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The Age of the Super-Recruiter

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An Interview with Sanjoe Jose, CEO and Cofounder of Talview

The challenges in hiring span from finding the right talent to finding the technology to help make successful hires. And we’ve seen an incredibly fast-paced phase of innovation in the field. At the forefront are leaders like Sanjoe Tom Jose, cofounder and CEO of Talview.

Sanjoe is passionate about making hiring easier with cutting-edge AI and machine learning-based technologies. He’s also likely one of the rare innovators who turned down an offer from Google to strike out on his own. Instead, he built Talview — which stands for ‘talent view.’ The firm’s Instahiring Experience platform is the first to bring the one-click consumer experience to a hiring context, radically shortening an organization’s time-to-hire. 

We talked about how AI can be leveraged to transform the whole hiring journey, about his thoughts on the market, and why certain hiring teams are AI-averse — despite the fact that given today’s talent race, recruiting needs, and deserves, a real shot in the arm.

What market problems are you trying to solve?

In speaking with our customers, hiring lag or a long hiring process is their biggest challenge. Businesses always need talent as soon as possible. But hiring lag also impacts other key hiring metrics — such as Quality of Hire, since the best candidates are only available in the market for ten days. This is especially critical in the gig economy, where companies can’t spend 2 months to hire someone who might only be employed for 6-12 months. Long hiring processes and not being able to provide remote functions also impact the Candidate Experience: 57% of candidates drop out of the hiring funnel due to a long hiring process. Companies also need a way to showcase their culture, values, and expectations to candidates before they’re hired, as it will help new hires ramp up quicker.

In your view, how is the candidate experience broken?

The back-and-forth process between employer and employee can easily become slow and confusing due to the time it takes to screen and select the best resumes, contact the candidate, arrange an interview time that suits both parties, and organize for someone to conduct

the interview. It’s not just a lack of speed that’s hurting businesses’ hiring process, either. The quality of the candidate’s experience as they go through the different stages of the funnel is also suffering at the hands of inefficient and aging recruitment practices. Job applicants are often forced to take off work to make multiple trips to attend interviews in person, and once they’re in the final stages, an offer can take weeks. A poor candidate experience also means applicants drop out of the funnel, and they’re less likely to reapply to the same company in the future.

Let’s talk a bit about solutions. How can technology fix some of these problems? 

In our own firm, we base solutions on three propellers: Remote, Automate, and Reuse. Generally, organizations need to be able to complete screening assessments and interviews online — they should not have to rely on physical face to face, especially if the best talent is remote. This can be done through live and recorded modes, saving candidates as well as recruiters immense time while both parties are still deciding if there’s a good fit.

And there are so many routine recruitment processes that are still completed by humans when they could be automated, such as screening resumes and scheduling interviews. Automating these processes frees up recruiters ’so they can spend time on far more value-adding activities,such as conducting final-stage interviews. And automation should be able to function round the clock, so candidates don’t have to wait to hear back from recruiters, and their questions can be answered immediately by a chatbot. 

Finally, recruiters and candidates as well need a way of streamlining the process when this is a repeat applicant. That means recording all application and interview data — so it’s available to reuse when a candidate reapplies to work for the same organization, and they can be fast-forwarded to the relevant stage. There’s no reason they should have to go through those assessments and interviews they already completed when they applied previously. Reuse just makes it easier.

How do you leverage AI and machine learning in your platform? What phases are you using AI and ML for in terms of the hiring journey? Is it end to end?

Yes — we’ve leveraged AI and ML to automate and drive more insights — all the way from the top of the hiring funnel right through to the moment of hire. From the start of the process, these technologies are used to screen resumes, derive additional insights from interviews and expertly match candidates to their ideal roles. As the candidate goes through each step from the location of their choice, computer vision is leveraged to authenticate their identity and administer multiple variety of skill based assessments. During the interview process, the technology assists hiring managers in conducting an objective interview — by building a behavioral profile of the candidate that leverages speech recognition and natural language processing, and giving suggestions as to areas hiring managers should probe from a non-technical skills standpoint. With companies struggling to assess soft skills accurately, these kinds of behavioral reports help companies hone in on communication, interpersonal, and leadership skills.

Do you think organizations can benefit more from recruiting platforms that are all-in-one inclusive?  

I do. While there are many pinpointed solutions that impact one or two steps in the hiring process, disparate systems can pose problems for enterprises, such as time-consuming, inefficient data entry and reconciliation processes. It’s more effective to have a seamless experience. Inefficiencies can lead to user dissatisfaction for all parties involved. And while some organizations do try to integrate disparate systems themselves, the performance is far from optimal and adds major overhead. We wanted to be enable the true digitization of hiring, and provide a platform that could be integrated with any of the leading Applicant Tracking Systems. I think that’s the kind of solution both sides want — the candidates as well as the hiring teams.  

My last question: What would you say to companies that are reluctant to shift to AI and machine learning for recruiting and hiring?

AI offers significant benefits while applied in the recruiting process, and hence is bound to become a significant part of every organization’s recruiting strategy sooner or later. AI can be leveraged to automate a lot of mundane tasks recruiters today perform — like matching a resume to the job description and scheduling of an interview. It enables recruitment teams to become true strategists and candidate experience champions, and ensure the best candidates join their organizations. It would be wiser for teams to leverage the benefits of AI and become super-recruiters than to get left behind.

To learn more about Talview, visit Talview.com.

How AI Makes Hiring More Accurate and More Personal

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AI is projected to catapult from a $643.7 million market today to $36.8 billion by 2025. Bersin by Deloitte calls it one of the ten major trends changing everything about how we build and manage the world of work. It’s becoming an incredibly powerful tool for recruiting, though not always understood. There are two questions I often hear:

How can we use AI to better match skills to openings?

How can we use AI to make the entire recruiting and hiring journey better, and improve candidate experience?

Before delving into specifics, consider this: Essentially, if A, then B. Just as AI is changing the game, we have to change how we see it: it’s a tool with multiple benefits at once. In other words: if we are better at sourcing the talent to find those with the right skills to match the right job opening, then the candidate experience will be better.

In this regard, AI is a positive disruption that not only improves how we find candidates, but how they experience the process of being found. All along the recruiting journey it works faster and more efficiently by profound degrees. And at the same time it has a tremendous impact on candidate experience. Let’s look at common pain points to recruiters and candidates and see how AI improves the outcome:

Recruiter Pain Point: Too Many Applications

A common pain point among recruiters is the sheer onslaught of digital applications — whether or not an applicant is actually qualified, with the required skills. We can’t put too fine a point on this: Job seekers spend an average of 49.7 seconds reading a job description, and 14.6 seconds of that is spent on the actual requirements of the job. Then, many just hit send. According to Glassdoor, each corporate job offer attracts 250 resumes on average. Of those, four to six are called for an interview — and one gets the job. Getting from 250 resumes and 4 to 6 callbacks per job is a whole lot of sorting.

AI Solution: Finding Soft Skills

AI can use pattern matching to connect the dots between job requirements and the skills and training listed on a resume. Machine learning means that AI can also get better at this the more it works, from building a bank of alternate phrases and variations it recognizes to tailoring its rankings to factor in other criteria. And AI can find soft skills just as quickly as hard skills. For instance, consider Arya: this new AI recruiting platform learns who the ideal candidate is through a combination of machine learning, big data and behavioral pattern recognition.

AI Solution: Assessing Fit

AI can also take an extremely educated and predictive guess about how a candidate may do in the long term, addressing concerns about ROI without bias. AI can use past hiring and employee records and patterns to get a clearer picture of the relative success and fit of a hire — and can identify potential blind spots of training gaps, enabling companies to put the services in place that support a better outcome.

Candidate Pain Point: an Overlong Application Process

Let’s face it: the digital environment has changed many job applicants’ perception of time. To a candidate in this digital environment, hours feel like days and days like weeks. Time, particularly for digital native generations, has shrunk — and the etiquette of responding to a message has radically changed. This is just one point of friction out of many in terms of how a candidate experiences the application process today. A delay in getting notified can feel like a rejection even if it’s not.

But while recruiters famously spend an average of 6 seconds reading a resume, finding the right hire for one job may take more than 20 hours. (And rare indeed is the recruiter tasked with filling one job at a time.) The wait — particularly if a candidate has been contacted by an organization’s hiring team — can feel like a hurry up and wait hustle, and may sour a candidate experience. Whether the result is a turn towards a different employer, or simply an element of disengagement in the process, it can stop a recruiter-candidate relationship before it starts. But recruiters simply don’t have the time or, most often, the person power to contact every applicant every step of the way.

AI Solution: Recruiters Don’t Do the Heavy Lifting

Allocating the heavy data sorting to AI frees more time for reading the resumes that actually matter. It means that unqualified candidates can be notified faster, and qualified candidates are really qualified — and the recruiter has had more time to spend getting to know them on paper before an interview. But additionally, AI can work as the messenger. For example, when a promising candidate is found with the qualifications and skills that match, Arya can reach out with a personalized message. If a candidate is interested, the connection has already been made — and a recruiter can take it from there. Instead of radio silence, there’s AI at work for you.

The myth that AI-powered recruiting is impersonal and inaccurate is just that: a misassumption about the power of AI. With the ability to greatly increase searches to radically cut down on searching time, as well as a way to reach out and develop a talent pipeline, AI enables recruiters to get back to what they know how to do best: spend time getting to know promising candidates, and find the best fit for each job. And for candidates, AI enables frequent contact and a faster process that improves their experience — and may just affect their decision to join your organization.

This post is sponsored by Leoforce.

5 Ways AI Makes Hiring Easier

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It’s one thing to see AI coming our way in HR. It’s another thing to know the best ways to harness it to improve sourcing and hiring success. AI isn’t just on the horizon — it’s part of some very forward-thinking recruiting and hiring programs already. Given how tight the job market is, AI is a way to give organizations a tangible edge on the competition. It facilitates a far more accurate way to see a far greater range and depth of talent — which means it’s easier to find better candidates — and more of them. And AI enables hiring teams to make and maintain radically better connections with talent, garner a far better sense of fit over a whole spectrum of criteria and frankly, be more human than we’ve been in a long time.

There’s no reason for any organization to shy away from AI’s capability — whether a big Silicon Valley firm or a small and lean startup. And leveling the playing field and reaching the same candidates as a larger organization, let alone a direct competitor, is just a matter of knowing how to use AI.

So, we decided to break down AI into five best practices along the hiring journey. We’re using a hypothetical hiring team we’re calling Talent Inc. to look at the five critical phases of talent acquisition, and how Talent Inc. draws on AI for tremendous advantages that result in better hires. This is an approach any hiring team can take:

Finding Talent

Our ambitious recruiting team at Talent Inc. has been tasked with sourcing 250 new hires for a growing company. These are positions from entry-level to senior management, covering a whole range of functions. Talent Inc.’s objective is to stretch their reach as far as possible to find the largest pool of talent they can. Their last AI-powered hiring campaign for this company was highly successful — and they still have all the data on the search patterns and strategies that found the best hires. This time, the team draws on that data to source a larger pool of similar candidates for the company’s new locations. They create a wireless “geofence” around specific locations. Automatically, the sourcing program gathers hordes of resumes of geographically segmented and promising candidates. Meanwhile, the team looks at the existing data on previous candidates and hires to see where there might be interest in relocating or moving up the ranks.

Making Connections

Since AI tools have done the heavy lifting for them, the team at Talent Inc. is ready to start sorting through the resumes of qualified candidates and reach out. They tailor their approaches to what they already know about these candidates — collected via AI — to make these vital first connections, using hiring events, social and mobile messages, and personalized emails. They begin to put together prospective talent pools for each level of hire, and start digging into resumes to see if they’re coming up short or sourcing sufficiently. They automatically set up and maintain an ATS. Since the whole team is working on the same platform with access to the same information, they can quickly set up automated tasks for AI to complete that will help them pinpoint ideal candidates for each position, and they can start reaching out to candidates who stand out.

Tending to the Talent

Even before they start screening for skills, competencies and experience, there are already conversations going on between prospective candidates and the hiring team. It’s not the hiring people doing the talking: there’s no phone tag or cumbersome emails. Instead, the candidates are engaging with a sophisticated virtual assistant. Candidates who show interest can do a pre-screening quickly with a chatbot, asking questions and getting a clearer picture on the position. Each conversation offers dynamic, responsive messaging and produces data on the candidate that the virtual assistant can share with the team at Talent Inc.

In the time it might take to reach out and have one initial conversation with one candidate, countless exchanges have already taken place and candidates are already engaged in the application process. There’s now a whole pool of candidates entering the talent pipeline, already having a positive experience and interested in finding out what comes next. Many of these candidates are digital natives, well used to interacting with chatbots and at ease with the process — and to them, the process implies that the employer is appealingly forward-thinking in its approach to business and people. Now candidates can start having real-time conversations with the recruiting team, who already know a great deal about each candidate before they talk — and can tailor their conversations based on what they know.

Making Sure the Fit Is Just Right

With 250 positions to fill, there’s little time to spend on potentially poor hires. But AI has already created predictive analytics on who may make the grade and be a great fit. A whole array of criteria has been used to create screenings and pinpoint promising matches, and the HR team can rely on the data to help narrow down the best candidates for each position — and find candidates that might be better fits for other positions they may not have applied for.

In each case, the hiring team can take time to get to know each candidate, whether in conversation or formal interviews, as the human recruiters are freed up from repetitive and tedious administrative tasks now being executed by the AI software. While the average recruiter only spends six seconds on a candidate’s resume, the team at Talent Inc. gets to know all the great candidates they can — and based on the data already gathered, there’s lots to talk about

Keeping the Hiring Process Going

Providing an outstanding candidate experience that really conveys the potential employer’s brand is a one of Talent Inc.’s core values. All the portals and dashboards prospective hires are using during this process are layered with the look, feel, mission and message of the employer. Interviews are being set up with the top-tier prospects within the company, but the employer and the hiring team have partnered on a new initiative of different interviewing tools.

A recent study on LinkedIn found that key hiring trends for 2018 include different kinds of interviews and conversations, adding more of a human side to the classic mano-a-mano. That may include online skills assessments — which may be built around the data AI has gathered already on candidates. There are VR options for “trying out” the position in the virtual workplace, job “auditions,” video interviews, and far more casual interviews that set both interviewer and candidate at ease and allow for more meaningful and spontaneous conversations. The data intelligence has enabled recruiters to use their emotional intelligence. Soon Talent Inc. has recommended a pool of terrific candidates for the expanding firm, is monitoring and facilitating the application process, and has also maintained connections with those who may not apply this round, but may in the future.

“21st-century HR isn’t about playing it safe,” noted IBM’s David Green in a recent article by Arya on the role of AI in HR. AI has enabled our hypothetical recruiters at Talent Inc. to keep their employer ahead of the competition — sourcing the best talent in an extremely short window of time using the power of data and AI, and the freedom these tools give them to provide a terrific candidate experience that reflects the employer and sets up hires for engagement and success.

Every interaction has added to the data gathered on each candidate, and improved the recruiter’s understanding of the relative strength and fit of that candidate with regards to the company. AI has predicted outcomes and suggested plans based on previous successes to drive better hires and forecast future hiring needs. AI has also kept a close watch on any skills gaps or problematic screenings, reducing risk and paying attention to ROI, while recruiters are spending more time with each candidate, establishing a connection and a relationship. The result is a whole crop of promising new hires who can help the organization continue its growth.

And based on the data gleaned during this hiring phase and over the course of onboarding, development, and indeed the employee journey, AI can improve the next hiring push even more. If I could pat the team at Talent Inc. on the virtual back, I would.

This post is sponsored by Leoforce.

How to Leverage AI Recruiting to Make Better Hires

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HR and recruiters don’t tend to take things at face value. For good reason: we’re called on to rely on our educated judgments. We’re in the business of futurecasting, person by person. We find the best talent with the most potential for doing great things for an employer in the near future, and we do it over and over again. But we’ve been up for a turbocharge for a long time. A career path that is this intense, combining administrative, personal, and strategic tasking constantly needs sophisticated ways to advance above old archaic practices we no longer want to rely on. With AI, we have it.

AI conducts its own version of futurecasting. It’s a fast and efficient supporting player that can scale up our efforts and free us the bandwidth so we can focus on the one-to-one. Fact is, AI is rapidly disrupting recruiting in a good way. But ask someone what it means and you may get a head-scratch. For anyone who’s been looking for a simple basic explanation of what and how AI recruiting works, here it is. One caveat: let’s not call this “AI for Dummies.” No one here is a dummy, and no matter how sophisticated AI is, talent acquisition needs your acumen, intelligence, and expertise.

Here’s a breakdown of five ways AI is taking recruiting to the next level, and knowing how to leverage what it’s capable of — that’s the ace up our sleeves.

Machine Learning

Machine learning is sometimes defined as the ability to “act without having to be programmed,” but what that means is that AI can comprehend, reason, and learn from every data point, interaction, and outcome. AI puts incredible muscle and speed into analyzing vast amounts of data and arriving at very specific, data-driven observations and predictions.

One way it works: It can find out if a certain hire might be a good fit or whether an employer is going to suffer from a skills gap. It can look at how we’ve been recruiting and find the weak points to make predictions and recommendations. And it can refine its own processes, looking at prior successes and failures to amplify or reframe its own approach.

Big Data

The cloud has essentially blown open the universe as far as the capacity for data. We’re now measuring data in terms of hundreds of zettabytes, being processed and archived and reprocessed and parsed at incredible speed. What we have to work with now was inconceivable even a year ago, let alone a decade, and its revolutionized talent sourcing. It’s not just about static information: this is data that can be accessed and analyzed from countless angles — with statistical models, predictive algorithms, innovative filters, with actionable results.

One way it works: Instead of a recruiter having to devote long hours to manually search through 200 contacts on a spreadsheet, AI creates a recruiting nerve center that can search and analyze massive volumes of applicants.

Pattern Recognition

Old-school recruiting, particularly for rapidly expanding organizations, could feel like searching for a needle in a haystack and like reinventing the same wheel over and over. AI can identify and learn from a recruiter’s most successful patterns — and then replicate them, adjusting for all manner of contexts or requirements. It can also find instances of bias and create ways to overcome them.

One way it works: we can take a job description, and use hiring successes from the past to find the most likely qualified candidates — wherever they are, from a database to a job board to social media. We can identify the likelihood of a hire being a success, identity the potential skills gaps or blind spots of weak points, clarify our best sources, and above all, retain the information. It becomes part an organization’s proprietary wisdom, building up a strong foundation for recruiting successes to come.

Messaging

There’s message — the DNA and brand culture an organization conveys, and then there’s messaging — which is, often, the way that DNA and culture are carried out into the talent market. What AI does is facilitate fast, effective, and dynamic messaging. It begins to build relationships with the right candidates as soon as they’re identified, engaging and even pre-screening them before they have their first real contact or interaction with a recruiter. But it’s not an alienating or generic form of messaging. It’s multilayered, highly attuned and customized to the individual organization and the individual candidate — based on the information already learned and collected, and integrated with an ATS.

One way it works: Chatbots are no longer an alien life-form online: they’re a part of our entire system of communication, commerce, fact finding, an accepted form of exchanging information. AI can provide meaningful, relevant answers to candidate’s questions, and then share this with the recruiter. It makes it possible to spark engagement, maintain and build a connection, and then pass the best candidates to the recruiter to get them started on the actual process of hiring. All without cumbersome emails threads, phone tag, or awkward texts.

Pipelining

AI packs a powerful punch: it can process massive amounts of recruiting, hiring, engagement, performance and behavioral data from millions of prospects. It can focus and search for skills, behavioral and even cultural matches. But even more than that, it empowers recruiters with the single most important resource to stay on target: a viable, dynamic, visible talent pipeline.

Frankly, it’s a game-changer: AI is a game-changing innovation that brings the best of HR to organizations no matter their size, location, or field. In this highly competitive talent market, it gives recruiters a vital resource. It enables recruiters to move candidates into the pipeline and keep track of them automatically, an effective way to maintain visibility across the broadest possible spectrum of talent that enables recruiters to act when they see a potential great fit. It’s also another way to overcome unconscious bias and increase diversity and inclusion.

What AI does is enhance the recruiting across the whole journey. It provides recruiters with a far broader and more accurately gathered pool of candidates, the tools to engage candidates sooner and more effectively, and the means to tend to a pipeline that can be searched and refined according to scaling or changing needs. It’s not really an option, either — as AI becomes part of how organizations function now, it’s changing the very Future of Work — even before we bring the talent to the door.

This post is sponsored by Leoforce.

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How Machine Learning is Changing Recruiting

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Companies approach recruiting in different ways. Facebook’s method, for example, involves acquisitions for human capital and a six-week onboarding boot camp. Of course, that’s not the norm: On average, a recent study found it takes 42 days—and a cost-per-hire of $4,129—to fill an open position. That adds up, especially since Bloomberg recently reported that approximately 10,000 members of the baby boomer generation reach retirement age every day. With numbers like that, it’s no wonder efficiency-boosting advancements in machine learning are beginning to challenge the HR status quo. Let’s explore how machine learning is changing recruiting.

How Businesses Use Algorithmic Assessments

Spotify. Waze. Netflix. Amazon. These are just a handful of the many companies that have opted-in to some form of AI or machine learning. Why the boom? There are lots of reasons, of course, but there’s a primary one that comes to mind: Using algorithmic assessments—i.e., a kind of machine learning—can speed existing processes for consumers and even predict their needs ahead of schedule. In other words, these companies know that to get ahead in this digital marketplace, they’ll need to compete on customer experience (CX). They’re using technology to get there.

The same principle holds true for HR and, more specifically, recruiting. Algorithmic assessments use statistics and historical data to predict whether or not a potential employee will perform well. The benefits of this process are numerous: Recruiters can consider more applicants in less time and with less effort. In addition, they have the added peace of mind that their decisions are backed by more data than subjectivity.

Let’s examine an international startup harnessing the power of algorithmic assessments.

From Reviewing Resumes to Reviewing Social Behavior

It’s impossible to tell everything about potential hires from resumes alone. What if you could incorporate that resume data with information about who applicants really are—their values, likes and dislikes, etc.—and then use machine learning to plug that information into an algorithm that could predict how well they fit within your corporate culture? That’s the premise behind the Indian startup Belong, a brand recently profiled by Forbes as one company leading the machine learning and recruiting charge.

Belong’s algorithms do two things: First, they analyze behavioral data from social networks, blogs, and other digital platforms in addition to the standard information like education, experience, and stated objectives found in CVs to gather “a more holistic and accurate” profile of each candidate. They also use machine learning to better understand the preferences and patterns of each hiring company. From all that data, Belong generates a tailored, SERP-esque database of matches, each unique to the company and the position.

Does it work? Belong’s method reportedly gets a minimum of a threefold increase in company-candidate engagement rates. With numbers like that and customers like Cisco and Amazon, I’d say they’re onto something.

Now, Belong was by no means the first to dip its toes into social when it comes to recruiting. Their application of machine learning to their solution, though, is their secret sauce. Note that my friend and colleague, Megan Biro, CEO of TalentCulture, has written extensively about the power of social media, especially in passive recruiting. The trifecta of relationships, conversation, and unprecedented access to digital tools is a veritable hotbed of HR potential—Meghan has even referred to it as the “tech meets HR marriage,” and I couldn’t agree more. We’ve both got eyes on what’s next.

Speaking of what’s next, are there any plans to shake up what recruiting looks like at your organization in the next year? If you’re looking for a change, what potential do you see in machine learning and HR? What challenges? Let me know in the comments.

Photo Credit: martinlouis2212 Flickr via Compfight cc

 This article was first published on FOW Media.

How Machine Learning is Changing Recruiting

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Companies approach recruiting in different ways. Facebook’s method, for example, involves acquisitions for human capital and a six-week onboarding boot camp. Of course, that’s not the norm: On average, a recent study found it takes 42 days—and a cost-per-hire of $4,129—to fill an open position. That adds up, especially since Bloomberg recently reported that approximately 10,000 members of the baby boomer generation reach retirement age every day. With numbers like that, it’s no wonder efficiency-boosting advancements in machine learning are beginning to challenge the HR status quo. Let’s explore how machine learning is changing recruiting.

How Businesses Use Algorithmic Assessments

Spotify. Waze. Netflix. Amazon. These are just a handful of the many companies that have opted-in to some form of AI or machine learning. Why the boom? There are lots of reasons, of course, but there’s a primary one that comes to mind: Using algorithmic assessments—i.e., a kind of machine learning—can speed existing processes for consumers and even predict their needs ahead of schedule. In other words, these companies know that to get ahead in this digital marketplace, they’ll need to compete on customer experience (CX). They’re using technology to get there.

The same principle holds true for HR and, more specifically, recruiting. Algorithmic assessments use statistics and historical data to predict whether or not a potential employee will perform well. The benefits of this process are numerous: Recruiters can consider more applicants in less time and with less effort. In addition, they have the added peace of mind that their decisions are backed by more data than subjectivity.

Let’s examine an international startup harnessing the power of algorithmic assessments.

From Reviewing Resumes to Reviewing Social Behavior

It’s impossible to tell everything about potential hires from resumes alone. What if you could incorporate that resume data with information about who applicants really are—their values, likes and dislikes, etc.—and then use machine learning to plug that information into an algorithm that could predict how well they fit within your corporate culture? That’s the premise behind the Indian startup Belong, a brand recently profiled by Forbes as one company leading the machine learning and recruiting charge.

Belong’s algorithms do two things: First, they analyze behavioral data from social networks, blogs, and other digital platforms in addition to the standard information like education, experience, and stated objectives found in CVs to gather “a more holistic and accurate” profile of each candidate. They also use machine learning to better understand the preferences and patterns of each hiring company. From all that data, Belong generates a tailored, SERP-esque database of matches, each unique to the company and the position.

Does it work? Belong’s method reportedly gets a minimum of a threefold increase in company-candidate engagement rates. With numbers like that and customers like Cisco and Amazon, I’d say they’re onto something.

Now, Belong was by no means the first to dip its toes into social when it comes to recruiting. Their application of machine learning to their solution, though, is their secret sauce. Note that my friend and colleague, Megan Biro, CEO of TalentCulture, has written extensively about the power of social media, especially in passive recruiting. The trifecta of relationships, conversation, and unprecedented access to digital tools is a veritable hotbed of HR potential—Meghan has even referred to it as the “tech meets HR marriage,” and I couldn’t agree more. We’ve both got eyes on what’s next.

Speaking of what’s next, are there any plans to shake up what recruiting looks like at your organization in the next year? If you’re looking for a change, what potential do you see in machine learning and HR? What challenges? Let me know in the comments.

Photo Credit: martinlouis2212 Flickr via Compfight cc

This article was first published on FOW Media.

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

Five HR Analytics Terms You Need to Know

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I love big data. I love it for many reasons, but, as I’ve said before, one of the main reasons is the way it’s “raised the profile” of HR and its importance. The sheer volume of information HR analytics can bring to the table has moved HR practitioners from an “out of sight out of mind” back room business function, to a major player when it comes to company goal setting and overall planning. Today we use HR analytics for everything from determining passive and active candidates; assisting with onboarding, training, and engagement; and predicting retention, attrition, and performance rates.

That said, the sheer volume of data available today for HR professionals to work with can feel overwhelming, and at times, paralyzing. Not only do we have mountains of data to interpret, but the data is constantly evolving, shifting, forming, and reforming as we learn about the newest technologies, which actively measure even more employment-related functions.

The key to getting your arms around big data and analytics is to do your research and start to understand it. And to do that you must become familiar with its “lingo”.

Five HR Analytics Terms You Need to Know

Before 2011, if you Googled “data scientists jobs” you would be lucky to find more than a handful of listings. That has changed, dramatically. In fact, by 2015 the demand for data scientists had surpassed the demand for statisticians. But if you’re not lucky enough to have a data scientist on your team, fear not. Knowing the following five terms will lead you one step closer toward all the benefits big data and analytics has to offer.

Data mining. Try and wrap your head around this number: Around 2.5 quintillion bytes of data are created every day. Clearly, it can’t all be analyzed. There’s not enough manpower on earth to get that job done. And that’s where data mining comes in. Akin to “panning for gold,” data mining involves sifting through raw data, and finding where patterns emerge. Analysts convert those patterns into tangible information, which then allows for relatively accurate prediction making about real life behaviors or events.

Machine learning piggybacks nicely off of data mining, as it’s often used to make that mining job just a little easier. Just as it sounds, computers can “learn” from the data ingested, helping to translate the data into recognizable patterns. You will sometimes see the term Artificial Intelligence used instead of “machine learning,” as AI is what provides computers with the tools they need to absorb and sift through new information.

Hiring channel mix modeling. There are myriad channels available today for talent managers looking to recruit, including—but not limited to—advertising, employee references, recruitment consultants, and social media. Hiring channel mix modeling allows HR pros to make use of previous data on all of the hiring channels employed in the past, and clearly map out which ones were most useful, allowing for more efficient, streamlined human resources departments and optimized hiring expenditures.

Cost modeling. Cost modeling helps those in the C-Suite understand many HR-driven costs. These include hiring and onboarding costs, the time estimated for an employee to reach full productivity, salary and productivity ratios, overall productivity, and employee turnover costs.  Cost Modeling can provide a rich “dollar value” picture of hiring and retention plans for a given year, and allow you to quantify costs associated with certain activities and processes (like mistakes made in hiring, voluntary turnover, etc.) 

People analytics. Simply put, people analytics involves combining all of the employee data in your organization—and using that data to understand and help predict potential business problems, issues like sales productivity, retention, fraud, customer satisfaction, and more. It effectively helps measure the success of both human resources practices and learning and development programs, and eventually (as new apps are developed) will begin measuring the value of different roles, leaders, and other business investments.

While “gut instinct” is always a good thing to have, long gone are the days when that and a fancy resume were the only things helping HR practitioners make hiring and other decisions. Now, big data and analytics can help HR teams run in tandem with those from other key departments, as well as play a significant part in helping their organization achieve success when it comes to business goals and strategies.

What do you think? Are there other HR analytics terms that you’ve encountered? Has your company delved into big data and all its potential? I’d love to hear your thoughts.

A version of this post was first published on Converge.xyz

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