What can predictive analytics give to recruiters and what are the perils and dangers in using predictive analytics to find, select or assess candidates.

1. © 2016, Future of Talent Institute
Kevin Wheeler
Talent Acquisition Tech Conference
Austin | November 15-16, 2016
The Promise &
Perils of
Predictive
Analytics
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2. © 2016, Future of Talent Institute
Challenge 1: Ambiguity
Recruiters are asked to find people
with skills we’re never heard of, in
unrealistic timeframes, in remote
places.

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Challenge 2: Complexity
We need to know how to find
the signal in the noise.
Desperatly these days!

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Challenge 3: Friction
Our processes do not flow. Required
information is often not easily
accessible. There are choke points
and ambiguities everywhere.

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Challenge 4: Interaction
Communication is unclear. We
need to know what to say. What
is effective?

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Challenge 5: Innovation
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Old thinking and bureaucratic
processes stifle innovation and
change.

7. © 2016, Future of Talent Institute
The Promise
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MORE EFFECTIVE
COMMUNICATION
FASTER
PROCESSES
GREATER
CANDIDATE
UNDERSTANDING
HIGHER
QUALITY
HIRES
INCREASED
INTERACTION
ENAGING
CANDIDATE
SERVICE
PEOPLE
ANALYTICS

8. © 2016, Future of Talent Institute
Predictive Analytics Promises Answers
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• Who are our top performers?
• When & how should we connect with them?
• What attracts them to our firm?
• Which assessment is more accurate?
• Which hires will be the most productive?
• What would increase our quality of hire?
• Which interview questions are most effective?
• What will our turnover rate be in the next quarter?

9. © 2016, Future of Talent Institute
Descriptive & Predictive Analytics Compared
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Descriptive Analytics Predictive Analytics
Purpose
Understand the Past
Observe Trends
Discuss
Gain Insights
Make Decisions
Take Action
Timeframe Past and Current Future
Metrics Type Lagging Leading
Data Used Raw/Tabulated Information
Data Type Structured Structured and Unstructured
Benefits
Understanding
Efficiency
Information & Insights
Effectiveness

10. © 2016, Future of Talent Institute
Predictive analytics uses algorithms,
machine learning, statistical analysis,
sentiment analysis, semantic analysis, and
other complex methods to provide insight.
But there are challenges and many things
to lead you astray. . .
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11. © 2016, Future of Talent Institute
Disruptive Technologies
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Internet
Social
Mobile
Cloud
Big Data - Analytics
Technology Foundation
Trends & Innovations
Internet of Things
Robotics
Disruptive Scenarios
Passive candidate assessment
Algorithms Automate Recruiting
Intelligent Personal Agent
Ultrasonic Tracking
Predictive Analytics
DNA Analysis/Assessment
Virtual/Augmented Reality
Chatbots
Biometric Assessment
Life Span
Blockchain
Supply Chains
Contingent Workers
Climate Change
Decline of Nation State
Urbanization
Emerging Economies

12. © 2016, Future of Talent Institute
Data Does Not Tell a Story
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Data by itself takes no
position and holds no bias.
Biases & other issues only
occur when we interpret it,
look for predictions, use it
to make decisions,.

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The Many Perils, Traps and Biases
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Assumptions
Predictions based on proxies
Not questioning
The future = the past
Privacy
Black boxes
Using data without
candidates knowledge
Lack of guidelines
Basics
No clear problem statement
Predicting what has no impact
Structural
Lack of clean data
Sample size too small
Too simplistic/too complex
models
Biases
Unintentional/Inherent
Favoring one set of data over
another

14. © 2016, Future of Talent Institute
What do you want to predict?
What is the problem you want to solve?
Do you have the right data?
Do we have enough data?
Do you have enough relevant data?
How do we prevent diversity issues?
What is a quality hire?
How do we define effective?
What’s in the algorithm?
Weapons of Conformity & Discrimination?
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Only 14% of organizations have data to
prove the positive business impact of
their assessment strategy. -Aberdeen 2014

15. © 2016, Future of Talent Institute
Assumptions
What are we assuming as we search?
How valid are our search criteria? How do you know?
Scoring algorithms
What is in the algorithm?
How do we know it is actually measuring what we think it is?
Is it discriminating?
Is it fostering clones?
How do we introduce diverse thinking?
Sourcing
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16. © 2016, Future of Talent Institute
Is a Facebook/LinkedIn profile accurate?
Can they predict ability, skill, or job performance?
Using Facebook or LinkedIn
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“. . .researchers hired HR types to rate hundreds of
college students’ Facebook pages according to how
employable they seemed.
. . .the over 500 guinea pigs, just 56 of the employers
responded. So the sample is small, but the researchers
found a strong correlation between those employers’
reviews and the employability predictions they had
made based on folks’ profile pages.”

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Check out a candidate’s social media profiles – even
informally
Use any of the social media info to influence your
opinion of a candidate
Do not let a candidate know if you looked
Do not let them know what you looked for
Social Media & Privacy
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Potential legal problems if you. . .

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What are the criteria?
Does personality testing correlate with performance?
Do we want everyone the same?
Assessment
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The personality test is a black box, and
it’s not clear what it is actually
assessing, and whether using it
constitutes discriminatory hiring
practices.

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What are the criteria?
What is the context?
When and to who was the comment made?
Sentiment Analysis
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“Even in the best of circumstances,
[sentiment analysis] is only 65% to 70%
accurate.”
-Susan Etlinger, analyst Altimeter Group

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Matching algorithms
Accuracy? Relevance? Privacy? Discrimination?
Passive Assessment
Privacy? Relevance? Accuracy?
Chatbots
Assumptions? Discrimination?
Online Assessments/games
Privacy? Relevance? Correlation=Causation?
A Few Emerging Tools
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How transparent are the vendors about their
algorithms and assumptions?

21. © 2016, Future of Talent Institute
Could We Automate Selection &
Assessment? Should We?
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People are complex, contradictory,
and so varied that even complex tools
and algorithms are rarely accurate.
We will continue to need human
judgement and tolerance.

22. © 2016, Future of Talent Institute
Most of the tools we are using today create as many
questions as answers.
No single test can predict anything with high certainty.
Many tools use proxies, which may not represent
reality.
We tend to validate a tool when it confirms our
suspicions.
Sample sizes are often way too tiny to be valid.
There is inherent bias in almost all screening.
People are highly complex and there are no simple
ways to say one person is better than another.
Unfortunately. . .
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23. © 2016, Future of Talent Institute
PA can provide insight and validate or disprove
assumptions.
Can augment human judgement.
Valuable when used responsibly following openness
guidelines.
Can provide early warning that employees are unhappy
or are thinking about leaving.
Can identify competencies and skills and predict their
value to a particular role.
Predictive Analytics
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24. © 2016, Future of Talent Institute
Have a privacy disclosure policy that is shared with
candidates and is on your career site.
Let candidates know why you have rejected them,
especially if based on social media information.
Have clear definitions of what you are looking for and
how you will know when you find it.
Train recruiters in what is acceptable, reliable, and
accurate.
Always use human judgement along with any AI or
other assessment or prediction.
Good Predictive Analytics Practices
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25. © 2016, Future of Talent Institute
. . .use more than one test or predictive tool.
. . .when choosing a tool, know what is in the
algorithm. Know what is being looked for, tested,
analyzed, and how each factor is weighted.
. . .make sure proxies are valid and really predictive.
. . .not adopt tools hastily and without careful thought
and knowledge.
. . .always question our own assumptions and beliefs.
We should. . .
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26. © 2016, Future of Talent Institute
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THANKS
YOUR THOUGHTS & QUESTIONS
Follow me on Twitter: @kwheeler
Email: kwheeler@futureoftalent.org