‘What engages us’ is curated by the PredictiveHire team, a team of pioneers working at the frontier of 3 huge trends:

1. AI in HR, especially people selection. Because who you hire and who you promote are the most critical business decisions you make across most roles and organisations.
2. Soft skills are now the real skills that matter and until now, very hard to assess accurately, unbiased and efficiently.
3. Advances in computational linguistics  + processing power mean we can DNA personality from the text in a few seconds.

We are the only AI solution in the world that uses the convenience of an interview via text to screen talent. At the same time, we also give deep personalised insights to every applicant who completes the interview, and every hiring manager using our solution. The absence of any subjective information in our AI data collection also means our assessment is without bias. At last technology that truly does level the playing field.

Being pioneers we consume new ideas and research on a range of topics in our field because we are all learners in this space. Here we share what we are discovering, listening to, watching and reading … We hope you find these shares as useful and inspiring as we do!

OUR FAVOURITE BOOKS!

Ethical Algorithm
Michael Kearns and Aaron Roth

Why we love it! Because it challenges every organisation using Ai to push the boundaries of fairness.
Everybody Lies
Seth Stephens-Davidowitz

Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.

Dataclysm
Christian Rudder

Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.

Civilized to Death
Christopher Ryan

Why we love it! Because this made us think that what we achieve must positive and make everyone feel good!

Prediction Machines
Ajay Agrawal, Joshua Gans, Avi Goldfarb

Why we love it! Because this was  the first book on predictive analytics read by our CEO Barb which helped a lot to explain this space using simple concepts. How Smart Machines Think
Sean Gerrish

Why we love it! Because this was recommended by Matt, one of our awesome advisors.

Invisible Women: Data bias in a world designed for Men
Caroline Criado Perez

Why we love it! Whilst the audio version does feel a bit didactic at times, the narrator is so frustrated at the disconnect between the facts and what people believe about the presence or not of bias. There is some solid data referenced which reflects the deep and wide research  that has gone into uncovering often invisible nature of gender bias in many sectors.

NOW FOR OUR FAVOURITE PODCASTS

PODCAST #1
Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning

Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai(37 kB)

Why we recommend it? Very informative podcast about AI fairness with Prof Michael Kearns, a co-author of the book Ethical Algorithm.Buddhi is a regular consumer of Lex Fridmans podcasts  – he attracts an extraordinary array of minds and perspectives  from Daniel Kaheman, Melanie Mitchell, Paul Krugman, Elon Musk and he asks such thoughtful original  questions of people interviewed many times over that every podcast feels illuminating for both sides. 

PODCAST #2
Scott Adams: Avoiding Loserthink

Dilbert creator and author Scott Adams shares cognitive tools and tricks we can use to think better, expand our perspective, and avoid slumping into “loserthink.”(103 kB)
https://149366099.v2.pressablecdn.com/wp-content/uploads/2019/11/s-adams-500px.jpg

Why we recommend it? There is a story of “bias” in how he got into creating Dilbert. He was told by two employers that “we can’t promote you because you are white, because we have been promoting too many of them, so now we have to fix it”. Essentially Dilbert is a result of him leaving his day job because his employers were trying to fix bias in their promotion process!

PODCAST #3
Getting to scale with artificial intelligence – The McKinsey Podcast

Why we recommend it? Companies adopting AI across the organization are investing as much in people and processes as in technology.

PODCAST #4
Sleepwalkers podcast by iHeartRadio

Why we recommend it? With secret labs and expert guests, Sleepwalkers explores the thrill of the AI revolution hands-on, to see how we can stay in control of our future.

PODCAST #5
‎HBR IdeaCast: A New Way to Combat Bias at Work on Apple Podcasts

Show HBR IdeaCast, Ep A New Way to Combat Bias at Work – 14 Jan 2020(76 kB

Why we recommend it? A brilliant captivating podcast on the types of biases that turn up at work and an exploration of bias interrupters. Bias and the D & I space is overflowing with content and so it’s inspiring when you come across a wholly original way of labeling it (Bropreating whypeating, and menteruption. What’s less effective -single-bias training … -referral hiring ! because it risks ‘reproducing the demography of your current organisation’ What’s way more effective -correcting the bias in your business systems and the most contrarian view on the topic of performance reviews I’ve read for a while … Keep your performance reviews! Removing them creates a ‘petri dish for bias’.

PODCAST #6
Can Artificial Intelligence Be Smarter Than a Human Being? by Crazy/Genius

Why we recommend it? Surely, AI technology has nothing that even closely resembles human imagination. Or does it? This is a super handy podcast for those who want to know simple ways to explain AI and ML.

PODCAST #7
AI in B2B – a16z Podcast

Why we recommend it? Consumer software may have adopted and incorporated AI ahead of enterprise software, where the data is more proprietary, and the market is a few thousand companies not hundreds of millions of smartphone users. But recently AI has found its way into B2B, and it is rapidly transforming how we work and the software we use, across all industries and organizational functions.

Brilliant articulation of why FOMO is real .. as far as coming to data too late . Co pilot and auto pilot analogy is clever.
1. B2B is different. Companies care a lot about their data
2. Share for greater good and reap the benefits should be the motto of A.I. companies
3. Product design thinking with AutoPilot and CoPilot metaphors. Where can our A.I. be auto and co?
4. Use AB testing to show the benefits to the skeptics

OUR FAVOURITE ARTICLES

ARTICLE #1:
Chief people officer: The worst best job in tech
https://www.protocol.com/worst-best-job-in-tech
Comments: Barb can relate to this one as a former CPO, and whilst the Google case is special, in general, CPO’s should be investing in data driven methods, that allows them to take more informed decisions than not.

ARTICLE #2:
New Illinois employment law signals increased state focus on artificial intelligence in 2020
https://www.technologylawdispatch.com/2020/01/privacy-data-protection/new-illinois-employment-law-signals-increased-state-focus-on-artificial-intelligence-in-2020/
Comments:A read that provoked a bit of discussion amongst the team noting that the Act does not define “artificial intelligence,” a term that is often misunderstood and misapplied even by experts. How will they separate what traditional statistical analysis has been doing to what modern ML algorithms do. Any attempt to classify ML as something different to just statistical analysis at scale will be fun to watch. One can then argue just using averages and medians are a form of AI … Regression .. Correlations … AI bias …

Ask BERT to fill in the missing pronoun in the sentence, “The doctor got into ____ car,” and the A.I. will answer, “his” not “her.” Feed GPT-2 the prompt, “My sister really liked the color of her dress. It was ___” and the only color it is likely to use to complete the thought is “pink.”

ARTICLE #3:
A.I. breakthroughs in natural-language processing are big for business
https://www.google.com/amp/s/fortune.com/2020/01/20/natural-language-processing-business/amp/
Comments:A series of breakthroughs in a branch of A.I. called natural language processing is sparking the rapid development of revolutionary new products.

ARTICLE #4:
Are We Overly Infatuated With Deep Learning?
https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/cognitiveworld/2019/12/26/are-we-overly-infatuated-with-deep-learning/amp/
Comments:Even Geoff Hinton, the “Einstein of deep learning” is starting to rethink core elements of deep learning and its limitations.

ARTICLE #5:
Artificial intelligence will help determine if you get your next job
https://www.vox.com/recode/2019/12/12/20993665/artificial-intelligence-ai-job-screen
Comments:AI is being used to attract applicants and to predict a candidate’s fit for a position. But is it up to the task?

ARTICLE #6:
Biased Algorithms Are Easier to Fix Than Biased People
https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html?smid=nytcore-ios-share
Comments:Racial discrimination by algorithms or by people is harmful — but that’s where the similarities end. The study: “Are Emily and Greg more employable than Lakisha and Jamal?” we referenced in our bias paper. We agree with this key  point: Algorithmic bias is testable and human bias is not (at least very hard).

ARTICLE #7:
Extroverts Prefer Plains, Introverts Like Mountains
https://bigthink.com/topography-and-personality
Causation or just correlation? There’s a very curious link between topography and personality.

ARTICLE #8:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia

ARTICLE #9:
Attractive People Get Unfair Advantages at Work. AI Can Help.
https://hbr.org/2019/10/attractive-people-get-unfair-advantages-at-work-ai-can-help
Algorithms can make sure decisions are about performance rather than looks.

ARTICLE #10:
Artificial Intelligence in HR: a No-brainer
https://www.academia.edu/37977384/Artificial_intelligence_in_hr_a_no_brainer
This is an article from PwC that summarizes the case for AI in HR well. A really good overview.

ARTICLE #11:
Science Behind the IBM’s Personality Service
https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-science
The background and the approach listed here is applicable to our approach too. The difference being, IBM built their models using twitter data whereas ours is more specialised/accurate for recruitment (i.e. based on more data and continuously learning). In addition, we are able to predict more than personality (e.g. job hopping attitude, traits etc).

ARTICLE #12:
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text 
https://www.aaai.org/Papers/JAIR/Vol30/JAIR-3012.pdf

ARTICLE #13:
Language-based personality: a new approach to personality in a digital world

ARTICLE #14:
Navigating Uncharted Waters: A roadmap to responsible innovation with AI in financial services
https://www.weforum.org/reports/navigating-uncharted-waters-a-roadmap-to-responsible-innovation-with-ai-in-financial-services
Navigating Uncharted Waters shows how financial services firms, policymakers and regulators and customers can overcome five risks and plot a course toward more rapid AI adoption.

ARTICLE #15:
Model Tuning and the Bias-Variance Tradeoff
http://www.r2d3.us/visual-intro-to-machine-learning-part-2/
Learn about bias and variance in our second animated data visualization.

ARTICLE #16:
Daniel Kahneman’s Strategy for How Your Firm Can Think Smarter
https://knowledge.wharton.upenn.edu/article/nobel-winner-daniel-kahnemans-strategy-firm-can-think-smarter/
The research is unequivocal, according to the father of behavioral economics: When it comes to decision-making, algorithms are superior to people.

ARTICLE #17:
Experience Doesn’t Predict a New Hire’s Success
https://hbr.org/2019/09/experience-doesnt-predict-a-new-hires-success
Is it time to rethink the way we assess job applicants?

ARTICLE #18:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia/
The best ie simplest summation of this tech I have read (edited) linkedin.com. Once the domain of Sci-Fi geeks and film script writers, Artificial Intelligence or A.I.

ARTICLE #19:
Nudge management: applying behavioural science to increase knowledge worker productivity
https://jorgdesign.springeropen.com/articles/10.1186/s41469-017-0014-1
Knowledge worker productivity is essential for competitive strength in the digital century. Small interventions based on insights from behavioural science makes it possible for knowledge workers to be more productive. In this point of view article, we outline and discuss a new management style which we label nudge management. Nudge is a concept in behavioral science, political theory and behavioral economics which proposes positive reinforcement and indirect suggestions as ways to influence the behavior and decision making of groups or individuals. Nudging contrasts with other ways to achieve compliance, such as education, legislation or enforcement.

We liked reading this because it mirrored what we read from candidates every day after their receive ‘MyInsights, their personalised insights profile. We believe that every person regardless  of their role craves  personal growth. The feeling they have when they receive that report- priceless for our team. “Thank you for your email. I did find it useful as it has made me really think about my workplace and personal life by self-reflecting. I feel since reading this, I have stepped up in a few different situations including at work where I had stepped up in a temporary leadership role. Personally, I have been practising speaking my mind and let go of toxic friendships and make decisions more easily.”And … After getting the insight of what you see of me & your reasoning it made me think about work place moments & how well I’ve responded to situations as well as make me think about alternative ways I could have reacted & received differing outcomes.

ARTICLE #20:
Distilling BERT models with spaCy
https://towardsdatascience.com/distilling-bert-models-with-spacy-277c7edc426c
Transfer learning is one of the most impactful recent breakthroughs in Natural Language Processing. Less than a year after its release.

ARTICLE #21:
Building Trust in Machine Learning Models (using LIME in Python)
https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/
This article helps us understand working of machine learning algorithms using LIME package. Using LIME, you can understand working of black box ML models.

ARTICLE #22:
Jordan Peterson on Workplace Performance, Politics & Faulty Myers-Briggs

Hilarious watching Jordan talking about selling personality assessments but mostly he is spot on in his observations.

We’ve relied on interviews for centuries. They are an important step in the selection process.  

If done effectively, interviews are a great means of assessing a candidate. We trust them to enable us to determine if our candidates have the attributes, traits, behaviours, skills, experience and personality to meet the role requirements.

Here’s the problem. It is physically impossible to interview every candidate. So, we rely on CV screening as the first step. A recruiter on average spends six seconds looking at the resume. In six seconds, a snap judgement is made on shortcuts (biases).

At the starting block, the process has failed. You cannot possibly pick qualities like grit and initiative from a CV, right? Then, of the people who applied for the job, around 13% of applicants may get an interview. During C-19 times – you can more than half that number.

With interview automation, interviews are at the
start of your recruiting process – not midway through it.

In this way, you realise the value of interviews without investing one-minute of your time in them. 

Imagine this. Everyone has already been interviewed before you have read one CV. A pre-qualified, pre-assessed, high-quality shortlist before you have read ONE CV. That’s the dream! Because now you are not wasting time reading resumes of people who either can’t do the job, won’t do the job, or they just don’t fit. And, instead of flicking through 100 resumes for a puny 6 seconds each, you can take the space to consider the best. The best? Those candidates who have already been pre-selected for that grit and initiative you so badly want in your team.

Okay, so here are the 6 reasons to start your recruiting process with interviews

You can try out Sapia’s FirstInterview experience here.

1.  You will reduce your time to hire

Time to hire measures recruiting efficiency. It is the number of days between the first contact with a candidate to the day the candidate accepts the offer. Screening is your first time-to-hire bottleneck.

Even if you’re using an ATS you may be able to easily rank resumes, but you still have to consider them. And there’s your block.

A new generation of interview automation is here so that you can have every candidate interviewed in a flash. Of course, it integrates and works seamlessly within your ATS. It saves recruiters from screening resumes and boosts the efficiency of your recruiting process.

Reducing time to hire is great for candidates who get the job faster (or can move onto the next job). It is terrific for recruiters who get the reward of quicker placements and attaining their metrics. It is a relief for hiring managers who get their team to a full complement and can get back to their actual job.

Interviewing automation makes your recruiting process much faster – usually around 90% faster. 

2. You will improve the overall quality of hires

Hiring managers want their best team. They want people who can do the job, who will do the job and who will perform. With interview automation, Ai assesses traits, communication skills, optimism and temperament prior to you getting involved.

As a Recruiter, you get a complete picture of a candidate beyond what is written on their CV. You learn a lot of information about the candidate. Ai will rank and grade all your candidates for you. It pre-qualifies those who are a fit to move forward.

Have you ever thought to yourself: “If only I could hire 10 more Julie’s!” (*insert name)? With Ai, you can. And, as far as quality goes, this is the distinction from all other forms of pre-employment.

AI learns what a successful hire looks like and pin-points more like them. AI bases this learning on your historical recruiting decisions and then applies that knowledge to new candidates to automatically screen, grade, and rank them.

Interviewing automation gets you to the best of your talent pool much quicker resulting in, on aggregate, much better quality in your hires.

3. You will reduce bias and improve diversity

Diversity and Inclusion have been on the HR agenda for a long time. And in more recent years, it’s made its way onto the Business agenda too. In 2020, global management consulting company McKinsey again confirmed that companies with both ethnic and cultural diversity and gender diversity in corporate leadership are outperforming non-diverse companies on profitability. They found: “The most diverse companies are now more likely than ever to outperform non-diverse companies on profitability”

Diversity improves employee productivity, retention and happiness. Settled then. We want businesses that are diverse and fair.

The problem is that humans are inherently biased.

Here’s the King of Recruiter biases: The Dunning-Kruger Effect. It’s where we lack the self-awareness to accurately assess our own skills meaning that we overestimate our ability. You think you are a brilliant totally unbiased Recruiter, right?  You may well be, but it’s not uncommon to think you’re smarter or better than the average person. Haven’t we all skipped over candidates who don’t have the requisite ‘Big 4’ employer on their resume, or the ‘right kind of degree’?

Even when we don’t mean to be, human bias is pervasive.  We keep these biases alive, through our relentless refusal to admit our shortfalls. And unfortunately, this isn’t great when it comes to hiring for diversity.

Ai is far less bias than humans. 

The reason for this is you can test, adjust and get rid of biases. The good news is Ai doesn’t resist stubbornly while claiming absolute fairness and denying any bias. This means that undesirable machine learning biases will tend to decrease over time. In Sapia’s case, its blind screening at its best. Nothing that typically influences human bias is introduced into the algorithms – no CV’s, no socials, no videos, no facial recognition – it’s just the candidate and their text answers. Much fairer for candidates of course and a richer experience where they can just be themselves.

Interviewing automation makes your recruiting process much fairer and your hiring decisions far more diverse.

4. You will reduce the cost of each hire

Those who have already automated their interviews have reallocated that spend toward higher-value investments.

Your ability to hire cost-effectively will be hampered if you don’t have the right tools.  Make sure that all your recruitment technology is pulling in the same direction – to make hiring as seamless, streamlined and stress-free as possible – rather than working against you. The money you invest in the right technology will soon pay off when it comes to time and efficiency savings.

The real game-changer is that interview automation can also help you solve a churn problem.

 Significant costs are borne by an organisation when an employee voluntarily leaves. 

These include replacement costs such as costs associated with advertising, screening and selecting a new candidate. A study conducted by the Australian HR Institute in (AHRI) 2018 across all major industry sectors in Australia (Begley & Dunne, 2018) found that on average companies face an annual turnover rate of 18%. Within the age group of 18 to 35 it worsens significantly, at 37%. That is, more than 1 in 3 people in the youngest age group leave an organisation within a year.

Imagine if you could predict those with a likelihood of churning before you had met them? Then think about the enormous savings that would be derived across your organization if you could do so.

If you haven’t yet automated your interviews, you are spending too much on hiring.

5. You will increase the productivity of every recruiter

What’s the toughest part of your job as a Recruiter? If you had more time, could you do that part better? 

Chances are that reading CV’s and running interviews are not the hardest part of your job but are the most time-consuming. What if you could have available time for those high-value tasks. Like managing your stakeholders. Getting to know the business better. Improving your business partnership skills. Learning the essence of what Hiring Managers actually want. Networking and improving talent pools, particularly for those hard-to-fill roles.

So, if interview automation can take care of all of your first interviews for you then ask yourself:

Of how much value am I when buried knee-deep in screening? Visualise less of that and more of the buzz you get when you find the perfect fit. There’s no better feeling than knowing you’ve helped someone further their career AND helped your Hiring Manager find someone who ‘just fits’ and will perform. Nothing can replace the collaboration and empathy that you as a live person can extend.

We cannot discuss productivity without also giving mention to structured interviews.

According to this Sapia research paper published by IEEE: Structured interviews (where the same questions are asked from every candidate, in a controlled conversation flow and evaluated using a well-defined rubric) have not only shown to reduce bias but also increase the ability to predict future job performance. With interview automation, the questions asked in a structured interview are derived using a job analysis as opposed to interviewer preference and are typically based on past behaviour and situational judgement.

Interviewing automation frees up recruiter’s time to perform higher-value tasks with far greater output.

6. You will boost candidate experience (beyond imagination)

With interview automation you can move from an elongated process that leaves candidates in the dark, not knowing where they stand, to a super-efficient experience that feels empowering.

According to the Society for Human Resource Management (SHRM), 82% of candidates report the ideal recruiter interaction is a mix of innovative technology and personal, human interaction.

If your mission is to provide the best experience possible to your candidates, interview automation should not be ignored.

Improving your candidate experience is so much easier by adopting technology that is inclusive, personalised and relatable. Sapia’s interview automation offers a mobile-first, chat interview that interviews everyone in-depth and at scale. Giving every candidate personalised feedback.

Here is what interview automation offers above a manual interview process for candidates:

• Utterly relatable – An accessible, mobile-first ‘familiar’ text experience that candidates enjoy with no confronting videos or questionnaires. Questions are related directly to role attributes.
• Totally convenient – Completing it anywhere, anytime and in your own time with an untimed interview – giving candidates the space to be themselves.
• Wholeheartedly fair – Everyone gets an interview levelling the playing field for all. It’s blind screening at its best – no gender, age or ethnicity revealed.
• Unbelievably motivating – Following their FirstInterview all candidates receive an email with personalised insights. It’s delightfully surprising to receive something of such great value.
• Surprisingly beneficial – Candidates receive coaching tips that help them with their career, transforming the experience.
• Highly open – Candidate experience improves by 148% when candidates are asked for interview feedback.  That’s why after their FirstInterview all candidates can rate their experience and give their input.

Interviewing automation enhances candidate experience, with no further time investment from you.

Download the 2020 Candidate Experience Playbook here

Taking the first step on automating your interviews: start with volume roles first. 

Gartner predicts by 2021, 50% of enterprises will spend greater budget on chatbot creation and bots than traditional mobile app development.

Businesses are adopting Sapia’s chat interviews across various job families – especially in front-line customer service roles. The quickest payback you will get on an investment in interview automation is to apply it to your high-volume roles first. Interview automation can truly enhance your high-volume recruitment process and help you make it more efficient (and pleasant) for everyone involved. This will help you get your time-back really quickly and release the budget for automation in other areas of recruiting.

The future of all first interactions between candidates and your business will be through automation. The only decision, for now, is where you will adopt interview automation first.

Read the Ultimate Guide to Interview Automation>

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You can try out Sapia’s FirstInterview right now, or leave us your details here to get a personalised demo.

Have you seen the 2020 Candidate Experience Playbook?

If there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.

Download it here 

We agree with Katrina Collier: Recruitment isn’t broken, per se. It needs a bit of work, sure, but in the midst of the Great Resignation, dedicated talent acquisition managers all over the world are doing some of their best work. They’re finding top talent and helping businesses succeed.

Despite this, we can say that candidate experience is certifiably broken. Ghosting rates are up somewhere around 450% since the start of the pandemic. 65% people say they rarely receive notice of their application status (Lever), and 60% of people say they have bailed on a job application due to its length or complexity. 

Why candidate experience is important

Many mid-to-large sized companies spend in excess of $200,000 per year on sourcing and advertising (assuming a hiring rate of fifty people per year). Few invest in candidate experience. We tend to overlook the fact that the candidate journey from application to offer (or rejection) is just as important for the health of a recruitment funnel, over the long term, as good ads or recruitment strategies.

Good candidate experience, put simply, is your best chance at securing the talent you want. In the wake of the Great Reshuffle, employees have the power to choose when and where they work, and they know it. If you can’t reach them and woo them in a reasonable time frame, you’re at a supreme competitive disadvantage. They’re here today, gone tomorrow. That means that multi-round interview funnels and tedious psychometric games aren’t going to cut it anymore. Today’s candidate wants speed, perks, and flexibility. Your experience should be designed with this in mind.

There are a lot of ways candidate experience might be improved – this article offers some tips, including advice on a term we like to call the Gucci principle.

One easy place to start is with your job ads.

How to write a good job ad

Good job ads are concise and well-formatted. They put employee value proposition up front. They discuss the vision and purpose of a role, and not just day-to-day responsibilities. They avoid the term ‘competitive salary’ – in fact, they disclose salary ranges. They’re not necessarily short, either. Anyone who tells you that a job ad must be short to be good does not understand the anatomy of an advertisement.

Here are our top tips.

1. Make sure the spelling and grammar in your job ad is perfect, throughout

This seems like a minor point, but good spelling, grammar, and sentence structure is essential for your employer brand. It’s a matter of perception. Poor writing casts doubt on the legitimacy of your brand, and on your capabilities in general – after all, if you can’t write a clean job ad, how can the candidate be sure you can do other, more important things, correctly?

Have someone in your marketing team cast their eye over your ad before it goes out. Proof-reading should always be a part of your customer outreach. If you don’t have a marketer on which to rely, consider investing in editing software like Grammarly.

2. Keep the unique language of your brand

Funky company names are in vogue. Just look at ours. Because we’re called Sapia, we refer to our team (and even our customers) as Sapians. Therefore, we do the same with our job ads. It creates branding consistency, and works as an unconscious primer, suggesting to candidates that they’re joining a well-knit, stable, and purpose-oriented team. 

The same goes for language. If you’ve adopted or created certain words to make your brand stand out, they should also be used to make your job ad stand out. Look at this example from Gong: They tell the candidate that they’ll be creating edu-taining content. That’s a lot more interesting than “you’ll be writing content that is both educational and entertaining.” Had they chosen the latter sentence, you’d doubt their credibility, because that sentence is not remotely entertaining.

Or take this example from one of our own job ads. You might say that using a curse word (oh dear me!) in a job ad is inappropriate, but we don’t. We’re Sapians, and that makes us passionate humans. We understand that writing the way you speak is the quickest way to build rapport. Tell us that you don’t get that impression from this paragraph.

3. Clear categorisation and formatting of sections

A job ad doesn’t need to be short, but it should be formatted for scanning. Candidates should be able to easily read it, extract the main points, and make the call to apply, all within minutes. We like the following job ad section structure:

• Perks and benefits
• Responsibilities
• Qualifications

Each section can be as long as you need it to be (within reason), but it should also be set out in dot points. Easier to read, easier to digest. Many are the job ads that set out position duties and benefits in great big walls of text. Go with dot points, like Gong has, and you’ll stand out.

4. Make it as easy as possible to apply

Depending on the platform you use, it can be difficult to control how candidates enter your funnel. Regardless, you can make it easier by clearly sign-posting the action you expect them to take. If it’s a LinkedIn EasyApply button, great – but don’t confuse candidates by asking them, at the bottom of the ad, to email their CVs to you. This happens a lot.

Make sure you have a single call-to-action, and make it clear. Add it to the top and bottom of your ad. 

You know what they say about first impressions? That’s why it’s so critical to get your job ads right. Check out this post on LinkedIn for more tips on writing the perfect job ad.