A review of Talent Acquisition reporting

1. TA Reporting,
Metrics and
Analytics
Catherine Parker
September 2020

2. Agenda
Use of Data
Talent Acquisition Analytics
Telling Stories with Data
Scorecards, Reporting & KPIs

3. Use of Data in TA

4. Facts = Data
Data = Credibility
Credibility = Trust
Trust = Partnership
Achieving true partnership with key stakeholders across the business
is the “holy grail” in talent acquisition

5. Why Use Analytics?
Drives more efficient talent acquisition processes
Improves quality of hire
Utilizes time more effectively and enables a faster time-to-
market
Enables advanced decision making which leads to better
outcomes

6. Laying the
Foundation
for
Analytics
• Communicate and demonstrate the significant ROI
that can be achieved through solid analytics
• Build a skilled data function with analytical, data
visualization and consulting skills
• Leadership Buy-In is crucial as the function involves
an investment of time, staff and budget
• Develop an Analytics Roadmap for the range of
analytics to be tracked and measured
• Enhance Analytics Literacy through training to
prepare key stakeholders to grasp complex data
points
• Avoid GIGO and ensure clean data…“Garbage In
Garbage Out” leads to the wrong outcome and
circulates flawed data
• Establish Data Strategy that aligns/integrates data
from multiple sources across the organization

7. Impact
Business Outcomes
TA Influence on Business Outcomes
Critical Role Hiring
Staffing versus Requirements
Hiring against Target
Improved workforce planning
Efficiency
Resource utilization, process results
Time/Cost/Source
Time to Fill
Cost Per Hire
Source Mix of Hires
Effectiveness
Talent Outcomes
Quality of Candidates/Hires
Quality of Candidates/Hires
Talent Community
New Hire Performance
Retention
Speed to Competency
Key Measures
Key Analytics
REPORTING
Look Back At Results
What happened? How many/long?
How do we compare? What works best?
Look for Patterns to Predict Outcomes
What is likely to happen? What tactics most influence
TA/HR/Business outcomes? What should we do next? Outcomes
ANALYTICS
Provides Information
Focus is on Past Performance
Provides Insights & Answers
Provides what is Needed
Focus is on the Future
Predictive PrescriptiveDiagnosticDescriptive
Framework
Business ROI from Analytics

8. Talent Acquisition Analytics

9. Descriptive What Happened?
Comprehensive, real time data
Diagnostic Why did it Happen?
Ability to drive down to the root cause
Predictive What is likely to Happen?
Risk analysis and mitigation, scenario
planning
Prescriptive What do I need to do?
Recommended outcomes based on
champion/challenger testing strategy
outcomes
Four Types of TA Analytics
What the data is telling you…
Diagnostic
Prescriptive
Predictive
Value
Complexity
Descriptive

10. Descriptive Analytics
• Descriptive analytics is
the most basic type of
analytics, simply taking
historical data and
summarizing it into
something that is
understandable.
• Descriptive analytics
focuses squarely on the
past, explaining why
something already
happened. It is solely
reactive.

11. Examples of Descriptive Analytics
• Source of hire
• Time to hire
• Applicants per hire
• Cost per hire
• Acceptance rate

12. Diagnostic Analytics
• Diagnostic analytics
examines data or
content to answer the
question, “Why did it
happen?”
• It is characterized by
techniques such as
drill-down, data
discovery, data
mining and
correlations.

13. Examples of Diagnostic Analytics
• Diagnostic Analytics: Focuses on why did it happen? It takes
a deep dive at the data to understand the causes of events
and behaviors.
• Examples:
• Quality of Hire - looking at turnover within a 12-month time frame helps the
TA team determine their level of success in finding the right candidates
• Employee turnover - identifying the type of separations voluntary vs
Involuntary from multiple perspectives including region, division, location,
and manager enables the TA team to identify areas of concern and work to
address them accordingly

14. Predictive Analytics
• Predictive Analytics is
the use of data,
statistics, machine
learning and modeling
techniques to advise
recruiting strategies,
hiring decisions and
workforce planning
• It not only draws
conclusions from past
performance that helps
depict, visualize and
read data-patterns to
build strategies for the
future

15. Examples of
Predictive
Analytics
1. Quality of Hire can be directly impacted by the
adoption of predictive analytics.
• Recruitment data plus HR analytics
(performance, attrition, employee lifecycle,
engagement survey feedback) can develop
patterns beneficial to the TA team – i.e. hiring
channels, KPIs
2. Sourcing can be optimized. Ineffective sources
can be eliminated with job boards, vendors, and
in-house recruiters evaluated against their
success rate
3. Faster and targeted hiring can be achieved with
the adoption of AI tools that rapidly assess large
numbers of applicants, thus reducing time spent
sourcing and increasing time with qualified
candidates

16. Prescriptive Analytics
• Prescriptive analytics is an
extension of predictive
analytics and provides
next step
recommendations based
on past performance and
future predictions
• Prescriptive analytics is
largely achieved via
computerized modelling
exercises using multiple
variables – market data,
trends, etc. to recommend
the best course of action

17. Examples of
Prescriptive Analytics
• Knowing where to act and how to move in a specific way
is prescriptive analytics. Few companies have this today.
• Possibilities are evolving for prescriptive analytics in
talent acquisition, it could help predict learning paths for
employees, helping to extend their tenure at an
organization,
• Using prescriptive analytics, Recruiters could anticipate
which candidates might not show up to scheduled
interviews, or don’t arrive on the first day of work –
known as a candidate’s joining probability – and get a
prescribed solution to fix the situation.

18. Challenges –
Prescriptive
Analytics
As prescriptive analytics models enter the TA world, what are the
implementation challenges?
• Price. Most solutions are targeted and priced for larger companies.
• Talent. Your analytics teams might not have the right staff to
implement this solution. Many companies are still struggling to adopt
predictive analytics.
• Buy-In. Organizational buy-in is crucial, and many companies are not
prepared for these kinds off offerings.

19. To be Proactive
Ask…Can technology do this for me? …and Use Deep Learning
Analytics Mapped to Technology
Analytics Stage Question Associated Technology
Descriptive What Happened? Quantitative
Diagnostic Why did it Happen? Qualitative
Predictive What will Happen Next? Statistical
Prescriptive What do we need to do? Machine Learning
Potential Providers
Crunchr
Contino
Looker

20. Telling Stories
with Data

21. Storytelling and Data
Visualization
• Telling a story with data gives it
meaning. Data storytellers begin
with establishing their audience
and key messages:
• What am I trying to achieve
with the data displayed?
• Who is my audience? What
do they care about? What
level of data detail will they
likely expect or appreciate?
• What am I trying to
communicate-- the one
thing I want my audience
to know or do with my
data?
• Storytellers also decide: "Is the
data I am choosing moving
forward the story I want to tell?"

22. Data storytelling is the marriage of Right Brain artistry combined
with the analytical fluency of the Left Brain.
Left Brain
Logic
Numbers
Language
Right Brain
Symbol
Image
Colour
Data
Visualization
versus
Storytelling

23. Impact from
Data
Visualization
• Accelerates change
• Inspires people
• Influences decision making
• Drives creativity
• Develops Trust

24. Key Elements of Great Storytelling
• Storytelling connects people and
builds trust in business. Connection
and trust are intangible, felt
experiences yet, they have a
tangible impact. They lead to
confident decision-making and
action.
• Data is complicated and your
audience needs guidance -
storytelling provides them form,
chronology and simplicity.

25. Dashboard & Scorecards

26. Determining the right
reports for the right
stakeholders… Are they looking for a detailed snapshot? Tactical Data
Do they want to see aggregated high level trends? Strategic KPIs

27. Use of Data
Data Must provide insights, not noise
Insightful reports should be used to
• Understand the health of the function
• Communicate progress
• Diagnose bottlenecks
• Drive decision-making
• Predict future performance
3 Questions
• Who is the Audience?
• How often is the Cadence of your reporting?
• What do they want to know - Strategic or Tactical?

28. Scorecards Versus Dashboards
Strategic – focused on long-term decision making
Represents trends/changes in activity over time
Supported by clearly defined strategy
Changes in performance measured against
business goals
Real time monitoring tool
Operationally focused, supported by individual
managers
Change in performance evaluated by primary
stakeholders
Tactical – focused on short term decision making
Dashboards offer a broad way to track
strategic goals and measure talent
acquisitions overall efficiency.
Scorecards provide a quick and concise
way to measure KPIs and give a clear
indication of how well TA is working to
achieve their targets

29. 4 Criteria to Enable Data-Driven Decisions
Relevance
Align to what
matters the most,
such as quality of
hires
Comparative
They should be
comparable to
time periods,
sites, segment, or
peers. i.e.,
comparing % of
referrals vs.
previous time
period
Rate or ratio
Examples include
applicants per day
during a specific
length of time
Actionable
Choose metrics
that will directly
influence future
behavior
Measure the results Benchmark the results Assess Reasons Take Action

30. Example of a Monthly Scorecard

31. Example of a Dashboard

32. In Conclusion
3 Key Points
1. Understand your starting line. If you aren’t
running rudimentary metrics, perhaps
jumping straight to sophisticated analytics
isn’t the right strategy. Plot where you are
and where you need to be..how will you get
there?
2. Create expectations for optimized analytics
at every level. Once the baseline is
evaluated, you can move into a more
mature, best-practice stage that looks at
more complex HR/TA issues.
3. Establishing expectations is key but moving
toward more optimized metrics also
requires resources. Ensuring support from
the top down, securing budget, headcount
and followership is integral to the overall
success of your efforts.