This document discusses forward-looking and predictive metrics that can be used for recruiting. It begins by defining key terms like historical, real-time, and predictive metrics. It then discusses reasons for using traditional metrics, such as increased business results when data-driven decision making is used. Examples of predictive recruiting metrics are also provided, such as predicting changing source effectiveness and upcoming talent availability. The document concludes by outlining elements that make a predictive metric actionable, such as listing revenue impact and recommended actions.

1. Forward-Looking And Predictive Metrics
For Recruiting
Presented by:
Dr. John Sullivan
Sponsored
by:

2. Click to to ed ite Mdaits tMera tistlete srty tleit le style
October 9, 2014
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6. FORWARD-LOOKING
AND PREDICTIVE
METRICS FOR
RECRUITING
The next big thing in Talent Acquisition
SourceCon Webinar - October 9, 2014
© Dr John Sullivan
www.drjohnsullivan.com or JohnS@sfsu.edu
V1 53

7. 2
I’m from the Silicon Valley…
So they asked me to highlight the most advanced
predictive metric practices in the world
Obviously you can’t adopt all of the leading-edge
metrics, but you should still be aware of them
So pick, choose and adapt whatever elements that
you find are best for your firm

8. 3
Topics we will cover today…
1. Introduction a nd some definitions
2. Reasons for using traditional metrics
3. Reasons for using predictive metrics
4. Examples of predictive metrics in
recruiting
5. Implementing predictive metrics

9. Part I
An introduction and some quick metric
4
related definitions

10. 5
Everyone else already “lives” metrics
“In God we trust, everything else we measure”
--VP of HR, UPS

11. 6
Some quick simplified definitions
1. Historical metrics – measures that focus on
reporting things that have already occurred
2. Workforce Analytics – a set of integrated
capabilities to measure, analyze, identify trends
and to improve workforce performance
3. Real-time metrics – reporting or monitoring
metrics that cover what is happening now
4. Predictive metrics – using past and current data to
forecast trends and upcoming problems/opportunities
5. “Why” analysis – a process for identifying what
causes things to happen (the root causes)
6. A scorecard – results listed on 1 sheet (Balanced)

12. 7
Some quick simplified definitions
7. A dashboard – an array of metrics, that in a single
view, covers all of the key functional measures
8. An index – numerous metrics converted to a single
number for ease of comparison (i.e. Dow Jones)
9. Business Intelligence (BI) – the executive term for
information for improving business decisions
10. Showing revenue impact – converting standard
HR metrics to their dollar impact on revenue
11. Big data – huge, changing and complex data
sets that can’t be easily analyzed using traditional
software

13. 8
Predictive analytics
Predictive analytics are more common outside of HR
Ø Weather forecasting – government and private
weather services predict weather patterns that will
create problems and provide business opportunities
Ø Predictive policing – many modern departments now
have algorithms and models that can predict where
and when crimes are likely to occur
Ø Insurance algorithms – all major insurance firms
have predictive models that forecast health problems
for segments of the population
Ø Supply chain/consumer functions - within the
business, these functions excel at predictive analytics
Ø Politics – Nate Silver proved their value here

14. Illustrations of data based recruiting
compared with traditional recruiting
TRADITIONAL TA
DATA DRIVEN TA
I know
Real-time/ predictive metrics
I test new ways with data
I have data/ analysis to prove it
Our firm’s data shows it works
Data / facts speak the loudest
Informed people decide
Failure analysis stops repeating
Productivity/bus goal advocate
9
I think, I believe or I feel
Use only historical metrics
I rely on past practices
My gut / heart tells me
I once saw this program fail
Political power triumphs
A consensus decides
We accept failure
An employee advocate

15. 10
Part II
Let’s highlight…
Reasons for using traditional metrics

16. #1 - The use of analytics increases business results
11
(source: Harvard Business Review)
A dvanced user firms that most effectively managed
their workforce… using analytics… produced
higher business performance %’s in these key areas
% increase in performance by advanced users

17. 12
#2 – Most business functions have already shifted
to data-based decision-making
% that are advanced users % that are non-users
Finance 58% 7%
Executive team 51% 11%
Operations 48% 9%
R&D 44% 23%
Marketing 41% 16%
Sales 34% 20%
HR 27% 23%
Learning: compared to HR, Finance has 2X the
advanced users and 3x fewer nonusers
Source: AMA/i4cp 2013

18. #3 – Much TA gut decision-making may be wrong
Google… is the benchmark data-based firm
Ø “We want people management decisions to reach
the level of engineering decisions” (People analytics group)
Ø “Part of the challenge with leadership is that it’s
very driven by gut instinct…
and even worse, everyone thinks they’re really
good at it” (hiring)
Ø Google used data to eliminate any “gut instinct”
decisions… on the hiring criteria that best
predict success on-the-job
13

19. These Google data points…
might change what you think you know about hiring
Ø “GPA’s
Ø “Test scores
Ø “Brainteasers
Ø Interviews – “many managers, recruiters, and HR
staffers think they have a special ability to sniff
out talent. They’re wrong”
“it’s a complete random mess”… “we found a
zero relationship” (between interview scores and on-the-job performance)
No value is added “after 4 interviews”
Ø College –“the proportion of people at Google
without any college education… has increased over time”
Ø What predicts across all jobs? – “learning ability”
14
are worthless as a criteria for hiring”
are worthless”
are a complete waste of time”
Laszlo Bock, Senior VP of people operations at Google The New York Times

20. Reason #4 - Metrics influence managers to change
Ø “The best thing about using data to influence
managers is… it’s hard for them to contest it.
For most people, just knowing that information…
causes them to change their conduct” (Google)
15
Ø Analytics are a must at an engineering-focused
company…
“appealing to emotions instead of logic "is not
going to work –
You need data" (Tesla)

21. #5 -Metrics reveal which programs have the
highest business impacts (BCG)
Which TM functions have the highest business impact?
Source: BCG/WFPMA - From Capability to Profitability: Realizing the Value of People Management, 2012 16

22. #6 – Metrics reveal what is working / not working
What source produces the best applicants / hires?
Ø Although only 7% of applicants come from
referrals, they produce 40% of all hires
Ø Between 14% to 25% of referrals are hired…vs.
1% from among all sources combined
Ø Referrals have the highest interview to hire ratio
(17% of referrals are interviewed)
Hiring speed - they have the fastest “time to fill” of
all sources, a 48 % lower time to fill (29 compared to 45 days)
Hiring cost – 58 % lower than other sources
Quality of hire – referrals are ranked #1
17

23. #7 - Realize that HR is not very good at analytics
What % of CEO’s are confident in the quality
of Human Capital metrics?
18
What would be an ugly%?
Source: AICPA survey 12%

24. Of the 13 major areas of HR performance…
19
where do metrics rank? (KPMG)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10
.
11
.
#12 & #13

25. 20
Part III
Now let’s shift to…
The reasons for using
predictive HR metrics

26. 21
What’s wrong with HR metrics?
Unfortunately, reporting “yesterday’s results”
adds little value
Almost all HR metrics report history, because they
tell you what happened last quarter or even last
year… which may be irrelevant in a fast-moving
world
Examples
Ø Last year’s most effective source
Ø Last year’s quality of hire
Ø Last year’s time to fill

27. 22
10 reasons why you need predictive metrics
1. Today TA is reactive – requisitions open up with
little warning and we mostly source for current
openings
In the future we will need a talent pipeline – in
the future we will begin sourcing in key jobs long
before an opening occurs. “Pre-need” sourcing
will give us more time to find and sell prospects
and to make offers whenever “not-looking
prospects” become available

28. Reasons to utilize actionable predictive metrics
An illustration – a turnover “alert” for recruiting
Ø Our turnover prediction metric suggests that Ms.
X, the head of sales has a 87% chance of quitting
within three months
Ø Because his new sales project was just rejected
Ø Because he recently updated his social media
profile and his resume/ CV was just posted on a
job board
Recommended recruiting actions
Ø Start sourcing now… so that we will have a pool
of qualified and interested candidates if he leaves
23

29. 24
More reasons to utilize predictive metrics
2. Predictive metrics make you aware of shifts in
historical patterns (where existing practices will no longer work)
3. Predictive metrics make you aware of upcoming
recruiting problems when they can still be
mitigated or prevented
4. Predictive metrics alert you to upcoming changes
in environmental and business factors
5. Your “time to act” may be reduced
6. An opportunity to be strategic & forward-looking
7. Increasing the odds that decision-makers will act
because they get “an alert”

30. 25
Reasons to utilize actionable predictive metrics
More reasons to utilize Predictive Analytics
8. Predictive metrics can provide time to “model”
different actions
9. Predictive metrics can provide your firm with a
competitive advantage
10. Predictive metrics will improve both the
accuracy and the impact of recruiting decisions

31. 26
Part IV
What do forward-looking recruiting
metrics look like?

32. Imagine the future of recruiting… when you can
27
predict these things:
1. Changing source effectiveness – predicting
where and when source effectiveness will shift, so
that using those newly powerful sources will make
us more effective in attracting “not-looking” and
active prospects
2. Skill needs – when and how will the future skill
and experience requirements for the firm change
3. Internal position openings – which current and
newly created jobs will need to be filled as a result
of corporate growth and employee turnover (when
and where)

33. Imagine the future of recruiting… when you can
28
predict these things:
4. General talent availability – predicting upcoming
talent shortages and surpluses in the marketplace is
important. This would include the local
unemployment rate… because it impacts the
availability of talent
5. Talent availability in specific fields – predicting
upcoming talent shortages & surpluses in key
fields, utilizing college grad. rates in those fields
and upcoming turnover rates in target firms
6. Individual talent opportunities – predicting when
individual top talent will be available Example >

34. An example… of a Talent Opportunity Alert
Why recruit Ms. Z away from DCX?
Ø Invited & spoke at DCX top managers award conference
Ø 2 hires from DCX said she was forced ranked #1 & her
group has a rev per employee of $489,000
Ø DCX manager hires have a 98% success rate at our firm
Why recruit her now during December?
Ø Her bonus is paid on Dec 20
Ø She updated her LinkedIn profile on Dec 10th
Ø She gets her night MBA on Dec 1
Ø Her boss is retiring 12/31, & she is not the replacement
Ø Dec. is the worst weather month where she works in SD
Ø She visited our LinkedIn site 5 times in Dec.
29

35. Imagine the future of recruiting… when you can
30
predict these things:
7. When direct recruiting competition will
increase / decrease – predicting when ramped
up hiring by competitor firms will make it
more difficult for our firm to successively hire
top candidates.
Also predict talent opportunities when slow
hiring months at competitors… and when hiring
freezes, layoffs, M&A and slow corporate growth
will make hiring less competitive
Also forecast their anticipated reaction to your
TA plans

36. Imagine the future of recruiting… when you can
31
predict these things:
8. Prospect visibility – predicting when (because of
social media and the Internet) previously “hidden
prospects” will become easier to find
9. Boomerangs – predicting when former top-performing
employees are likely to want to return
10. Identifying the factors that predict hiring
success – creating a hiring algorithm that can
successfully identify the factors that will predict
on-the-job performance. A different algorithm
may be needed for innovators / college students
Example >

37. 32
An example – where metrics identified
the root cause of a new hire failure
Gategourmet had extreme new hire turnover rates
Ø It used Q of H data to identify the most
important factors that predicted new hire
performance & low turnover
Ø Surprisingly they learned that the key factors were
commute distance & access to public transportation
Ø After changing its hiring criteria…
the firm achieved “fully staffed status” for the
first time
Ø And cut unwanted new hire turnover to just 27%
Source: Talent Management 11/22/13

38. 33
Possible predictive metrics to consider
Recruiting related predictive metrics
11. Changing candidate expectations – when and
how the expectations of our targets will shift
12. Referrals – identifying which new employees are
most likely to be able to make quality referrals
13. Acqui-hire targets – identifying which “talent
rich” firms will be available for purchase or
merger
14. Employer brand strength – predicting when and
why our employer brand strength will increase or
decrease, compared to others

39. Now let’s shift to how to
implement predictive analytics
34
Part V

40. Elements of an individual predictive metric
An individual predictive metric should reveal…
1. What will likely happen
2. The probability that it will happen (in percentages)
3. When it will happen (month or quarter)
4. Where it will happen (region, facility or business unit)
5. To who will it happen (which executive will be
impacted the most)
6. The $ consequences when it happens (+ or -)
7. The cost of doing nothing or delaying
35

41. How to convert ordinary predictive metrics
into actionable metrics
(Metrics that drive action)
36

42. These 13 factors turn an ordinary predictive metric
37
into an actionable one
1. A red, yellow or green light indicator
2. Predict the $ revenue impact of the problem/ opp.
3. List the corporate goals that it impacts
4. Include a visual trendline
5. Benchmark comparison numbers (average, best, worst)
6. Reveal the root cause of the problem (Why)
7. Highlight the recommended actions and their
success rate, costs and ease of implementation
8. Provide “drop-down” more detailed information
9. Include the cost of doing nothing or delaying
10. List the accountable individual (Problem owner)

43. - $4.1 million Corp goal: Time to Market
38
An example of an actionable metric display
Yearly rev. impact from no action
HR metric – Time to fill (TTF)
This months' TTF = 80 days
Projected TTF = 99 days within 4 mths.
Last year’s TTF = 68 days TTF Trend (Up 22%)
Best in the industry = 29 days (We are 51 days behind)
Cause – Mgr.'s workload is slowing interview scheduling
Cost of doing nothing - $360,000 per month
Action required – Cut delays with after-hour remote video
interviews – $10,000 cost and a 87% success rate
Accountable individual – Pam Tyne, staffing manager

44. 39
Provide “drop-down” menus
Provide quick access to “in depth” information
Time to fill is up 22% (Rev impact $4.1.million)
Drop-down menu
• Formula for time to fill
• Definition
• TTM for your unit
• Impact on new hire quality
• Reasons for hiring delays
• Recommended action
steps
Run your cursor over the metric

45. 40
Elements of an actionable predictive metric
Do these things to make your metrics actionable
11. Prioritize your metrics by their level of
importance
12. Include them in standard business reports
13. Provide only a handful of metrics

46. What are the best ways to predict
upcoming Talent Acquisition events?
41

47. 7 basic approaches to identify upcoming TA events
1) Extrapolate from a trendline
Ø Extrapolate from past data and events inside
your firm and at your competitors (Excel creates trend lines)
Ø Follow the firms that historically take talent
management actions first (i.e. first to begin hiring, first to layoff)
Ø Also track the TA actions of “lagging” firms as
an indication that you are falling behind
Ø Track “indicator firms” outside your industry
Ø A trendline example – turnover rates today are
10%... but they are increasing by 2% a month… so
in 6 months they will be… at a 5 year high at 22%
42

48. 43
Identifying upcoming TA events
2) Identify seasonal or repeating events
Ø Identify trends that happen at a certain time
each year and alert managers about their impacts
based on past data…
and don’t be fooled into thinking this trend is
something new
Ø A seasonal example – in the past, application rates
have gone up 20% at the beginning of summer and
down to only 1% in December

49. 44
Identifying upcoming TA events
3) Identify precursors
Ø Identify things that occur immediately before a
business, economic or talent event and use them
as alerts about what is likely to happen
Ø A precursor example – when a major competitor
goes through a layoff or merger, they institute a
hiring freeze… Which means that we can hire
higher-quality talent without competition

50. 45
An example of talent surplus precursors
Precursors indicating a coming surplus of talent
1. An increase in the unemployment rate
2. Employee turnover rates are decreasing
3. “Leading firms” have massive layoffs and hiring
freezes
4. The number of applications is increasing
5. Offer acceptance rates are sky high
6. Open jobs stay open 25% longer

51. 46
Identifying upcoming TA events
4) Identify sudden shifts in internal data
Ø Watch your data closely and look for any shifts
above 5% that did not occur at the same time
last year
Ø A shift in data example – when offer acceptance
rates drop suddenly… you must conduct “a root
cause analysis” and alert managers if the trend is
expected to continue

52. 47
Identifying future TA events
5) Identify shifts in environmental factor data
Ø Utilize existing government and university data
on environmental factors to predict significant
changes up or down (interest rates, economic
growth rates, government spending etc.)
Ø An environmental shift example – when the
regions unemployment rate goes up 1%,
historically turnover rates have also gone up 1%
within two months

53. 48
Identifying future TA events
6) Identify shifts within your own business
Ø Utilize existing internal strategic planning,
budgets, sales / production forecasts as a guide
to what TA must do to meet those new goals
Examples
Ø Projected % revenue growth or shrinkage
Ø New product development plans and product
introduction dates (tells you type of skills)
Ø The purchasing of new technology
Ø Geographic expansion plans & remote work %
Ø M&A and divestitures
Ø The budget available for recruiting (CFO)

54. 49
Identifying future TA events
And finally
7) Identify shifts at your major bus. competitors
Ø Create Google alerts and read… the newspaper,
business magazines, online blogs, CEO speeches
and press releases to identify what your
competitors are doing
Ø A shift at your competitors example – social
media blogs in some industries accurately reveal
what your competitors are planning or what they
are about to do in products and recruiting (Apple)

55. Let’s end with…
Basic action steps for implementing
predictive metrics
50

56. Action steps for implementing predictive metrics
1. Put together a team of TA professionals with a
knowledge of statistics and data
2. Talk to your CFO and get their support for your
metrics effort
3. Provide executives with a list of talent acquisition
problems/ opportunities and ask them which
“pain points” they would like to see predictive
metrics and alerts in
4. If they select Q of H, I recommend that you start
there (predict when the rate will go up / down
dramatically and “why”)
51

57. More action steps for implementing predictive metrics
5. Benchmark with other firms and “big data”
experts to learn from their best practices in this
area
6. Run a pilot in a business function that’s easy to
measure (sales or customer service)
7. Invite some of your company’s finance and
business analytics experts to critically review
your first run of data and your approach
8. Roll out a broader predictive metrics effort but
continually monitor usage, manager satisfaction
and accuracy rates
9. Ask for a raise 52

58. Did I make you think?
Are there any more questions?
JohnS@sfsu.edu or www.drjohnsullivan.com
53