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7 Recruiting AI Terms Every Recruiter Needs to Know

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Interest in artificial intelligence (AI) recruiting technology has exploded recently. From finance to sales departments, business leaders are asking how they can leverage AI technology to become more efficient, cost-effective, and competitive. HR is no exception.

To stay on top of this trend, here are seven recruiting AI terms that every recruiter needs to know.

  1. Artificial intelligence

Artificial intelligence (AI) is a machine that can mimic human abilities such as learning, problem- solving, planning, perception, and the ability to move objects.

In a nutshell, AI requires large amounts of data as inputs to produce an output which is a solution to a problem. Core areas of AI include machine learning (e.g., Netflix recommendations), machine perception (e.g., Apple’s Siri), and robotics (e.g., self-driving cars).

How AI is used in recruiting

AI for recruiting is the application of artificial intelligence such as learning or problem-solving to the recruitment function. Recruiting AI technology is designed to automate some part of the recruiting workflow, especially repetitive, high-volume tasks.

Applications of recruiting AI technology that currently exist include automated resume screening, recruiter chatbots, and digitized interviews.

  1. Algorithm

An algorithm is a procedure or formula that takes inputs through a sequence of steps to produce an output in order to solve a problem.

How an algorithm is used in recruiting

The simplest form of an algorithm used in recruiting is a keyword or Boolean search. The problem here is identifying qualified candidates from a larger applicant pool, the inputs are your search terms, and the output is a shortlist of candidates who meet your search specifications.

An example of how an algorithm is used in recruiting AI technology is intelligent resume screening. The problem here is the same: identifying qualified candidates from a larger applicant pool.

Instead of using pre-selected search terms, this type of machine learning algorithm trains itself on prior employees to learn which resume data points (inputs) are correlated with successful employees to produce a shortlist of qualified candidates (output).

  1. Machine learning

Machine learning is a type of computer program or algorithm with the ability to teach itself by analyzing data (inputs) and coming up with a solution (output).

A machine learning algorithm continues to learn from new data you input to increase the accuracy of the solution it comes up with.

How machine learning is used in recruiting

Machine learning algorithms in recruiting AI technology is being used to automate resume screening to shortlist and grade candidates by learning from existing employees’ resumes.

Machine learning algorithms in recruiting software are also being used assess candidates’ personality, and job fit through digitized interviews by learning from successful candidates’ facial expressions and word choices.

  1. Natural language processing

Natural language processing is the ability of a computer program to understand human speech as it is spoken or written.

How natural language processing is used in recruiting

One way natural language processing is being used in recruitment automation technology is through AI chatbots that provide answers, feedback, and suggestions to candidates in real time.

Based on candidates’ replies and feedback, the chatbot uses machine learning to teach itself to become more accurate in its answers when interacting with other candidates in the future.

  1. People analytics

People analytics is the use of data and data analysis techniques to understand, improve, and optimize the people side of business.

People analytics links people data (inputs) with different types of business data using predictive algorithms to produce outcomes (outputs) aligned with company goals such as increased revenues and lowered costs.

How people analytics is used in recruiting

People analytics isn’t a recruiting AI term on its own, but it falls under the same umbrella of leveraging data and technology to optimize HR and recruiting processes.

  1. Predictive analytics

Predictive analytics is a catch-all term for the application of a statistical equation or algorithm to a data set (inputs) to create a predictive model (output) that determines a numerical value of a future probability.

In many cases, the data set used contains multiple variables that are believed to be predictive of a particular outcome.

How predictive analytics is used in recruiting

Predictive analytics can be applied to candidates to predict which ones are likely to be successful employees. This predictive model can be created using resume data, pre-hire assessments, or interview scores.

For a predictive model that uses resume data as its inputs, the variables could include education level, years of experience, skills, and personality traits.

Predictive analytics can also be applied to employees to predict which one are likely to quit. This predictive model may use multiple variables such as commute distance, company tenure, employee engagement, and compensation.

  1. Sentiment analysis

Sentiment analysis is the ability of a computer program to determine the subjective opinion, emotional state, or intended emotional effect of spoken or written word.

The basic form of sentiment analysis is classifying the polarity of a given text: positive, negative, or neutral. More advanced sentiment analysis classifies text into specific emotions such as “angry” and “happy.”

How sentiment analysis is used in recruiting

Sentiment analysis is being used to identify potentially biased language in job descriptions. The program is fed inputs that words such as “aggressive” are perceived as masculine-sounding whereas words such as “collaborative” are perceived as feminine-sounding.

By analyzing the words used in a  job posting, the program can create output in the form of suggested replacement words in order to help solve the problem that these words may be discouraging female candidates from applying.

The takeaways

The dominant theme in recruiting right now is AI for recruiting. It’s clear that tech-enabled recruiting is here to stay. Give yourself a leg up by familiarizing yourself with the AI recruiting technology terms below:

  1. Artificial intelligence
  2. Algorithm
  3. Machine learning
  4. Natural language processing
  5. Predictive analytics
  6. Sentiment analysis

A version of this post was first published on Ideal.

Photo Credit: jhuniorig Flickr via Compfight cc

How Big Data Drives HR in 2016

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Big data has helped earn human resources a seat at the table (or so we hope, as we move beyond buzzword phrases)—an active part of the business planning process, supported by the deep insights and predictive analytics that this gold mine of information provides.

But a lot of HR departments are still trying to figure out what to do with all that data. A report from Oxford Economics and SAP found that few HR departments are without business analytics altogether, but many still struggle to use what they have in a way that matters.

There’s good news for the coming year. As our ability to analyze and interpret big data matures, new tools are hitting the marketplace while existing ones are getting smarter. Here’s a look at how big data will drive HR this year, and the biggest trends you need to know about.

Watch for These Big Data Trends

Technology will never take the place of a highly skilled HR professional, but it can validate decisions and streamline operations—in real time. Companies who take an interest in these trends early on may be able to leverage them in the marketplace.

  1. Vanity metrics—stats that look good but offer little meaningful insight—are fading away. Quality trumps quantity when it comes to data sets, and the application of metrics matters far more than in the past. As companies attract more data analysts and train employees to use analytics programs, teams are focusing more on the strategic use behind how and what they collect.
  1. Predictive analytics are getting smarter. Predictive analytics can be a powerful tool for business as a whole, and the programs available are finally stepping up their game. While they can provide insights into employee benefits, promotions, and talent management, predictive analytics are starting to be used for deeper forecasting. For example, they can help measure the efficacy of training courses, or to identify which employees are more likely to reach their targets and why.
  1. Analytics tools are getting simpler—and more affordable. One thing that’s held the rise of analytics back is the fact that some companies can’t afford a full suite of tools while others find the applications they have don’t always uncover the information they want. But new options are on the horizon. Companies such as Dell and Oracle have embraced HR Open Source (#HROS), a movement to bring “an open source approach to HR and recruiting.” More options will fuel the use of analytics across organizations of all sizes.
  1. You can put a value on human capital. Organizations often claim that human capital is one of the most important business assets companies have, but they have a difficult time backing up that statement with data. With analytics, companies can assign financial values to individual tasks and better understand the financial impact of every person in their organization, which has potential implications for recruitment, benefits, and talent retention.
  1. Sensors offer a whole new perspective. There are new ways to collect data—from internal monitoring systems, online listening platforms, or even the growing Internet of Things (IoT)—and use it for on-the-floor insights. In industries such as manufacturing and farming, sensor-driven data can provide information on machine or crop performance. But it can also impact HR responsibilities: For example, Honeywell and Intel recently introduced a prototype for sensors that monitor worker safety. If HR departments can identify warning signs or other real-time data signals, they can find new ways to improve regulation compliance and worker safety.
  1. Data analysts are in high demand. CNBC called it “the sexiest job of the 21st century,” and it’s definitely one of the hottest jobs out there. It takes a skilled data analyst to understand how to massage and extract data and produce actionable reports. Not surprisingly, they’re in relatively short supply. Organizations will need to get creative to find the talent they need to meet their analytics needs.

Big data has the potential to improve every aspect of business—if companies are willing to take the time and effort to figure out how. The right data-focused talent and tools can transform an organization. The opportunity is there for big data to drive HR; You just need to take advantage of it.

 

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HR Leaders: The Value of Climbing the Analytics Ladder

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HR leaders are often put in tough spots. It’s not unusual to have to choose between a decision that’s unpopular and one that’s ineffective.

Take this real-life example: A line manager noticed that more of his employees were resigning than ever before, and he wants his company to do something about it. He approaches HR with what he thought was the right solution, to raise the base pay for all of his employees by 10%, and he now just wanted HR to execute his idea.

The HR team knew from past experience that an across-the-board pay raise was the wrong thing to do. It was an expensive way to fix the problem, and worse, it was unlikely to lead to fewer resignations. The problem was that HR had no data to prove it. Worse, the data the HR team did have only served to support the manager’s case. The data confirmed that the resignation rate had in fact increased. Given the lack of evidence to prove that an increase in base pay wouldn’t work, the people experts were left with two unsatisfying choices: Acquiesce to the manager’s demand and put the company’s headcount budget at risk, or dig up a compliance-related reason for not raising base pay—and risk annoying the manager and his team.

The Metrics Trap

This unfortunate story highlights the trap many HR teams have fallen into: They’ve bootstrapped their people metrics to the point where they can answer a few basic questions about their workforce, such as the resignation rate for a given team, but they discover that a limited set of descriptive analytics isn’t enough. For many, it’s worse than having nothing.

What they need is a way not just to see what happened, but to understand why it happened, what will happen next, and how to adapt their workforce strategy to align with company objectives.

If the HR team had been able to dig deeper into the reasons for the resignation rate increase, they could have proposed a solution that created a better business result—instead of engaging in a head-to-head conflict with the manager, supported by little more than anecdotes and gut feel.

For HR teams to climb out of the metrics trap and realize their full strategic potential, they need to get past the first rung of the analytics ladder— descriptive analytics—and move up to exploratory, predictive, and guided analytics.

The analytics ladder

Descriptive Analytics: The First Rung

To be clear, there’s nothing wrong with descriptive analytics—they’re a vital component of any workforce intelligence solution, and they’re the foundation that subsequent rungs of the analytics ladder are built on. To even begin creating a strategy, you first need to answer simple questions like, “How many people in Group X have resigned in the last year?”

Every HCM vendor worth its salt will say their product comes pre-built with analytics, but typically these are nothing more than descriptive analytics— a visual representation of what’s happening, sometimes appended with basic, simple-to-calculate attributes of the data set, like mean, median, and mode. Beyond describing the current situation, they do little to facilitate effective decisions or drive action.

What’s missing from descriptive analytics is a way to see why something is happening. People who resign, for instance, might have something in common that isn’t easily uncovered by basic descriptive analytics. Their age, tenure, performance rating, or some other more obscure attribute might play a role—and that’s the kind of context you need to move up to the next rung of the ladder.

Exploratory Analytics: The Second Rung

Exploratory analytics facilitated by in-memory technology help you drill deep into the data quickly and see patterns that otherwise aren’t obvious. If the resignation rate is increasing only for low performers, perhaps the answer is not to focus on retention, but instead to focus on hiring top performers.

On the other hand, if top performers are leaving, exploratory analytics can help you create strategies to retain them. Perhaps a specific career band of top performers is most at risk — you can develop a targeted approach to keep that group engaged and excited about their job and career prospects.

Exploratory analytics can also help you discover common ground among people who do stay. For instance, if you see that internal referrals are more likely to stay than external hires, you can ask your recruiters to shift even more of their focus to internal referrals.

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Predictive Analytics: The Third Rung

As much as exploratory analytics help you, predictive analytics give you even more power: In the context of resignations, predictive analytics can help you pinpoint who is most at risk of leaving the company next. This level of foresight means you can take action to prevent a negative outcome before it happens.

After you’ve used exploratory analytics to create a long-term strategy for reducing resignation rates in a business unit, you can use predictive analytics to see who the Top 100 employees most likely to resign are. If the list includes key performers you need to keep, you can create custom retention strategies tailored to just those individuals.

For instance, based on statistical analysis by data scientists at Visier, predictive analytics technology can be up to 8 times more accurate at predicting who will resign than guesswork or intuition alone. If you can stop even a single mid-level salaried worker from leaving, you’ve saved your company a replacement cost that could range from 1 to 1.5 times that person’s annual salary. For senior-level employees and executives, you’ve saved a replacement cost that could be up to four times their annual salary.

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Guided Analytics: The Top Rung

Once you’ve reached the third rung, you might think you’ve reached the pinnacle of analytics—what more is there to do beyond predicting the future? There is in fact a next step that’s even more critical for a business, and that’s the ability to share insights with the teams that need them.

If your business is seeking to transform HR into an evidence-based practice, then it’s not enough to be data-driven within your own silo. You have to use data to convince the entire business to make the right workforce decisions, and the answer is guided analytics. They’re the sum of the first three types of analytics, shaped into a meaningful story.

Most of your stakeholders will not have a background in statistics or analytics—or the workforce expertise HR has—so they’ll need help. The approach you take depends on your audience.

The first approach, often most effective for conversations with executives, is to present to the findings in person, weaving all three types of analytics into a compelling story. If you’re proposing a program to reduce resignations, you’ll want to help your leaders understand what the resignation rate has been, what key patterns you’ve discovered, who’s likely to leave next, and why you expect your retention plan to work.

The second approach—best when you need to provide workforce data to many stakeholders—is to give them self-serve access. But if they’ve only used descriptive analytics in the past, they’ll need a helping hand—a guided experience—to get the most out of exploratory and predictive analytics. If the tool or solution you use to do this is too complex, self-service will not work (read this study about why Yahoo replaced their legacy dashboard system for this very reason). The best guided experience is one that’s designed specifically for the workforce domain and prompts your stakeholders to ask the right questions.

It’s All About Trust

To build trust with the business, HR needs to climb all four rungs of the ladder. Once you prove how effective data-driven workforce decisions are, you’ll capture the attention of the business and earn the right to make valued recommendations.

Think of it as a ripple effect—in a typical business, roughly 1 percent of staff are in HR, but 10 percent are in management. If you embrace the use of guided workforce analytics to build trust, you’ll expand the practice of evidence-based HR beyond your own team, making your company’s workforce decisions more effective by a substantial margin.

A version of this article originally appeared on the Visier Workforce Intelligence Blog.

Image credit: StockSnap.io

 

Visier Analytics is a client of TalentCulture and sponsored this post. 

#TChat Recap: The Predictive Power Of HR Analytics

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We could easily be intimidated by data. Yet, we crave answers on how to make the best hires, reduce cost, drive strategy… the list goes on and on.

Many organizations now turn to predictive analytics: the ability to take what happened in the past and find common relationships and factors (leveraging human behavior and neural networks) to model and predict the future, enabling us to report back in analytics with recommendations for the future.

Finance, Sales, and Marketing departments have already realized the importance of predictive analytics. Now it’s HR’s turn to gaze into the crystal ball.

This week’s #TChat guests: Chad W. Harness, VP of Lead Human Capital Analytics Consultant at Fifth Third Bank; and Jen Phillips Kirkwood, ADP Analytics and Innovation Ambassador, shared their insights on the predictive power that HR can bring (we’re proud sponsors of the Predictive Analytics World for Workforce.)

First step? Get clear on objectives and take a close look at problems that are in need to be solved. Once we have painted a clear picture, ask yourself: How can HR help to support KPIs and find analytics that can yield real action?

If we don’t trust or understand the data, it’s easy to make knee-jerk hiring decisions.

By understanding key differences between data, metrics and analytics we can make better recommendations and decisions for the future.

So how do HR leaders start a predictive analytics initiative successfully?

There’s no doubt that predictive analytics will have an immense role to play as we move forward. HR leaders want to get there, and frankly, they have to get there. Once we have arrived, HR will be given a stronger voice that will drive strategy and help cost reduction and retention.

Just remember, it’s not only about the data, it sometimes comes back to a curiosity and willingness to change.

See What The #TChat Community Said About The Predictive Power of HR Analytics:

What’s Up Next? #TChat Returns Wednesday, April 8th!

#TChat Radio Kicks Off at 7pm ET / 4pm PT — Our weekly radio show runs 30 minutes. Usually, our social community joins us on Twitter as well. Next week’s topic: Adopting Social Software for Workforce Collaboration and Communication

#TChat Twitter Kicks Off at 7:30pm ET / 4:30pm PT — Our halfway point begins with our highly engaging Twitter discussion. We take a social inside look at our weekly topic. Everyone is welcome to share their social insights #TChat.

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Passive-Recruiting

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Reversing The Polarity Of Recruitment

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We’ve all seen it in the cinema or on TV. That moment when the beleaguered starship engineer, asked to achieve things his machines were never designed for, declares that he’s going to ‘reverse the polarity!’ None of us, not even the writers who put the words into that character’s mouth, know what this means in technical terms, but we all understand what it represents. It’s about taking the tools he already has and turning them around, using them in new and challenging ways to achieve something even more incredible.

So if we want to achieve greater things than we ever have before how can we turn our corporate engines around and reverse the polarity of recruitment?

Listen to the new guys

A lot can be achieved just by looking at what we have from a different perspective.

We tend to view new recruits as a burden in the short term, but as Liz Wiseman has pointed out, this needn’t be the case. The fact that they don’t understand the normal workings of your organization can actually be an asset. They won’t be bound by expectations, and so will come up with innovative solutions. Because they aren’t used to your habitual timescales they will deliver work as fast as they can, not to the deadline, however distant. They will never say ‘that’s not how we do things around here’, and they will look at your work through the filter of their existing skills and experience, bringing in fresh knowledge and analysis.

So instead of trying to hammer new recruits into a familiar shape as soon as possible, or fobbing their unexpected opinions off as naïve, try listening to them and using them as a source of energy and innovation.

Is it really a weakness?

Many of the qualities we view as weaknesses can actually be strengths. Jim Haudan has recommended working on your vulnerability to make yourself more open to new ideas and emotional connections with those around you. But this is only one way in which you can transform your relationship with your own personality and with the supposed weaknesses of job applicants.

Managerial culture is strongly shaped by the values of a white, middle-class, male, puritan mind-set, with all the assumptions that brings about what is a strength or a weakness of personality. But often the characteristics rejected by these values can be useful if applied in the right way.

Maybe someone is naturally quiet, and they’ve spent time trying to learn to be more assertive. Maybe you can make the most of their quiet nature, recruiting them to absorb the ideas of others and become someone they talk to. Maybe an applicant is fidgety and finds it hard to fit with routines. That energy could make them perfect to drive projects or prevent your department’s routines from growing stale.

Look again at the supposed weaknesses of potential recruits, and consider how they might benefit you.

Seeing futures, not the future

Predictive analytics is all the rage at the moment, but as John Boudreau has pointed out it has its limits. We habitually use analytics to try to predict and prepare for a single future, recruiting to that end, even though that is only the most likely future. Instead look at the most likely set of futures and recruit for several of them. This will give you the flexibility to face a range of outcomes, not binding you to a single future that may never come.

Reversing the polarity of recruitment isn’t about technical understanding; it’s about a shift in perspective. It’s a shift that will allow you to be more innovative and flexible than ever before.

Is Technology Making HR More Innovative?

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Most people don’t associate HR with innovation. They’d associate HR with the things that traditionally we have been good at — rules, policies, administration.

And they they’d be wrong. The first wave of innovation centered around a more business-focused HR function, in which HR developed as a business partner. This was something of a revolution, although its application rarely worked as well as was hoped (except in larger organizations) — in theory, it aligned HR to business needs.

And, eventually, business outcomes. However, it was needs that were at the heart of the discussion. What’s the business strategy? Are we looking for growth? New markets? How will we deliver the right form of HR: the right people, the right support?

This second wave of innovation is technological. After initial HR solutions delivered solutions around which HR could work, we’ve moved more into bespoke cloud-based HR software, which has resulted in HR demanding more technology.

And that has uncovered limitless potential for HR to inform and even shape the business. The cloud has made HR more innovative. Here are some real-world examples:

Resourcing & allocation of resources

Imagine a world where CRM and HR data are combined — giving the company a double vision. On the one hand, you have customer data — buying patterns, be they geographic or time-based — and on the other, you have people data.

Match the two up, and you can start allocating your people resources better. Starbucks has this down to a tee — matching its staffing levels to when each store is at its busiest. The same applies for talent acquisition — getting in the right resources, for the right times, in the right places — and it’s evidence-based.

Now that HR has this data on the cloud, it’s readily available and, with a little development, can be integrated with other systems.

Talent management

Advanced HR analytics can tell you more about your talent than perhaps you previously thought. From hire to onboarding and talent pipelines, the knowledge is available. And here’s where the Ulrich model blends seamlessly into technology — a knowledge of the business and how each department functions is essential for understanding business requirements.

However, a knowledge of the potential behind the technology is what really underpins talent management.

For instance, technology can do the sifting through CVs for you. It can guide employees through the onboarding process, and can be the foundation for performance management. Advanced analytics can then pinpoint potential future leaders, and map their career progression.

Keeping your best people

Predictive analytics is the Next. Big. Thing. If you believe everything you read. Knowing when people are ready to leave you, therefore, could give you a competitive advantage, especially if you are in a talent marketplace that is particularly competitive.

By analyzing the past and mapping that out into a predicted future model, you’ll be able to foresee which key employees are most likely to leave the business — and introduce a proactive retention program. Equally, you will be able to give the business a better idea of what level of recruitment spend it’ll need over the next 12 months, which helps with budgeting.

But remember…

My old colleague Sean Dunphy says of HR software – “If you do what you’ve always done, you’ll get what you’ve always got” – and he’s right. The technology is meant to be an enabler, not a solution to your problems. It’s meant to deliver business-wide success, not internal departmental success.

Therefore, remember that you’re not working around technology, you’re getting technology that meets your business requirements. It’s a key consideration, and one that many businesses tend to forget over time.

You can do so much with technology in HR – but only if you first consider the potential, before scoping it out. From predictive people analytics to better resourcing & staffing, there’s so much that can be done.

photo credit: “Caveman Chuck” Coker via photopin cc

Games and Data and Talent — Oh My! #TChat Preview

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(Editor’s Note: Looking for a full review of this week’s events and resources? Read the #TChat Recap: “Game On: Playing to Business Strengths.”)

Two of the hottest trends in the world of work today are “gamification” and “big data.” But what do these concepts really mean to you?

For some Leaders and HR professionals, this looks and feels like buzzword territory. But others are starting to recognize how game-based tools and big data intelligence can truly transform talent strategy. In fact, some of today’s most innovative organizations are actually combining these techniques — creating powerful new solutions that improve management decisions, as well as business outcomes.

New Paths To Better Talent Choices

The truth is, gamification, big data and advanced analytics are creating a perfect storm that is rapidly redefining employee acquisition and retention. These emerging trends are central to the future of work. And that’s why they are our focus this week at #TChat Events.

As our community explores the connection between games, big data and talent strategy, we welcome two experts:

• Guy Halfteck, Founder and CEO of Knack, a company that combines cutting-edge video games, big data analytics and behavioral science to help companies identify and recruit top talent. (Connect with Knack on Twitter)

• Mark Howorth, COO at Panavision, and former Partner and Sr. Director of Global Recruiting at Bain & Company.

To kick-off the discussion, I spoke briefly with Guy in a G+ Hangout recently about how big data shapes the human side of business:

For a deeper look into Guy’s perspective on this topic, you may also want to watch his recent appearance at The Economist forum “The Ideas Economy: Human Potential 2012”

#TChat Events: Games + Big Data + Talent Management

Our guests this week are seasoned innovators who deeply understand the strategic implications of gaming and data. This promises to be a fascinating discussion for talent-minded professionals everywhere. So please plan to join us, and bring your ideas, questions and concerns!

#TChat Radio — Wed, Sep 18 — 6:30pmET / 3:30pmPT

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Tune-in to the #TChat Radio show

Our hosts, Meghan M. Biro and Kevin W. Grossman talk with Guy Halfteck and Mark Howorth about how games are emerging as a highly effective, reliable way to select, recruit and retain employees. Follow the action online, and dial-in LIVE with your feedback and questions!

#TChat Twitter — Wed, Sep 18 — 7pmET / 4pmPT

Immediately following the radio show, we’ll move the discussion to the #TChat Twitter stream, where Dr. Nancy Rubin will lead an open chat with the entire TalentCulture community. Anyone with a Twitter account is invited to participate, as we address these questions:

Q1: Why is gamification becoming more important to the world of work?
Q2: How can gaming data improve recruiting and hiring decisions?
Q3: What are some real-world use cases of successful workplace gamification?
Q4: How can business leaders best deploy games in the workplace?
Q5: How can companies use gaming technology to improve employee engagement?

Throughout the week, we’ll keep the discussion going on the #TChat Twitter feed and on our LinkedIn Discussion Group. So please join us share your questions, ideas and opinions.

We’ll see you on the stream!