rewards Maturity has positive correlation with

• Performance distribution levels

• Leadership Effectiveness scores

• Rewards scores

• Profitability

Drug treatment studies

Active agent

Placebo

Survival time

Formal definitions

- Time: ‘What is the start of follow up time?’

- Event: ‘How do we define a fail?’

SURVIVAL ANALYSIS

CAN WE PREDICT WHO IS LIKELY TO LEAVE THE COMPANY?

All employees will ultimately leave, so the question is not whether, but when? Survival analysis generates conditional probabilities – the hazard rate – the instantaneous risk that

employees will quit during a given time interval.

all relevant events in the life of an employee

Demographics

age

gender

education

commuting

distance

marital

status

past work

experience

Events during employee’s career

promotion

performance review

international

assignment sabbatical leave

training

salary increase

External influences

business

performance

external

opportunities

team dynamics

Input for statistical analyses

- Mantel Haenszel log rank test

- (Extended) Cox Proportional Hazards

model

- Survival trees and random forests

SURVIVAL ANALYSIS

Sample of Night’s Watch Rangers, Stewards and Builders in Westeros

N = 335

A (NOT SO) REAL EXAMPLE

ID Name Gender Origin Age (at T0) Marital Status Training

Period Position

Intelligence

Score Recruitment Encounters

Size of

Enemy

Army

Event

Reason

for

Leaving

T1 T2

105 S T Male The Reach 24 Single 3 Steward 120 Forced by

father 2 20 000 0 NA 0 12

105 S T Male The Reach 24 Registered

Partnership 3 Steward 120

Forced by

father 4 40 000 1

Become a

Maester 12 22

106 O Y Male White Harbor 31 Single 10 Builder 90 To avoid

punishment 1 20 000 0 NA 0 12

106 O Y Male White Harbor 31 Single 10 Builder 90 To avoid

punishment 2 40 000 0 NA 12 20

106 O Y Male White Harbor 31 Single 10 Builder 90 To avoid

punishment 4 60 000 0 NA 20 32

106 O Y Male White Harbor 31 Single 10 Builder 90 To avoid

punishment 6 100 000 0 NA 32 39

A (NOT SO) REAL EXAMPLE

Non-parametric estimator of

the survival curve

The fraction of employees that is still

with the company for x amount of

months after joining

28

A (NOT SO) REAL EXAMPLE

Comparing subgroup differences

A (NOT SO) REAL EXAMPLE

𝜆 𝑡 𝑋𝑖 = 𝜆0(𝑡) ∙ 𝑒𝛽1𝑋𝑖1+⋯+𝛽𝑝𝑋𝑖𝑝

where 𝑿𝑖 = 𝑋𝑖1, … , 𝑋𝑖𝑝 are the realised values of the covariates for subject 𝑖.

Position

Reference: Builder

Location Reference: King’s Landing

Marital Status Reference: Married

Steward: 0.91

Winterfell: 0.42

Dragonstone: 0.40

The Reach: 0.80

Baseline Hazard Single: 1.22

Interpretation: the hazard rate for

Rangers is 4.1 times higher than

for Builders

Ranger: 4.10

to other techniques (e.g. logistic regression)

- Flexible:

Can handle time varying covariates as the characteristics of an employee can change

during the follow-up period

- Competing events:

Can model cause-specific hazards

SURVIVAL ANALYSIS

A (NOT SO) REAL EXAMPLE

• Independent Censoring: the subjects who are censored at time t should be representative of all the

subjects in that subgroup who remained at risk at time t with respect to their survival experience.

– This assumption is fulfilled when the end of follow-up period is defined.

• Proportional Hazards: the survival curves for 2 strata must have hazard functions that are proportional

over time (i.e. constant relative hazard).

• Stratifying

• Interaction with time

Risk of turnover within 12, 24, 36 months

Identifying dominant themes in a vast array of documents

Each document is a mixture of topics and each word is

attributable to one of the document’s topics

Example: Free text in Exit surveys

TEXT ANALYSIS

Identifying dominant themes in a vast array of documents

Fictional results

Data: free text answers to the question «Why

did you decide to leave the company?»

Result:

Topic 1: Family reasons

Topic 2: Career advancement

TEXT ANALYSIS

PAY ANALYSIS

HOW DO WE MONITOR FOR FAIR PAY PRACTICES

Pay equity analysis checks the current payroll against the compensation philosophy.

Interactive, web-based application that enables local C&B to investigate Pay Equity through linear

regression.

Create applications directly from R

No web development skills needed

Users can interactively look at results from the analysis

PAY ANALYSIS

Input year and market of

interest

2

Take a look at the descriptive

plots of the data

3

Decide which variables

you want to include in

your model

4

Interpret the

results

6

Check the quality

of the model

5

Template file is provided

1

performance

pay

job level

seniority

work experience

PAY ANALYSIS

Which variables should be included in the regression model?

Control variables Independent variable Dependent variable

Accounting for these is this correlated with this outcome?

Aimed at improving the predictive ability

of the model

Diagnostic plots guide the user to decide

whether the model is adequate

QQ-plot checks for normality of the error terms

PAY ANALYSIS

PAY ANALYSIS performance

performance

reference high

Predictor Estimate 95% Confidence Interval

High Performance 1.013 [0.981; 1.047]

High performers earn 1.013 times what the reference category earns.

When controlling for the other variables in the model, the difference in pay

between high performers and the reference group is not statistically

significant.

Merci

Questions?

Feedback?

Get in touch!

@: jordan.pettman@nestle.com

: @jl_p

: linkedin.com/in/jordanpettman