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Forward-Looking And Predictive Metrics 
For Recruiting 
Presented by: 
Dr. John Sullivan 
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October 9, 2014 
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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
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
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
Part I 
An introduction and some quick metric 
4 
related definitions
5 
Everyone else already “lives” metrics 
“In God we trust, everything else we measure” 
--VP of HR, UPS
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)
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
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
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
10 
Part II 
Let’s highlight… 
Reasons for using traditional metrics
#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
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
#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
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
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)
#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
#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
#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%
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
20 
Part III 
Now let’s shift to… 
The reasons for using 
predictive HR metrics
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
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
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
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”
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
26 
Part IV 
What do forward-looking recruiting 
metrics look like?
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)
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 >
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
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
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 >
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
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
Now let’s shift to how to 
implement predictive analytics 
34 
Part V
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
How to convert ordinary predictive metrics 
into actionable metrics 
(Metrics that drive action) 
36
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)
- $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
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
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
What are the best ways to predict 
upcoming Talent Acquisition events? 
41
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
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
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
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
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
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
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)
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)
Let’s end with… 
Basic action steps for implementing 
predictive metrics 
50
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
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
Did I make you think? 
Are there any more questions? 
JohnS@sfsu.edu or www.drjohnsullivan.com 
53

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10.09.14 glassdoor webinar_all_slides

  • 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 Confidential and Proprietary © Glassdoor, Inc. 2008-2014
  • 3. 80% Approve Confidential and Proprietary © Glassdoor, Inc. 2008-2014 2 Why Glassdoor? 48% job seekers in US use Glassdoor when searching for jobs Pros Good opportunity to learn a lot Work-life balance Cons can be tough Company Benefits Stock & Health “Exceptional benefits package. Great stock and health options” Interview Questions Sales Representative “Make sure to research companies growth strategy” Salary Software Dev Engineer 107k (1,448 Salaries) Amazon.com 3.5k Reviews Reviews 3.3 “Opportunity like nowhere else”
  • 4. Fastest Growing Career Sit e 23,000,000+ Unique Users Worldwide 70% 60% 50% 40% 30% 20% 10% 0% -­‐10% Jan Feb Mar Apr May Jun Unique Users Mobile Users Content Job Clicks Confidential and Proprietary © Glassdoor, Inc. 2008-2014 Visits 76% 66% 176% 141% 84%
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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