The document discusses the importance of talent analytics in optimizing recruiting processes, emphasizing the need for predictive analytics and data integrity. It highlights challenges such as relying on outdated metrics and the lack of standardized data collection methods, along with strategies for improving recruitment efficiency and quality through data-driven decision-making. The document also explores various performance metrics and their implications for hiring practices, suggesting that effective storytelling with data can significantly influence talent acquisition success.

1. Talent Analytics.
Maximizing the
TA Value
“After my wife, Data is my best friend

2. Current State – Rearview Mirror Metrics
• Discussion
Agenda
Future State - Predictive analytics and causality metrics
• Discussion
Telling Stories with the metrics
• Discussion
Optimal scorecards, reporting and KPI’s
• Discussion
Roadblocks to success - Data Integrity and how to fix it
• Discussion
Benchmarking with real data – Brightfield Tool
• Discussion

3. “Without data, you are
blind and deaf and in the
middle of a freeway.”
– Geoffrey Moore

4. Let Me Tell You A Story
My Career
A-ha Moment!

5. My Career
A-ha Moment!
Facts = Data…
Data = Credibility…
Credibility = Trust…
Trust = Partnership

6. Current State – Rearview
Mirror Metrics
• Discussion
Agenda

7. Current State
Most Recruiting Metrics
are still about looking in
the rear view mirror

9. Data & Analytics
We
do
this
today
We think we
need to but
are not quite
sure how,
why or the
value
We see value
and plan on
doing in the
next 18
months
We see no
value and
are not
going to
adopt

10. Data & Analytics
We do
this
today
We think we need
to but are not quite
sure how, why or
the value
We see value and
plan on doing in the
next 18 months
We see no value
and are not going
to adopt
We currently use an
analytics solution
19% 23% 52% 6%
Data & Analytics

11. Data & Analytics
We do
this
today
We think we need
to but are not quite
sure how, why or
the value
We see value and
plan on doing in the
next 18 months
We see no value
and are not going
to adopt
We currently use an analytics solution 19% 23% 52% 6%
We have moved
beyond basic data
reporting
12% 29% 48% 11%
Data & Analytics

12. Data & Analytics
We do
this
today
We think we need
to but are not quite
sure how, why or
the value
We see value and
plan on doing in the
next 18 months
We see no value
and are not
going to adopt
We currently use an analytics solution 19% 23% 52% 6%
We have moved beyond basic data
reporting
12% 29% 48% 11%
We have a formal
dashboard
31% 16% 43% 10%
Data & Analytics

13. Data & Analytics
We do
this
today
We think we need
to but are not quite
sure how, why or
the value
We see value and
plan on doing in the
next 18 months
We see no value
and are not
going to adopt
We currently use an analytics solution 19% 23% 52% 6%
We have moved beyond basic data
reporting
12% 29% 48% 11%
We have a formal dashboard 31% 16% 43% 10%
We benchmark our
KPIs
29% 19% 39% 13%
Data & Analytics

14. Data & Analytics
We do
this
today
We think we need
to but are not quite
sure how, why or
the value
We see value and
plan on doing in the
next 18 months
We see no value
and are not
going to adopt
We currently use an analytics solution 19% 23% 52% 6%
We have moved beyond basic data
reporting
12% 29% 48% 11%
We have a formal dashboard 31% 16% 43% 10%
We benchmark our KPIs 29% 19% 39% 13%
We have a dedicated
resource
26% 26 % 28% 20%
Data & Analytics

15. Reporting Analytics and
KPIs….What Doesn’t get
Tracked or Measured

16. 32%
CPH
31%
Diverse Hires
31%
Hiring against
Demand Plans
28%
TA KPI Scorecard
>40%
Retail
>40%
Manufacturing
Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured
Q: What Doesn’t
get Tracked or
Measured

17. 32%
CPH
31%
Diverse Hires
31%
Hiring against
Demand Plans
31%
Funnel Metrics
40%
HM Satisfaction
34%
Sourcing Time vs
Business Time
28%
TA KPI Scorecard
Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured

18. 32%
CPH
31%
Diverse Hires
31%
Hiring against
Demand Plans
43%
Social Media
Engagement
31%
Funnel Metrics
40%
HM Satisfaction
34%
Sourcing Time vs
Business Time
28%
TA KPI Scorecard
44%
Candidate
Satisfaction
Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured

19. 32%
CPH
31%
Diverse Hires
31%
Hiring against
Demand Plans
43%
Social Media
Engagement
31%
Funnel Metrics
40%
HM Satisfaction
34%
Sourcing Time vs
Business Time
28%
TA KPI Scorecard
44%
Candidate
Satisfaction
46%
Quality of Hire
Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured

20. A Staffing.org CEO Survey rated new hire
quality as the #1 most important
performance metric
out of 20 possible metrics. It was rated
9.6/10

21. Quality of Hire (QoH) = (APR + AE + HMS + ER) / N
APR = Avg. Performance Rating for new employees in first 12 months
AE = Employee Performance as a % of Achieves Expectations of
performance in first year.
HMS = Annual Hiring Manager Survey Q:“Overall quality of New Hires”
ER = % of Employee Retention first 12 months of employment.
N = Number of indicators used.
APR= 68% + AE= 94% + HMS= 80% + ER= 90% / N = 4
QoH = 83%

23. QoH

24. Data Compression & Perception
Highest = 83%
Lowest = 62%
Performance Management
New
Hires
QoH

25. 25
Retention/Attrition
Performance Promotions Business Satisfaction
1
2
3
4
Promotions

26. Business
Accountability
Recruiter
Accountability
Biggest lesson learned?

27. Number of candidates submitted to the business that
they accept as a %
(Recruiter Accountability)
+
% of candidates employed (Retention) in their first
12 months of employment
(Business Accountability)
divided by these two data points.
1
2

28. 1,000 Submittals
800 Acceptances
80%
First Year Retention
90%
+
Two Data Points (80% & 90%)
= 85% First Year Quality (FYQ)

29. Discussion
What other metrics are you tracking and why?
Does the business want metrics that you can’t provide, and why not?

30. Agenda
Future State - Predictive analytics and
causality metrics
• Discussion

31. Future State
Predictive analytics is
the practice of
extracting information
from existing data
sets in order to
determine patterns
and predict future
outcomes and trends

32. Profile ‘A’ Pipeline Robustness
Recruiter
Screen
Business
Interview 1
Business
Interview 2
Offer Hire
100
90
80
70
60
50
40
30
20
10
10:1
7:1
4:1
2:1
1:1
Profile ‘A’ Historical
Throughput
Benchmark
Target
# of
Candidates
Actual
Candidates

33. 100:1
30:1
10:1
8:1
3:1
1:1
Full Funnel Throughput (FFT)
Applications
Recruiter Screens
Hire
HM Accepts
Final Interviews
Submittals

34. 100:1
30:1
10:1
8:1
3:1
1:1
Full Funnel Throughput (FFT)
Tele-Sales
Java Developers
Job Families
Store Mgr’s
55:1
30:1
100:1

35. 100:1
30:1
10:1
8:1
3:1
1:1
Alert
20 more Quality
Candidates needed
this week to fill the 5
Tele-Sales positions by
end of the month
Full Funnel Throughput (FFT)

36. Predicting Which Reqs Will Be at Risk
• Chance of the req being
filled by its goal drops
dramatically if there is no
candidate submission by
the end of week two or
no candidate interview by
week three.

38. - Approach to Role Difficulty

39. Speed
Quality
Cost
Req Load
Predictive Metric
Causality
Example
Better Quality
impacts longer
hiring times and
increases cost

42. Discussion
What other predictive TA metrics are you tracking?
What predictive or causality metrics would you love to get your hands on?

43. Agenda
Telling Stories with the
metrics
• Discussion

44. Telling Stories with Data
“Stories are the single most powerful
weapon in a leader’s arsenal.
—Howard Gardner, Harvard University
“Why was Solomon recognized as the
wisest man in the world? Because he knew
more stories (proverbs) than anyone else.
Scratch the surface in a typical boardroom
and we’re all just cavemen with briefcases,
hungry for a wise person to tell us stories.
—Alan Kay, vice president at Walt Disney

45. The most important TA metrics:
• Does it make me money?
• Does is save me money?
Show me the money

46. Rewire Your
Brain To
Recognize
The
$$$$ Stories

47. Business Executive:
“It costs too much money to hire people !

48. Business Executive:
“It costs too much money to hire people !
Talent Acquisition Executive:
“Compared to what?

49. Back of the napkin math stories
Example: Hiring Time and Investment
1. We hire 5,000 people a year.
2. At an average of 5:1 (Candidates interviewed to produce one
hire).
3. We have 5 different business interviewers involved on average
for each candidate going through the interview process.
4. Each business interview on average lasts 60 minutes (early
screens a little less, later interviews potentially more), but lets
be conservative and call it 30 minutes on average.
5. The average fully loaded hourly rate for a business interviewer
is $70, but even being conservative here as well, lets say 30
minute interviews. So that's $35 per hour.
6. 1 candidate gets 5 business interviews x 5 candidates per req =
25 interviews per req.

50. 5,000 hires (reqs) x 25 interviews on average per req = 125,000
interviews a year.
125,000 x $35 per hour = $4,375,000 in time spent interviewing.
If we (Recruiting and Business) could improve our throughput ratio
down to 4:1 then the financial impact is a $875,000 less.
If we (Business and HR) could create a more effective interviewing
and assessment framework which required 1 less business
interviewer on average, then that is an additional $700,000 in
savings as well.
If we could give the business back 22 thousand interview hours
going forward, what would you do with them?

53. 1. Problem we/you are trying to solve
2. Benefit we will get from solving this problem
3. How you are progressing against the plan to solve it (on
track/off track)
4. The issues causing you to be off track
5. What are you doing about resolving the issues that get you
back on track, and by when
5 Simple Story Telling Rules

54. Look for the Data Outliers
Best Practice
Opportunity to
share, discuss and
apply elsewhere?
Challenge or
Opportunity to
discuss, fix and
improve?

55. Contrast and Compare
= Bad = Good
Golden Rule No. 1 Don’t compare datasets that are not directionally the same.
Example: Your Companies Cost Per Hire to your Competitors if you/they calculate differently.
Golden Rule No. 2 Wherever possible compare your dataset to something else. A metric and
data point standing alone by itself tells very little.
Example: Compare your Cost Per Hire this year/month with last year/month.
The Story
‘Did it go up or go down, why and what are you doing about it’?

56. Different Analytical Recruitment Stories
Cost
 Cost Per Hire comparisons and outliers vs. other Workforces/Job Families
 ROI on different Sourcing Channels and Outliers
 Proactive vs. Reactive Sourcing results and Outliers vs. other Workforces/Job Families
 Lost Opportunity Cost = Financial impact for unfilled roles
Speed
 Time to Hire comparisons and outliers vs. other Workforces/Job Families
 Time to Source (Recruiting) vs. Interview Time to Hire (Business) and outliers by
Workforces/Job Families = RvB Metric.
Quality
 New Hire Performance & New Hire Managed Attrition (1st 12 months)
 Offer Acceptance and outliers over time vs. other Workforces/Job Families
 Productivity Throughput Ratio’s (Submits to Hire) vs. other Workforces/Job Families
+ Operational Effectiveness
 Hires against a annual or monthly business/recruiting demand plan
 Tracking against Recruitment Goals/KPI’s
 Volume of recruiting resources/effort spent on Attrition vs. Growth roles
Against Plan
Against Time & %
Against Goals

57. Story
“We need to do x,y
and z to increase the
passive candidate
initial response %”
“We need to do x,y
and z to help with
efficiencies around
active candidate
screening to
determine quality
earlier on in the
process”
Throughput Analysis: ‘Cradle to Grave Metrics’

58. Target Company Throughput
Target
Company
Total Candidates in
database Total Hires
% We reject
Candidates
% Candidates
reject us
% of hires to
applications
A 337 21 67% 9% (16:1)
B 222 13 57% 8% (17:1)
C 135 13 70% 8% (17:1)
D 533 16 71% 10% (33:1)
E 351 8 74% 7% (47:1)
F 64 1 80% 1% (64:1)
Story
“Business says look at people from Company ‘F’ but
the data does not support the value”
“We found people coming from Company ‘A’ are more
successful because of ‘x,y and z’. This must be our
broad assessment criteria vs. just Target companies?
Negative Disposition Trends Story
“Can we improve our EVP to move the
60% rejection reason’s down?”
“Can we look at more flexible travel
arrangements for this profile?”
“What additional relationship
development programs can we put in
place to keep connected to the
interested but timing not quite right
group?”

59. Comparative Source Analysis
Agency vs. Job Board Hire Overlap
Story
“Opportunity to get more
effective with our own
Sourcing Channel Strategy
and coverage to optimize
costs and results”
Comparative Source Analysis
Job Board vs. Job Board Overlap Story
“Certain jobs should only be posted on Job
Board ‘X’”
“Stop spending Business Group ‘C’ money
on Job Board ‘X’ and reinvest elsewhere.”

60. Lost Opportunity Cost Stories
60
Creating a Scenario in our
WFP tool, a positive shift
in attrition by 4.2%
Positively impacts
Company Revenue by
3.2% (40million)
Modeled Forecasting

61. Talent
Mapping
Stories

62. The Future is Big Data Stories
ATS
SEO
CRM
Performance
Management
HRIS
Big
Data
Sourcing
Tools
Social
Networks
WFP
Multi-dimensional
Recruitment Stories

63. Discussion
What are creative ways you have leveraged
data to tell the story for change?
What are the major roadblocks you have seen on enabling change with
data?

64. Agenda
Optimal scorecards,
reporting and KPI’s
• Discussion

65. Lots of pretty choices

66. April: Talent Acquisition Monthly Scorecard
Status
Enhanced employee referral program go live in Q2
SEO solution delayed to Q4 because of contract
negotiations
Productivity per Recruiter increases after training
investment last year
Technology Blueprint and SOW completed for new
global ATS
Key Performance Indicators Goal/Plan Actual Trending
Speed 50 days
Cost 2.5k
Quality 78%
Customer Satisfaction 80%+
Challenges
FY Initiatives / Investments
+ to Goal < 10% of Goal > 10% of Goal
• Speed = Core Technical roles in Texas and Germany
problematic. Too many interviews and 2 groups extended
delays in responding to TA.
• Quality = First year attrition issues in sales roles,
particularly in the Solutions Group
• Customer Satisfaction = While above goal by 4% the Sales
Organization is causing trending down.
Opportunities & Plan
• Speed = Working with SVP on plan to optimize interviews and
reduce the decision time. Meeting scheduled 4/5
• Quality = Partnering with Talent Management & Business to
create better interviewing/assessment guides. Additional
onboarding tweaks being made by Talent Management.
Targeting Q3 release.
• Customer Satisfaction = Keeping an eye on this and setting
expectation with leadership that we will not be trending up
until after Sales issues resolved after Q3
On Track
Off Track
On Track
On Track

67. Hiring Manager and Candidate Satisfaction
67

68. Transparency and Directional Correctness
68
Transparency
1. Be clear on how and where you collect your data
source’s.
2. Be clear on why you are measuring and the
benefit you are after by presenting the data.
Directional Correctness
1. 100% Data accuracy is very hard to achieve. Pick
datasets that are directionally correct.
2. In instances where the data is more opinionated vs.
factually driven, make sure the message focuses on
the directional story vs. just the numbers

69. Discussion
What scorecard approaches have you found
create the greatest impact?
What KPI’s do you use to hold the business, HRBP’s and Vendors
accountable?

70. Agenda
Roadblocks to success - Data
Integrity and how to fix it
• Discussion

71. Roadblocks to success

72. Metrics Standardization

73. Still
challenges
with how
recruiters
use their
ATS.

74. - Still multiple versions of the Truth
- Companies all over the map with how they use
ATS’s (or Don’t)
- Some ATS’s are
just plain useless in
their functionality

75. Pivot Tables can be evil

76. What can you do to fix it?

77. “We also had a major problem with data accuracy because recruiters
were using our system inconsistently.
“First, we spent a month scrubbing our historical data,
removing errors and compiling a sample of “clean jobs” with
accurate metrics.
“Second, we taught recruiters to “live and breathe” our
applicant tracking system by holding mandatory, weekly job
reviews for 3 months. Recruiters sat in a room for an hour and
we would audit one job per recruiter (recruiters wouldn’t know
which job until the meeting).

78. Correct
ATS step
& status
workflow

79.  Goal and reward the right behaviors
 Be Transparent – Create a data integrity
report/scorecard
 Create one version of the truth

80. Discussion
What other approaches have you taken to fix people, process
or technology dependencies associated to data integrity?
Do you currently have a data integrity metric or KPI?, if no, why not?

81. Suggested Reference Materials
• CareerXroads Colloquium of course 
• David Green – People Analytics IBM
• https://www.linkedin.com/in/davidrgreen/detail/recent-activity/posts/
• Your Intelligent Talent Acquisition Advisor Blog
• https://intelligentta.wordpress.com/
• Analytics in HR Blog
• https://www.analyticsinhr.com/
• Tucana’s Podcast series
• https://tucana-global.com/category/podcast/
• hiQ Labs Podcast
• https://www.hiqlabs.com/podcast/
Conferences:
• Wharton’s People Analytics Conference
• https://wpa.wharton.upenn.edu/conference/
• People Analytics World
• https://tucana-global.com/people-analytics-world-2017/

82. Agenda
Benchmarking with real data –
Brightfield Tool
• Discussion

84. End-user / Employer User Types
 CW/TA Program Team Users – Routine operational
benchmark look-ups
 CW/TA Program Owners – Trend exploration,
taxonomy improvement, strategy testing, overall
function optimization
 SWP Finance/HR Professionals – Worker type
comparisons & optimization, location selection
Talent Data Exchange (TDX)
workforce analytics platform
84
Supplier / Support User Types
 MSP/RPO Operations Professionals – Trend
exploration, taxonomy improvement, strategy
testing
 VMS/ATS Implementation & Product Teams –
embedded market benchmarks, conditional
application behavior
 Brightfield Consultants – efficiency & efficacy

85. 85

87. Live Demo of Talent Data Exchange (TDX)