People Analytics

Jeremy Doyle

People Analytics

Digital transformation and the future of work

What does it take to win in your

business?

vs.

A Regular Day

Generating Data

62

%

38

%

12

%

88

%

21

%

79

%

29

%

71

%

Speaking Time

Interactivity

88%

38%

79%

71%

Individual Behaviour

30◦

9 AM 10 AM 11 AM 12 PM 1 PM 2 PM

Pitch and Volume

We all generate reams of data traces. By tracking social behaviors over time one can identify communication problem areas and solutions as well as demonstrate how behavioral patterns drive success.

Team 1 Employee

Team 2 Employee

How it Works

What is People Analytics?

The basic concepts of people analytics are:

• View the organization from a holistic perspective with People at the center

• Individual and team behavior correlate to organizational KPI’s

• Data on these behaviors can be objective and quantifiable

• Capturing this often unstructured data will provide new visibility for decision making

“You can’t manage what you don’t measure” – Peter Drucker

$50x

$10x

$1x

Human Capital

(Salaries, wage, benefits)

Workplace Investment

(Real estate, IT, services)

ROI

(Revenue, earnings, growth)

People Analytics provides data to understand the organization like

never before, and to make optimal investments for performance

Sensing Technology

1997 2014

Social Sensing

• Ability to link key behaviors with specific KPIs

• Enables employees to drive change through understanding of behavioral profiles and self set goals and benchmarks

• Enables senior leaders to unlock the behavioral differences in high performing teams

• Enables direct line managers to spot delivery risks based on communication gaps early

• Enables continuous improvement in all aspects of business

The Technology Social Sensing & Analytics

Sociometer

Participants

Registration Data

Insights

Email/Chat Data

KPI Data

Dashboards

Analytics Data

Algorithms

Study Results

Targeted Improvements

A/B Test Everything

Privacy Policy

• Individual information must be kept confidential

• Only users should have access to their individual data

• Others can only see aggregated data

• Conversations are not recorded

• Only features such as tone of voice and volume level are

collected

• No content is captured

• Only patterns of communication

• Opt-In required

Quantifying Collaboration

Cohesion - How tightly knit is the group?

• Productivity

• Engagement

• Trust

Centrality – How freely does information flow?

• Autonomy

• Bottlenecks

• Influence

Exploration – Cross-functional collisions

• Adaptability

• Innovation

• Openness

F2F vs Digital – Volume across mediums

• Workload

• Job Satisfaction

• Retention

What do we need to know?

Past success

linked to informal

relationships?

Generational

expertise

(to be transferred)

Informal

leadership

Organizational

Gaps

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Information Vitality Analysis Dependence on a few influential individuals to share knowledge? Formal leaders are significantly more influential than most staff, but they are not alone

The highlighted individuals are integral to the business, however their extreme centrality may represent

process inefficiency or risks to knowledge retention

Are other team members building influence and expertise to fill gaps?

Centrality (Influence)

Exp

lora

tion

(Inn

ovat

ion)

Integration Initiatives

Can we increase INTEGRATION to drive productivity?

YES

0.7

0.8

3.1

-4.1

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0

Time Change (hours/week)

F2F

Email

Phone/Lync

Physical Activity

Fewer long

meetings

Workload

grows with new

connections

Impacts of Change on Culture

wyze

Drill into your data to find what

behaviors you’re doing well…

…and what needs some work

Track your improvement over

time

Online travel company: Amenities and office layout Software firm: Work process and employee integration

10%

30%

0%

5%

10%

15%

20%

25%

30%

Productivity Job Satisfaction

Att

rib

ute

Im

pro

ve

me

nt

Network Size

• F2F network size • Drives productivity (software code) • Supports job satisfaction post layoff process

• F2F network size driven by lunch habits • Lunch space redesign aimed to increase network size

• Interaction frequency improves completion of interdependent code by 32%

• Remote teams communicated 8% less about dependencies which added cost of over $150 million/year

Collocated

Remote

Time and expense

Interaction Frequency

$150MM cost

-32% time

Highest Performing Lowest Performing Typical

• Extremely cohesive group

• Promoted group achievement

goals over individual rewards

• Three new employees (circled) were

not socially integrated with cohesive

core - even after over one month they

only spoke with their direct manager

Floor 1 Floor 2

• F2F network clearly shows two groups

• The physical layout of the branch

inhibited information sharing

• Future locations all one floor

Retail Banking: Layout and employee integration

• Performance driven by cohesion • Physical distance impedes knowledge sharing

• New employee integration is essential

Case Examples

Case Examples

0%

5%

10%

15%

20%

Sales

Redesign Impact on Sales

0%

2%

4%

6%

8%

10%

Cohesion Productivity Engagement

F2F Cohesion Drives Engagement

• F2F interactions driven by coffee machine location • Redesign aimed to increase F2F time and decrease email volume

0%

2%

4%

6%

8%

10%

12%

Exploration Sales

F2F Effects on Performance

Pharmaceutical Sales & Marketing

German Bank

• Identify processes responsible for human capital bottlenecks • Reveal informal processes driving performance

IT Firm / Engineering Processes

• Identified informal experts; extreme centrality may be barrier

• Informal experts had average completion rate

• Compensation system discouraged expertise sharing

info@humanyze.com

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