Ai in HR Tech: Use Cases, Arising Startups and M&A Activity

This piece was originally created by Data Root Labs, edited and republished here with permission.

HR is one of those “human” jobs that will be hard to replace with AI. Akin to psychology, it requires a high level of EQ (quotient of emotional intelligence) and profound personal touch. The common preconception is that it’s very hard to augment, let alone automate. Over the last half-decade, this line of logic has come under fire, with data-driven approaches penetrating more and more human resource practices.

With the cost of AI adoption plummeting and the data science community growing by the minute, business leaders are starting to reach out to process automation and augmentation opportunities in the people operations realm. Gartner predicts that by 2022 (three years from now!) one in five workers engaged in non-routine tasks will rely on AI to do their jobs.

The key drivers fueling the growth of HR tech market include increasing need for managing the widespread workforce, growing demand for replacing the legacy systems, the increasing importance of candidate experience and technological proliferation in Big Data analytics, Machine Learning, Artificial Intelligence (AI), and Internet of Things (IoT). Thus, new report by Grand View Research, Inc. projects the global human resource management market to reach USD 30.01 billion by 2025, registering a CAGR of 11.0% from 2019 to 2025.

PwC forecasts that 20% of U.S. companies with AI initiatives will roll out AI across their business this year to both re-imagine jobs and work processes as well as grow profits and revenue.

A lot of AI use cases revolve around eliminating the tedious and routine tasks, while HR managers handle the most important side of the job – human interaction. We have identified the following key problems that AI can solve:

  • Efficiency improvement: automating repetitive tasks.
  • Recruitment and candidate assessment: more efficient and less biased hiring.
  • Employee development: coaching and engaging employees to enable personal growth.
  • Culture maintenance and HR management: identifying cultural issues and key areas for improvement.

Let’s look at those cases one by one:

Efficiency improvement

Automating repetitive tasks.

AI in HR and recruiting gets people to move away from repetitive tasks not to waste their talents. As a result, they can target their energies on more high-level tasks, such as finding promising employees and working to keep them with the company. HR staff will focus on being a strategic partner to business divisions rather than crunching data as a task processor. AI will empower companies to make faster decisions by:

  • Accessing up-to-date information.
  • Spotting trends through pattern recognition.
  • Using machine learning to identify past mistakes to avoid and accelerating processes.

Recruitment and candidate assessment

More efficient and less biased hiring.

One of the major disruptors in candidate hiring is HireVue’s artificial intelligence platform. It identifies and analyzes the tone, word choice, body language, question context, and answers of candidates who have recorded video interviews to determine if they’re a good fit – by skillset and culture. Their quantifiable score compares all the candidates, ensuring HireVue makes the optimal unbiased hiring decisions – all thanks to AI and Machine Learning.

AI helps companies through the process of pre-hire assessments. While assessments are nothing new, in the era of AI, a company can predict which candidate will be a better hire in the future. Sample this, a candidate applies for a role in a company by entering his resume. An AI-powered system analyses the resume and compares it to the successful employees in the same role.

A chatbot reaches out to the candidate and asks some pre-screening questions. They collect candidate data, and AI uses it to score the candidate and present the result to a recruiter.

Pymetrics uses AI-based gamified assessment to screen candidates. One of their customer’s hiring success rate has gone up by over 30% while eliminating all the “educational pedigree bias” inherent to the recruitment process faced by almost all companies out there. AI in recruitment is already huge.

Another player in the HR assessment game, Sapia, helps big brands to get the candidate experience right. They already have text data on 15% of the Australian population that helps identify with 85% accuracy the personality profile of applicants into all customer-facing roles (retail, sales, customer service, etc.). To reduce the hiring bias, the company doesn’t use CV data, nor it builds data models off the existing employees. They only use the text data ensuring that everyone gets a fair go at the role. That allows their clients to hire for the right values and behaviors. In addition, Sapia provides personalized personality reports to every applicant for every role. Amid low unemployment and the rule of social media, this is particularly important to consumer brands who measure the impact of poor candidate experience on customer attrition.

Employee development

Coaching and engaging employees to enable personal growth.

Learning and development (L&D) is a young HR practice and yet the global L&D industry is worth over $200B. Nearly half of the L&D opportunities organization forget, inappropriately apply, or waste on people that don’t want or need it.

Companies like Fuse are now building AI-based coaching tools that request feedback, read comments, and glean sentiment from employees and entire teams. They use data to match these individual and team issues against higher performing teams, giving managers and supervisors the requisite tools to do better.

As part of it, HR undertakes activities such as talent acquisition, employee management, performance management, succession management, etc.

For example, organizations like Greenhouse Software are integrating IBM-AI capabilities through the IBM Watson Candidate Assistant. It is a suite of AI-powered tools that matches jobs to the candidates and vice-versa based on their personalities, skills and interest areas. Greenhouse collects candidate data through various funnels including social media.

It observes minute details such as whether an employee submitted their work sample through LinkedIn or Glassdoor. It maintains scorecards for all the employees based on its own analysis with zero human intervention. This helps the interviewers sort through the applications ahead of time and keep the talent funnel ready.

Culture maintenance and HR management

Identifying cultural issues and key areas for improvement.

AI can look at organizational network data like email traffic, survey results and sentiment of comments to identify areas of stress, arising ethical dilemmas, and various forms of spreading toxicity within a company culture. This helps HR managers identify red flags faster and act in a preventive rather than reactive way.

Employee mental health is extremely important. People are the foundation of your company and they better be happy if management aims to achieve some lofty goals. To remedy some ongoing cultural issues, AI now identifies behaviors that cause poor work performance and disrupt the balance of your working environment. A new breed of intelligent chatbots can ease these situations by providing interactions in an intelligent and easy-going manner while alerting the HR department of any cases that go beyond the red line and need in person handling.

The above are some of the key cases that new technologies enable. For a deeper understanding of the innovation happening in HR tech, take a look at the following infographic by Will Saborio at Silicon Salsa.

Many of these companies already have AI and Big Data components.

ai-in-hr-cos(fig. 1) The early-stage HR tech landscape

M&A activity in HR sector

The mergers and acquisitions activity in the past year has been on the rise in HR tech, characterized by both more deals and bigger volume of deals. The trend for consolidation is strong as companies look to increase scale, add new customers and product lines, and penetrate new geographies.

From Recruit acquiring Glassdoor for $1.2B and K1 Investment Management acquiring Jobvite for $135M to SAP’s acquisition of Qualtrics for $8B in cash, there is no lack of jaw-dropping deals in HR tech from both financial and strategic buyers.

What are the most important factors for the buyers? The buyers are willing to pay more for high growth, SaaS product offering, dominance in a particular vertical or geography, ability to scale current solution, sticky enterprise customers, and clean financials. Yet the ultimate factor that determines the highest price possible is the 2nd (or even 3rd) acquisition offer on the table forcing buyers compete for a given company.

With a changing and developing technology landscape, we predict a continuous increase in HR tech M&A activity. In the table below, we summarize the key recent M&A events in HR tech:

Company HQ / year founded Amount Raised, $ Deal Amount, $ Acquirer Deal Rationale
Get BoxSuite Pty Australia / 2009 N/A 1.4M ELMO Software Acquiring cutting edge native SaaS, cloud-based technology which will disrupt the large and growing rostering and time & attendance market.
Imaginatik US / 1994 2.6M 1.7M Planbox The new combined company will expand Imaginatik’s UK and US operations.
Scannel Solutions Ireland / 1998 N/A 4.5M Ideagen Acquiring a leader in Environmental Health, Safety and Quality solutions, which it offers via a SaaS platform, will enable Ideagen to grow both SaaS capabilities and accelerate EHSQ offering.
The Sage Group (Sage Payroll Solutions) US / 2016 N/A 94M iSolved HCM (Accel-KKR Company) Sage’s payroll processing SaaS solutions will accelerate iSolved’s growth in HCM industry, specifically targeted toward the midmarket, while increasing its licensee and partner network as well.
Jobvite US / 2006 256M 135M K1 Investment Management Jobvite, a provider of analytics-based recruitment management SaaS for businesses. K1 and Jobvite also announced the acquisition of Talemetry, RolePoint, and Canvas. The investment by K1 and acquisitions will enable Jobvite to create a comprehensive, end-to-end talent acquisition platform.
Workmarket US / 2010 66M 400M ADP To add agile tool to convince enterprises to use it as part of a larger system of workforce products and compete with smaller companies.
Glassdoor US / 2007 204.5M 1200M Recruite Acquiring a leading job and recruiting company well known for providing greater workplace transparency.
Ultimate Software US / 1990 19.1M 11000M Hellman & Friedman Capital Partners PE deal. The acquisition will allow the company to utilize financial and strategic advice to bring new features and services to the market more quickly.
ThinkHR US / 2005 82.5M N/A Mammoth To expand Mammoth product and service offerings, leverage complementary capabilities and expertise.
ePoise India / 2013 0.5M N/A Zoho Likely, an acquihire of a hiring automation product startup’s team.
Aasaanjobs India / 2004 6.5M N/A OLX To strengthen OLX position in the online job search segment by adding blue collar job vertical.
Le & Associates Vietnam / 2001 N/A N/A Trust Tech 44.42% stake in L&A investment corporation, which owns Vietnam HR company Le & Associates. The acquisition comes as part of Trust Tech’s plan of expanding its businesses in different countries which is part of its strategy in its medium-term management plan.
Zugata US / 2014 10.2M N/A Culture Amp Zugata’s acquisition allows Culture Amp to bring an increasingly sophisticated use of data across the employee lifecycle.
Rallyteam US / 2013 8.6M N/A Workday Rallyteam, a talent mobility platform that uses machine learning to help companies better understand and optimise their workforces by matching a worker’s interests, skills, and connections with relevant jobs, projects, tasks, and people.
Jibe US / 2009 41M N/A iCIMS iCIMS acquires Jibe to provide employers best-in-class candidate engagement and recruitment marketing capabilities. Jibe’s talent and jobs matching capability is powered by Opening, a company later mentioned in this article.

Startups and investments

In Q2 2019 alone, VCs have poured $1.448B of VC investment into HR tech. The categories that saw a spike in investments are Wellness (Gympass, $300M round ), Benefits (Collective Health, $205M), and Core HR (HR Path, $112.5M, Payfit $78.6M). When it comes to AI-focused startups, the results are also impressive. In the table below we summarize the biggest rounds and most interesting deals around companies using AI in HR:

Company HQ / year founded Amount Raised, $ Investors What they are doing
Visier Canada / 2010 91.5M D Sorenson Capital, Adams Street Partners, Summit Partners Visier People™, the leading people analytics and workforce planning solution, provides with answers to hundreds of pre-built, best practice questions about workforce, across HR and business topics to strategically manage a complex workforce.
Wrkit Ireland / 2016 N/A Enterprise Ireland Wrkit is a one stop shop to inspire better, healthier, working environments. With the help of AI and automation, wrkit specialises in the creation of better, healthier working environments using our online suite of data driven Employee Engagement & Retention tools.
Sapia Australia / 2013 3M A Capital Zed, Rampersand Sapia is an online platform which takes a data-first approach to help businesses hire the right people. By leveraging the power of technology, Sapia helps quickly evaluate a large number of applications and narrow down the list to those with suitable profiles and ensuring quality candidate experience along the way.
Lumity US / 2013 33M B Social Capital, True Ventures, Threshold, Rock Health Lumity simplifies the pain of company health plan decisions with data-driven recommendations that drive cost savings and improve outcomes.
Jumpstart US / 2017 4.2M Seed Michael Lynton, Joshua Steiner, Glenn Dubin Jumpstart is a machine learning platform that enables students to learn, discover and connect with the most innovative companies in the world. The company aims to create equal opportunity for students in a highly competitive and biased industry by learning about a students interests, values and experiences and making intelligent matches using data and technology.
PredictiveHR US / 2016 1M Seed Trendata AI Powered platform helps aggregate and normalize data across disparate systems to create rich visualizations and predictive people analytics.
HireVue US / 2004 93M E Sequoia Capital Combining predictive, validated industrial/organizational science with artificial intelligence allows recruiting professionals to augment human decision-making in the hiring process, delivering higher quality talent, faster. HireVue has hosted over six million interviews for more than 700 customers worldwide.
Pymetrics US / 2013 56M B General Atlantic Pymetrics develops neuroscience-based assessment and prediction technology to transform the way companies hire, retain, and develop their employees. It offers cognitive and emotional assessment solutions; and a personalized and dynamic recommendation engine for recruiting/hiring, retaining, and developing talent.
JobRocker Austria / 2015 1.83 A Surplus Invest Online job search portal that connects applicants’ CVs with job openings using a proprietary algorithm and further human-driven consulting.
MoBerries Germany / 2015 1.8M E High-Tech Gründerfonds Automated ranking system that matches applicants with companies searching for new hires. The goal now is to build a screening bot for selecting candidates before interviews, as the founders deeply believe the candidates’ pre-filtering phase can be fully automated.
Productive Mobile Germany / 2014 3.4M E HV Holtzbrinck Ventures Human Process Augmentation (HPA). Their software disrupts the way enterprise software workflows are built, optimized and automated because of its fast implementation and lower costs, making humans more productive.
MeetFrank Estonia / 2017 1M E Hummingbird VC, Karma VC, and Change Ventures Сhatbot that interacts directly with applicants, using AI and machine learning. The app analyzes the needs of users and proposes job advertisements that match with the candidates’ profile and abilities, from the jobs pool. If the user is interested in one of the suggested positions, he or she can start a private and anonymous chat with the company.
Opening Ireland / 2015 $600K Seed NDRC Opening builds cognitive talent and jobs matching solutions and helps HR technology vendors, enterprises and staffing agencies leverage the power of natural language understanding to create smarter talent solutions in days, not months. Opening’s core technology, Baikal AI, is the world’s deepest and clearest data lake for talent and jobs data. Baikal AI, combines the key innovations in deep learning and natural language understanding, and simplifies the deployment of talent and job matching models.
Mya Systems US / 2011 32.4M B Foundation Capital A conversational AI equipped with both natural language understanding and natural language generation. Mya aims to automate sourcing, screening, and scheduling for recruiters, initiating conversation with candidates right after they apply and assess them for baseline requirements with real dialogue.
Textio US / 2014 29.5M B Scale Venture Partners Textio’s ‘augmented writing platform’ helps companies create better, more effective job listings. Textio’s predictive engine analyzes global hiring data from over 10 million jobs and their associated hiring outcomes every month, to uncover the language patterns that lead to successful job postings. It then uses this intelligence to make real-time recommendations as you craft your listing, predicting its performance and guiding you to an ultimately solid job post.
Entelo US / 2011 40.7M C U.S. Venture Partners (USVP) Entelo identifies those candidates who are most likely to be open to new opportunities, removing the pre-qualification layer from recruiters’ responsibilities. Additionally, the Entelo platform includes a robust candidate search database; recruiter email tracking, management, and analytics; algorithm and search filters built specifically for diversity initiatives, and more.
Restless Bandit US / 2014 10M A Toba Capital An artificial intelligence tool that finds both passive and active qualified talent from a candidate pool of over 100 million, and engages them and only them. Using discovery and rediscovery algorithms, Restless Bandit searches for candidates externally and within your ATS, respectively. It also intelligently and automatically retargets top prospects.
Hire Abby US / 2018 to watch Hire Abby helps companies gather applicant intel and predict a candidate’s potential fit for the company. majorly improves the candidate experience, while also helping the business make better, faster hiring decisions.
Paradox US / 2016 13.3M A Paradox’s AI recruiting assistant, Olivia, takes the focus on candidate experience to a new level. She uses advanced natural language processing to answer all your applicants’ questions; she has real, one-to-one conversations with them through the channel of their choosing — web.
Jobiak US / 2018 to watch Jobiak provides the industry’s first AI-based recruitment marketing platform that is designed to quickly and directly publish job postings to Google for Jobs, maximize their visibility and accelerate the flow of qualified candidates.
Harver  US / 2013 14M A Insight Venture Partners The company’s TalentPitch predicts which applicant performs the best. The experience is tailor-made for organizations that give a realistic preview of the job while collecting success-predicting data. Harver algorithms calculate the likeliness of success for each applicant.
Hackajob UK / 2014 8M A AXA Venture Partners, Downing Ventures Hackajob is a data-driven and engaging recruitment platform that matches top digital talent with exciting companies.
Uncommon US / 2015 18M A Spark Capital, Zeev Ventures, Canaan Parners Uncommon Co. uses artificial intelligence to identify the requirements for job postings and matching those jobs to qualified applicant.
Astound US / 2016 11.5M A Pelion Venture Partners, Vertex Ventures US The future of AI for employee service: support automation using NLP and machine learning. US / 2016 Seed Pete Flint Stella is an online recruitment agency that focuses on reducing time to hire with the use of artificial intelligence. The company helps to speed up hiring by 80% for any position using artificial intelligence to pre-qualify talent.
Scoutible  US / 2015 6.5M Seed Learn Capital, Mark Cuban Scoutible is a game-based hiring platform, using immersive mobile games to pinpoint perfect-fit candidates for jobs. Scoutible’s patent-pending technology identifies players’ unique cognitive and personality traits through gameplay, then spots opportunities where players’ attributes match those of companies’ proven top performers.
Headstart UK / 2016 1.6M Seed Plug & Play, Momentum, FoundersX, Tenaya Capital, others Headstart is diversity recruiting software focused on breaking the cycle of exclusion.
Ambit Analytics  US / 2017 1.1M Seed Romulus Capital Ambit Analytics helps leaders build collaborative teams through insights on how they talk and listen.
Beaconforce US / 2017 800K Pre-seed Beaconforce has developed a methodology and SaaS platform that combines Artificial and Emotional Intelligence to create challenging, motivating, and productive work environments.
Censia US / 2017 7.6M Seed Streamlined Ventures, Plug&Play, X Factor Ventures Censia empowers talent acquisition by equipping recruiters with the power of artificial intelligence to eliminate low-value work, error and bias. The result is substantially better, faster hiring decisions that ultimately drive revenue with every hire.
Remesh US / 2014 13.8M A General Catalyst, others Remesh empowers researchers and executives to have a dynamic conversation with up to 1,000 participants- online and in real-time. The Remesh platform uses AI to understand, analyze, and segment the vast amount of open-ended responses pouring in- in a matter of minutes.
Leena AI India / 2015 2M Seed Y Combinator, angels Leena is smart AI powered HR companion dedicated towards engaging employees. Leena is powered by ChatterOn, a cutting-edge AI chatbot development platform with self- learning cognitive capabilities, which leverages 10 Mn+ conversational data points from over 12,000 businesses.
Enboarder Australia / 2012 12M A Greycroft, Next Coast Ventures, Stage 2 Capital, Thrive Global, Venmo, others Enboarder is an onboarding and engagement platform that focuses on new hire experience and engagement rather than just tasks, forms and paperwork.
dotin US / 2012 1.2M Seed Amar Chokhawala, Bishop Ranch Intelligence Innovation Accelerator, Net One System, Ganesh Iyer. is a dedicated to understanding the art of decisions made by the subconscious mind to yield powerful business outcomes. By using the science of psychological, structured/unstructured social or enterprise data and machine learning, the company taps into the subconscious mind of humans enabling to capture the true digital personality fingerprint of every user.
Helena by Woo US + Israel / 2015 7M A Lord David Alliance, Acecap, Microsoft Scaleup TelAviv, others Woo, the marketplace for matching employers and ‘passive’ job seekers, has launched Helena, an AI-driven headhunter that automatically scouts, approaches and sources the best candidates on behalf of employers.
SmartDreamers Romania / 2014 2.1M Seed Gapminder VC, Catalyst Romania, 3TS Capital Partners SmartDreamers is a Recruitment Marketing Automation platform that empowers recruitment teams to smartly advertise jobs across the web. Integrated with Facebook, Google Ads, YouTube, Snap, Instagram, publishers and niche websites, SmartDreamers streamlines the recruitment marketing processes. SmartDreamers helps companies such as Uber, Vodafone, Siemens, Orange, IBM and many others to smartly recruit talent.
Botbot.AI Singapore / 2017 to watch Botbot.AI is an enterprise productivity solution that uses chat as an interface to automate business processes and workflows in order to move people away from low-value, menial and transactional work and refocusing them on high-cognition, high-value tasks, driving enterprise productivity and elevating the level of engagement.
Ambit Analytics US / 2017 1.1M Seed Romulus Capital Ambit Analytics helps leaders build collaborative teams through insights on how they talk and listen. A spin-off from SRI International, the birthplace of Apple’s Siri and Nuance, Ambit leverages world-class audio AI technology to quantify verbal communication and uses those metrics to train people to become better communicators.
Talespin US / 2015 5.6M A Talespin is disrupting the future of work through radical change in enterprise tools. By leveraging the power of artificial intelligence (AI), virtual reality (VR), and augmented reality (AR) Talespin is changing the way people engage, educate, and empower the next generation of the workforce.
AmazingHiring US / 2012 800K AltaIR Capital, SMRK, Starta Ventures AmazingHiring is a web application for technical talents acquisition. It automatically searches for the right candidates across 50+ sources. AI-based technology helps to save time on candidate pre-screening. Powered by AI technology, trusted by 6000+ recruiters. Clients already include VMware, Nvidia, Dell, Intercom, Capgemini, and ThoughtWorks.

Final thoughts and real cases

The human-AI collaboration model is essential for a people-oriented domain like HR. It’s unlikely that AI will replicate the nature of human relationships and its nuance. That said, advantages of AI in HR can help us attune to people operations and make better decisions supported by solid data.

Since AI is developing and new opportunities are opening up, the ways companies use AI and the impact of AI in HR and recruiting will also change. The future of AI in HR processes is clear – AI will affect every organization. The question is how companies adapt and which processes they choose to reinvent or improve.

“Companies will need to be mindful of existing biases and work to ensure that AI does not perpetuate the problem”Ben Reuveni, the CEO of Workey

We at DataRoot Labs work with various HR-tech enterprises and startups. We help them build out AI-powered MVPs or transform enterprises by reinventing their HR systems with AI. Below, you may find practical cases on how we have solved real HR challenges with AI technologies:


Contact Centre recruitment & retention – this will blow your mind!

Imagine being able to dial-up (or down) any chosen metric such as NPS, retention, absenteeism, staff turnover or any performance data point simply through smarter, predictive, data-driven hiring.

Predictive Talent Analytics turns the imaginary into reality, presenting a variety of businesses, including contact centres, with the opportunity to improve hiring outcomes and raise the performance bar. With only a minor tweak to existing business processes, predictive talent analytics addresses challenge faced by many contact centres.

Recruitment typically involves face-to-face or telephone interviews and psychometric or situational awareness tests. However, there’s an opportunity to make better hires and to achieve better outcomes through the use of Predictive Talent Analytics.

Many organisations are already using analytics to help with their talent processes. Crucially, these are descriptive analytical tools. They’re reporting the past and present. They aren’t looking forward to tomorrow and that’s key. If the business is moving forward your talent tools should also be pointing in the same direction.

Consider a call-waiting display board showing missed and waiting calls. This is reporting.

Alternatively, consider a board that does the same but also accurately predicts significant increases in call volumes, providing you with enough time to increase staffing levels appropriately. That’s predictive.

Descriptive analytical tools showing the path to achievement taken by good performers within the business can add value. But does that mean that every candidate within a bracketed level of academic achievement, from a particular socio-economic background, from a certain area of town or from a particular job board is right for your business? It’s unlikely! Psychometric tests add value but does that mean that everyone within a pre-set number of personality types will be a good fit for your business? That’s also unlikely.

The simple truth is that, even with psychometric testing and rigorous interviews, people are still cycling out of contact centres and the same business challenges remain.

With only a minor tweak to talent processes, predictive talent analytics presents an opportunity to harness existing data and drive the business forward by making hiring recommendations based on somebody’s future capability.

Telling you who is more likely to stay and produce better results for your business.

But wait, it gets better!

Pick the right predictive talent analytics tool and this can be done in an interesting, innovative and intriguing way taking approximately five minutes.

Once the tool’s algorithm knows what good looks like, crucially within your business (because every company is different!), your talent acquisition team can approach the wider talent market armed with a new tool that will drive up efficiency and performance.

Picking the right hires, first time.

Predictive talent analytics boosts business performance

  • Volume & time – with the right choice of tool, your talent team can simultaneously engage hundreds or thousands of candidates and, within a few minutes, be shown which applicants should be at the top of the talent pile because the data is showing they’ll be a good hire.
  • Retention – Each hiring intake is full of talent with the capability to perform for the business. An algorithm has effectively asked thousands of questions and subsequently identified the people who will be capable performers, specifically for your business.
  • Goodbye generic – Your business is unique. If the algorithm provided by your predictive analytics provider is unique to your business, then every single candidate prediction is personalised. A contact centre has the potential to analyse thousands of candidates and pick the individuals who best fit the specific requirements of the business or team, driven by data.

Consider this. Candidate A has solid, recent, relevant experience and good academic grades, ticking all the right hiring boxes but post-hire subsequently cycles out of the business in a few months.

Candidate B is a recent school-leaver with poor grades, no work history but receives a high-performance prediction and, once trained, becomes an excellent employee for many years to come.

On paper candidate A is the better prospect but with the fullness of time, candidate B, identified using predictive talent analytics, is the better hire.

Instead of using generic personality bandings to make hiring decisions, use a different solution.

Use predictive talent analytics to rapidly identify people who will generate more sales or any other measured output. Find those who will be absent less or those who will help the business achieve a higher NPS. Bring applicants into the recruitment pipeline knowing the data is showing they will be a capable, or excellent, performer for your business.

Now that’s an opportunity worth grasping!

Steven John worked within contact centres whilst studying at university, was a recruiter for 13 years and is now Business Development Manager at Sapia, a leading workforce science business providing a data-driven prediction with every hire. This article was originally written for the UK Contact Centre Forum

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Algorithmic Hiring to Improve Social Mobility

It is a widely held belief that diversity brings strength to the workplace through different perspectives and talents.

The value is greatest when companies harness the differences between employees from multiple demographic backgrounds to understand and appeal to a broad customer base. But true diversity relies on social mobility and therein lies the problem: the rate of social mobility in the UK is the worst in the developed world.

The root cause of the UK’s lack of social mobility can be found in the very place that it can bring the most value – the workplace. Employers’ recruiting processes often suffer from unconscious human bias that results in involuntary discrimination. As a result, the correlation between what an employee in the UK earns today and what his or her father earned is more apparent than in any other major economy.

This article explores the barriers to occupational mobility in the UK and the growing use of predictive analytics or algorithmic hiring to neutralise unintentional prejudice against age, academic background, class, ethnicity, colour, gender, disability, sexual orientation and religion.

The government wants to promote equal opportunity

The UK government has highlighted the fact that ‘patterns of inequality are imprinted from one generation to the next’ and has pledged to make their vision of a socially mobile country a reality. At the recent Conservative party conference in Manchester, David Cameron condemned the country’s lack of social mobility as unacceptable for ‘the party of aspiration’. Some of the eye-opening statistics quoted by Cameron include:

  • 7% of the UK population has been privately educated.
  • 22% of FTSE 350 chief executives have been privately educated.
  • 44% within the creative industries have been privately educated.
  • By the age of three, children from disadvantaged families are already nine months behind their upper middle class peers.
  • At sixteen, children receiving school meals will on average achieve 1.7 grades lower in their GCSEs.
  • For A levels, the school one attends has a disproportionate effect on A* level achievement; 30% of A* achievers attend an independent school, while children attending such schools make up merely 7% of the general population.
  • Independent school graduates make up 32% of MPs, 51% of medics, 54% of FTSE 100 chief executives, 54% of top journalists and 70% of High Court judges.
  • By the age of 42, those educated privately will earn on average £200,000 more than those educated at state school.

Social immobility is an economic problem as well as a social problem

The OECD claims that income inequality cost the UK 9% in GDP growth between 1990 and 2010. Fewer educational opportunities for disadvantaged individuals had the effect of lowering social mobility and hampering skills development. Those from poor socio economic backgrounds may be just as talented as their privately educated contemporaries and perhaps the missing link in bridging the skills gap in the UK. Various industry sectors have hit out at the government’s immigration policy, claiming this widens the country’s skills gap still further.

Besides immigration, there are other barriers to social mobility within the UK that need to be lifted. Research by Deloitte has shown that 35% of jobs over the next 20 years will be automated. These are mainly unskilled roles that will impact people from low incomes. Rather than relying too heavily on skilled immigrants, the country needs to invest in training and development to upskill young people and provide home-grown talent to meet the future needs of the UK economy. Countries that promote equal opportunity for everyone from an early age are those that will grow and prosper.

How are employers supporting the government’s social mobility policy?

The UK government’s proposal to tackle the issue of social mobility, both in education and in the workplace, has to be greatly welcomed. Cameron cited evidence that people with white-sounding names are more likely to get job interviews than equally qualified people with ethnic names, a trend that he described as ‘disgraceful’. He also referred to employers discriminating against gay people and the need to close the pay gap between men and women. Some major employers – including Deloitte, HSBC, the BBC and the NHS – are combatting this issue by introducing blind-name CVs, where the candidate’s name is blocked out on the CV and the initial screening process. UCAS has also adopted this approach in light of the fact that 36% of ethnic minority applicants from 2010-2012 received places at Russell Group universities, compared with 55% of white applicants.

Although blind-name CVs avoid initial discriminatory biases in an attempt to improve diversity in the workforce, recruiters may still be subject to similar or other biases later in the hiring process. Some law firms, for example, still insist on recruiting Oxbridge graduates, when in fact their skillset may not correlate positively with the job or company culture. While conscious human bias can only be changed through education, lobbying and a shift in attitude, a great deal can be done to eliminate unconscious human bias through predictive analytics or algorithmic hiring.

How can algorithmic hiring help?

Bias in the hiring process not only thwarts social mobility but is detrimental to productivity, profitability and brand value. The best way to remove such bias is to shift reliance from humans to data science and algorithms. Human subjectivity relies on gut feel and is liable to passive bias or, at worst, active discrimination. If an employer genuinely wants to ignore a candidate’s schooling, racial background or social class, these variables can be hidden. Algorithms can have a non-discriminatory output as long as the data used to build them is also of a non-discriminatory nature.

Predictive analytics is an objective way of analysing relevant variables – such as biodata, pre-hire attitudes and personality traits – to determine which candidates are likely to perform best in their roles. By blocking out social background data, informed hiring decisions can be made that have a positive impact on company performance. The primary aim of predictive analytics is to improve organisational profitability, while a positive impact on social mobility is a healthy by-product.

An example of predictive analytics at work

A recent study in the USA revealed that the dropout rate at university will lead to a shortage of qualified graduates in the market (3 million deficit in the short term, rising to 16 million by 2025). Predictive analytics was trialled to anticipate early signs of struggle among students and to reach out with additional coaching and support. As a result, within the state of Georgia student retention rates increased by 5% and the time needed to earn a degree decreased by almost half a semester. The programme ascertained that students from high-income families were ten times more likely to complete their course than those from low-income households, enabling preventative measures to be put in place to help students from socially deprived backgrounds to succeed.

What can be done to combat the biases that affect recruitment?

Bias and stereotyping are in-built physiological behaviours that help humans identify kinship and avoid dangerous circumstances. Such behaviours, however, cloud our judgement when it comes to recruitment decisions. More companies are shifting from a subjective recruitment process to a more objective process, which leads to decision making based on factual evidence. According to the CIPD, on average one-third of companies use assessment centres as a method to select an employee from their candidate pool. This no doubt helps to reduce subjectivity but does not eradicate it completely, as peer group bias can still be brought to bear on the outcome.

Two of the main biases which may be detrimental to hiring decisions are ‘Affinity bias’ and ‘Status Quo bias’. ‘Affinity bias’ leads to people recruiting those who are similar to themselves, while ‘Status Quo bias’ leads to recruitment decisions based on the likeness candidates have with previous hires. Recruiting on this basis may fail to match the selected person’s attributes with the requirements of the job.

Undoubtedly it is important to get along with those who will be joining the company. The key is to use data-driven modelling to narrow down the search in an objective manner before selecting based on compatibility. Predictive analytics can project how a person will fare by comparing candidate data with that of existing employees deemed to be h3 performers and relying on metrics that are devoid of the type of questioning that could lead to the discriminatory biases that inhibit social mobility.

“When it comes to making final decisions, the more data-driven recruiting managers can be, the better.”

Bias works on many levels of consciousness

‘Heuristic bias’ is another example of normal human behaviour that influences hiring decisions. Also known as ‘Confirmation bias’, it allows us to quickly make sense of a complex environment by drawing upon relevant known information to substantiate our reasoning. Since it is anchored on personal experience, it is by default arbitrary and can give rise to an incorrect assessment.

Other forms of bias include ‘Contrast bias’, when a candidate is compared with the previous one instead of comparing his or her individual skills and attributes to those required for the job. ‘Halo bias’ is when a recruiter sees one great thing about a candidate and allows that to sway opinion on everything else about that candidate. The opposite is ‘Horns bias’, where the recruiter sees one bad thing about a candidate and lets it cloud opinion on all their other attributes. Again, predictive analytics precludes all these forms of bias by sticking to the facts.

Age is firmly on the agenda in the world of recruitment, yet it has been reported that over 50% of recruiters who record age in the hiring process do not employ people older than themselves. Disabled candidates are often discriminated against because recruiters cannot see past the disability. Even these fundamental stereotypes and biases can be avoided through data-driven analytics that cut to the core in matching attitudes, skills and personality to job requirements.

Once objective decisions have been made, companies need to have the confidence not to overturn them and revert to reliance on one-to-one interviews, which have low predictive power. The CIPD cautions against this and advocates a pure, data-driven approach: ‘When it comes to making final decisions, the more data-driven recruiting managers can be, the better’.

The government’s strategy for social mobility states that ‘tackling the opportunity deficit – creating an open, socially mobile society – is our guiding purpose’ but that ‘by definition, this is a long-term undertaking. There is no magic wand we can wave to see immediate effects.’ Being aware of bias is just the first step in minimising its negative effect in the hiring process. Algorithmic hiring is not the only solution but, if supported by the government and key trade bodies, it can go a long way towards remedying the inherent weakness in current recruitment practice. Once the UK’s leading businesses begin to witness the benefits of a genuinely diverse workforce in terms of increased productivity and profitability, predictive hiring will become a self-fulfilling prophecy.

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Predictive Analytics: Your resolution will fail, here’s a promise you can keep

When technology is used in the right way it can enhance and improve our lives

During this seasonal holiday a great many of us will start to create plans for the forthcoming New Year. We’ll think about events, occurrences and happenings of the year gone by and many will commit to doing things better next year.

Even though studies have shown that only 8% of people keep their New Year’s resolutions , we still make (and subsequently break) them. But the intention was there, so good work!

Have you ever stopped to think about the processes your brain undertakes to enable you to set your goals for the New Year? No? Well, luckily I’ve done that bit for you. To make that resolution you combined your current and historical personal data and produced a future outcome, factoring in the probability of success, based on your analysis. A form of predictive analytics, if you like!

Predictive Analytics.

Using historical data to predict tomorrow’s outcomes

Thinking about those things you did (and didn’t do) this year and predicting/projecting for next year.

So now you know what it involves and we are (loosely) agreed that you’re on board with predictive analytics, when better than to tell you now that 2016 is going to be the year when we really start to see the benefits of predictive analytics within our jobs and people functions at work.

I think it’s now universally accepted that when technology is used in the right way it can enhance and improve our lives across every sector and industry. Most fields have seen significant developments over the last 20-30 years as technology is increasingly used to further our understanding of the way things work, enabling us to make better decisions in areas such as medicine, sport, communication and, arguably, even dating (predictive analytics is used in all of those sectors by the way!) so why not use it to help us find the right people for the right organisations?

Did you know you no longer need a top-class honours degree to work at Google?

Every employee is put through their analytics process allowing the business to match the right person with the right team, giving each individual the best environment to allow their talent to flourish.

Companies such as E&Y and Deloitte are using different methods to tackle the same problem – removing conscious and subconscious bias attached to the name and/or perceived quality of the university where applicants studied.

Airlines, retail, BPOs, recruitment firms a growing number of businesses within these sectors are using or on-boarding predictive analytics to achieve upturns in profits, productivity and achieving a more diverse and happier workforce.

Predictive analytics helps us make people and talent decisions to positively influence tomorrow’s business performance without bias, so I guess the question is this – if it’s already a proven science to achieve results, why isn’t everyone doing it? How long until everyone starts to use, and see the benefits, of predictive analytics?

Which logically raises the question: what are the benefits?

  • Time efficiencies – wouldn’t it be great for all parties in an interview to know that the data is indicating this role and person are a good fit before the candidate walks into the room? Hopefully reducing the reliance on both parties to be having a “good day”.
  • Diversity, inclusion removing the optics so often associated with a role. No more stated, or implied, previous sales / retail / PR experience but instead you can attract people from as broad a spectrum as possible knowing the data will help identify those candidates who have the foundation for success within your business and could well be your next superstars!
  • Churn/attrition – wouldn’t it be great to know that you can fill your 10 / 50 / 2000 seasonal/part-time roles from a pool of candidates who will have a higher chance of staying with the business longer, becoming successful brand ambassadors for your company leading to happier staff and customers alike.
  • Unique to your business – wouldn’t it be fantastic to know that all of these predictions are tailored purely for your business? For example, knowing that a candidate not overachieving in their previous role at one of your competitors isn’t reflective of their potential and that you can take advantage of their previous training and knowledge because the data says they’re going to be a better performer within your business.

Data can be big and it can be daunting, but it can also be invaluable if you ask and frame the right questions and combine the answers with human knowledge and experience. You will be surprised by the insights, knowledge and benefits that your business can obtain from even the smallest amounts of data. Data you probably already collect, even if it’s unknowingly or unwittingly!

List of Recruiting Resolutions

So as you start rummaging through your brain trying to remember where you filed your finest seasonal outfit(s) (that might just be me!), start prepping for the new year budgets, or start writing your list of resolutions let me help you frame a few questions:

  • Does your sector suffer from a skills shortage?
  • Would your company like to know which candidates from another sector have a higher likelihood of success post-training?
  • Would your business like to see an upturn in performance or people metrics such as increased sales, decreased absenteeism, longer tenure for better performers or a more diverse workforce? Would your Finance, Talent or HR head of department like to see an improvement in the variety of measures that indicate a better, more productive and happier workforce?

Statistically, your personal New Year’s resolution is unlikely to be on course in 12-months time so instead, why not make a resolution to bring predictive analytics into your talent processes in the upcoming year?

You’ll see the benefits for years to come, and that’s a promise we can both keep.

Happy holidays!

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