10.09.14 glassdoor webinar_all_slides

Presented by: Dr. John Sullivan

Forward-Looking And Predictive Metrics For Recruiting

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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 [email protected]

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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

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Topics we will cover today…

1. Introduction and 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

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An introduction and some quick metric

related definitions

Part I

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“In God we trust, everything else we measure”

--VP of HR, UPS

Everyone else already “lives” metrics

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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)

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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

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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

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

DATA DRIVEN TA

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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

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Let’s highlight…

Reasons for using traditional metrics

Part II

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#1 - The use of analytics increases business results (source: Harvard Business Review)

Advanced 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

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#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

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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”

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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

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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)

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)

16 Source: BCG/WFPMA - From Capability to Profitability: Realizing the Value of People Management, 2012

Which TM functions have the highest business impact?

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#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

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#7 - Realize that HR is not very good at analytics

What % of CEO’s are confident in the quality of Human Capital metrics? What would be an ugly%?

18 Source: AICPA survey

12%

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Of the 13 major areas of HR performance… where do metrics rank? (KPMG)

1.  2.  3.  4.  5.  6.  7.  8.  9.  10

. 

11. 

12. 

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.

 

#12 & #13

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Now let’s shift to…

The reasons for using predictive HR metrics

Part III

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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

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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

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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

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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”

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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

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What do forward-looking recruiting

metrics look like?

Part IV

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Imagine the future of recruiting… when you can 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)

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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 >

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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.

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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

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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 >

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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%

32 Source: Talent Management 11/22/13

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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

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Now let’s shift to how to

implement predictive analytics

Part V

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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

How to convert ordinary predictive metrics

into actionable metrics

(Metrics that drive action)

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These 13 factors turn an ordinary predictive metric 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)

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An example of an actionable metric display

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

Yearly rev. impact from no action

- $4.1 million Corp goal: Time to Market

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Provide “drop-down” menus

Provide quick access to “in depth” information Time to fill is up 22% (Rev impact $4.1.million)

• Formula for time to fill • Definition • TTM for your unit • Impact on new hire quality • Reasons for hiring delays • Recommended action steps

Drop-down menu

Run your cursor over the metric

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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?

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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%

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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

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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

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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

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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

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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

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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) 48

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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) 49

Let’s end with…

Basic action steps for implementing

predictive metrics

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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”)

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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

[email protected] or www.drjohnsullivan.com

:if the rate117

Did I make you think?

Are there any more questions?

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