Actionable Insights through Innovative Talent Assessment – How Big Data is Changing HR and...

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www.cut-e.com Actionable Big Data Insights in talent selection and development Through online measurement, psychometrics and predictive analytics Richard Justenhoven, Research Consultant, cut-e group

Transcript of Actionable Insights through Innovative Talent Assessment – How Big Data is Changing HR and...

Page 1: Actionable Insights through Innovative Talent Assessment – How Big Data is Changing HR and Assessment

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Actionable Big Data Insightsin talent selection anddevelopment

Through online measurement, psychometricsand predictive analyticsRichard Justenhoven, Research Consultant, cut-e group

Page 2: Actionable Insights through Innovative Talent Assessment – How Big Data is Changing HR and Assessment

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cut-e – who we are

Predictiveanalytics

Robustpsychometrics

Enabled byweb technology

Measurement solutions for all target groups,

sectors and challenges you might want to

assess.

Online assessment products for all levels, jobs and aspects you might want to assess.

Unparalleled integration expertise and truly mobile solutions to

measure wherever you want.

Powering yourTalent Strategy

Make evidence based decisions rooted in data

to drive your talent strategy and deliver a

truly tailored leadership journey

35 Offices worldwide 12 million candidates per year 300 employees 70 countries 40+ languages

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Customers who trust in cut-e

Work across a number of countries together to deliver validated assessment processes for the selection of interns, graduates and experienced hires

Deliver Manager Effectiveness Assessments across Europe and furthermore provide assessments for the hiring of staff across all levels of seniority

Integrated assessment processes delivered across multiple brands for graduates, intern, apprentice and volume recruitment

Trusted Partner Within Finance & Banking;

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Innovative Assessment Tools

Mobile First Assessments

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Introducing ChatAssess

cut-e provides players that

Simulate a real-time WhatsApp like communication

Task for candidate is to respond to incoming messages from different people

All item formats are supported: i.e. Logical reasoning, verbal reasoning & numerical reasoning, Situational Judgement

Innovative Players supporting multiple item formats

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Is this really Big Data?

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Then what is it?

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How can we use this data?

•Probe Candidates on Areas of Risk at Interview

•Clear indicators of what makes a good performer for you

•Early Identification of development areas•Early roadmap of performance potential

•Create a model to assess against

•Early identification of high potential performers

•Efficient screening of potential low performers

•Know who to target•Identify where good performers come from

•Define an attraction campaign

Attraction Screening

AssessmentOnboarding

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Linking Assessment To Big DataCreate a Success Model

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Predicting Performance at an International Investment Bank

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Context

Global Financial Services Firm

Since 2011, cut-e have provided a final-stage executive assessment service to the organisation, for senior and specialist roles

We have tracked the careers of 982 of these senior hires in the organisation over a 5-year period

Process is recommended but not mandated, so a good opportunity for a comparative study

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Findings

1. Combining assessment tools - better prediction of job success than any one assessment tool by itself; - each assessment tool makes a unique value-added contribution to

predicting performance

2. The best predictor of job success is the deductive logical reasoning test: working in a systematic way with a rule-driven logical process

- Medium & High Scorers on this test have a performance rating that is, on average, 33% higher than low scorers on the test

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Findings #2

The data (manager rating & questionnaire results) allowed us to make accurate predictions about work behaviour- Proactive employees tend to have a certain personality profile- Role models tend to have another certain personality profile

The work style questionnaire also allows us to make accurate predictions about the likelihood of leaving e.g.

Rating on Work-style Questionnaire Abandon Rate (leave the organisation within two years)

Work style ‘low fit’ 71% Work style ‘medium and high fit’ 18%

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What does this become?

We use this information to; Inform the final selection

decision Make better, more accurate

decisions Based on high quality predictions about whether a candidate will be successful in the job

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Driving sales productivity through assessment & selection

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Cut-e & Dell – Background

The Beginnings

• Started working together in 2011• Dell undergoing major transformation at the time

New Skillsets were needed and subsequently a new hiring process!

A standardized & validated process rooted in cut-e research was introduced to predict performance in this new salesforce

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Path to Success

Proven set of questions

shapesquestionnaire

Work relevant traits (24 traits)

Directing

Persuasive

Socially confident

Sociable

Agreeable

Behavioural

etc. …

Our research informed us which of the

measured traits are the most important for

success in sales in Dell

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What did this lead to?

What did we do;Introduced shapes assessment with Bespoke Dell Output report and Interview Guide

What did we find out in 2012; High Scorers generate, individually, on

average

36%more than Low Scorers in Revenue terms

High Scorers generate, individually, on average

42%more than Low Scorers in Profit terms

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2016 – Dell ran a follow-up study using 4 years of data

-2 0 2 4 6 8 10 12 14 16 18 20 22 24 26Months from Hire Date

Time to Proficiency Sales Total Revenue RegionAmericasAPJEMEA

Metric 1 NameNullAPOS Total MgnESG RevESG Rev SPMESG Solns RevEUC SW MgnIDM RevIDM RevenuePORTotal MarginTotal Rev SPMTotal Rev SPM No DFSTotal Rev w DFS EMEATotal RevenueTotal Revenue CSMRXDomain Revenue

Months until Term Date (..0-6 months

Did not Term

6-12 months

12-18 months

18-24 months

2-2.5 years

2.5-3 years

3.5-4 years

4.5-5 years

What does this tell us;• Hires cost more than simply acquisition cost• New hires take time to get “good”• Revenue producers actually take a lot of time

Problems:1) Revenue producers at Dell have high

turnover and a long ramp up time2) Their turnover impacts customers and

revenue3) The culture of Dell cannot be changed easily4) How do we identify talent that adapts to Dell

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Matching Performance Against Shapes

We took Dell’s “Big Data” and matched against our shapes assessment scores

Now we know what personality traits have a positive impact on performance/tenure and what has a negative impact

Trait-3 -2 -1 0 1 2 3

Avg. Score

-3 -2 -1 0 1 2 3Avg. Score

competitive

achieving

imaginative

autonomous

Robust

socially confident

Influencing

focused on results

Canny

Open

systematic

driven by recognition

behavioural

sociable

driven by financial reward

identification

agreeable

conscientious

need for support

driven by development opportunities

professional challenge

driven by security

need for structure

working environment

-2.865

-1.328

-2.213

-2.433

-3.000

-3.000

-3.000

-2.884

-2.731

2.085

0.975

1.015

2.195

1.895

1.159

1.559

1.629

2.428

2.949

1.818

2.859

1.108

0.627

2.223

2.377

1.723

0.457

1.043

1.453

1.637

1.073

1.640

3.000

3.0002.010

3.000

3.000

1.510

2.484

2.944

1.502

1.572

2.791

1.411

1.731

-0.135

-0.265

-0.269

-0.469

-0.739

-0.107

-0.287

-1.186

-0.070

-0.200

-0.610

-0.002

0.495

0.598

0.258

0.238

0.563

0.387

0.646

0.130

0.264

0.331

0.321

0.031

Dell Team Account Executive Scores(Each dot is a TM Score, Bar Plot is the Average)

Job TitleAccount Executive I

Account Executive II

Account Executive III

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What did this tell us?

Shapes Dimensions Impact

Achieving-and Imaginative Quickest time to proficiency and strongest revenue contribution in ISR RoleAchieving and CompetitiveAchieving and Driven by Recognition Highest contribution to revenue for AE Role

Systematic and ConscientiousConscientious with Imaginative Conscientious with Autonomous

Least likely to have voluntary termination within 10 month

Shapes Dimensions Impact

Agreeable and Need for Structure Highest contribution to early voluntary termination and strongest detractor to revenue

Agreeable and Work environment Strong detractor to revenueNeed for Structure and Working Environment

Strong detractor to revenue and higher influence on early voluntary termination

Dimensions with a Positive Impact

Dimensions with a Negative Impact

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What do we now know?

Research based on:• 1775 hires in EMEA from 2012-2015

– 293 hired using cut-e psychometrics test– 450 early terminations in 2014-2015 (25%) and we now understand the following

across a large population of hires:

Personality traits that positively impacts performance in terms of revenue and margin

Personality traits that negatively impacts performance in terms of revenue and margin

Productivity curve shows those who remain and become tenured have a productivity advantage of those who leave of $ 2.4m per month or $30m per annum

We now understand what makes a good hire in Dell – each good hire adds $30m in revenue per annum

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Summary

Big Data is all about

• How you use it as well as how much you have

• Good measurement that will bring a lot added value to your recruitment

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Thank youcut-e UK1 Quality CourtLondon WC2A 1HR

E-Mail: [email protected]: www.cut-e.com/UK