COVID-19 has forced a lot of us to become remote workers by default. Now more companies are now declaring it is likely to become their new norm, with little understanding of what successful remote teams look like.
Zoom exhaustion is a thing. The reality of working from home for many of us has become long days trying to get small tasks done between back-to-back video calls. The founder and CEO of Automattic, Matt Mullenweg, a company with over 1000 remote workers spread across 75 countries, chose remote as the working norm for two key reasons. First, to access a broader pool of talent, and second to unleash productivity. He describes five levels of remote work maturity. Most companies now forced into WFH are at Level 1. We have just moved our way of doing things to a different location and are following the same daily routines that we always have.
Mullenwag describes Level 5 as the ‘nirvana’ for remote work where your distributed team works better than any in-person team ever could. He says his company is not even there yet.
So, what are we all doing wrong?
We have missed one of the drivers of remote work productivity gains which is asynchronous work- which needs asynchronous communication. This simply means that work doesn’t happen at the same time for everyone. Productivity and flexibility for employees come when we don’t all have to get in a room or via Zoom. That means communicating in writing, not by video is imperative.
Forcing people to do video meetings also risks continuing to drown out team members who don’t thrive in a live group setting. The introverts. The deep quiet thinkers. The ones who prefer to reflect on an issue and not be forced into making a contribution because everyone else is on Zoom. Again, written communication solves for this.
It’s quite simple, if you want a fully functioning remote team written communication is the way to go. It has to be the way you define a business problem, debate the key issues, and fast track from idea to execution.
Case in point: Jeff Bezos
Jeff Bezos cottoned on to this years ago. Amazon requires every meeting to be guided by a six-page memo laying out all the key issues. Everyone, regardless of their title, has to read every word. Bezos turned written narrative into a competitive advantage, recognising that writing clearly requires clear thinking. Effective written communication is a foundational building block of a successful remote workforce. GitLab, another fully remote organization with over 1000 employees across the world highlights this fact in their Remote Work Playbook (page 19).
This ‘new productivity hack’, how you write, whether via text, Slack, Wiki or on Google docs also impacts your hiring processes. At what point do any of us test for written communication skills when hiring? If you want to hire people who can work autonomously, be productive and who can collaborate you need to test their text communication. This calls for a radically different approach to talent acquisition.
A solution!
Mullenweg worked that out early in Automattic’s remote working journey and all their job interviews are via text. The other obvious benefit of this approach is it means there is far less room for bias. In contrast, put someone in front of a camera for a video interview and the bias risk is amplified. Hiring going forward has to test for written communication. This is not something you can ignore anymore.
How to hire for a remote environment?
If you speak to C-suite about why it’s taken so long to permit remote work, the word trust will come up a lot. Bottom line, managers don’t trust that people will actually work when at home, creating instead an unproductive culture of ‘presenteeism’. To manage the risk of hiring ‘slackers’, the other thing you have to test for is motivation.
Other personality traits that relate to good remote workers include discipline. The advantage here is that we stop Big ‘P’ personality-based hiring. We have all made those hiring mistakes – inclined to the person who tells a good story. In a remote work environment, self motivated employees, big talkers and non-doers get discovered quickly!
What may not be known to many people, is that testing for written fluency, clarity of thought, motivation, discipline, can all be done via text analysis in the hiring process. Testing should not be just limited to the skill of writing. It should also test the motivation behind expressing something in writing. That requires more effort and thinking than speaking it out. If someone is not motivated to express themselves in writing when a job is on the line, you can assume what it might be like once they are on the job.
The power of NLP!
The power of Natural Language Processing (NLP) based machine learning models that can tell you all of this immediately is here today. From just 300 words, we can infer writing skills, personality traits and job hopping motives. This means there is no excuse for not hiring for the key skills required for remote work right now.
Noam Chomsky, a pioneer of language studies said it best –
“Language is a mirror of mind in a deep and significant sense. It is a product of human intelligence. By studying the properties of natural languages, their structure, organisation, and use, we may hope to learn something about human nature; something significant, …” (Noam Chomsky, Reflections on Language, 1975)
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How to reduce candidate dropout by measuring and boosting candidate experience
Candidate experience: Everybody’s talking about it, few companies are actively investing in it.
According to a Sapia-sponsored Aptitude Research report from earlier this year, 68% of companies admit they have no plans to address the interview portion of their candidate experience throughout 2022 and 2023. Despite this, 50% of these companies know they’re losing talent due to their application and interview processes. What’s more, according to Forbes, companies that prioritize candidate experience can see their average quality-of-hire improve by 70%.
Why the unwillingness to address such an important facet of recruitment? In most cases, the teams responsible for enacting change to candidate experience are steeped in the everyday throes of talent acquisition, and don’t have time right now to examine their processes. Statistically speaking, this is probably where you’re at. Totally understandable; the 2023 labor market is tough. If your house is on fire, you’re probably not focussed on how well you treat the visitors at your doorstep.
Recently, on our Pink Squirrels! podcast, we sat down with Lars van Wieren, CEO at Starred, a candidate experience measurement tool. Lars offered some practical tips on getting started with candidate experience: Benchmarking it, measuring it at different stages of the process, and setting your business up to review and act on the findings.
Step 1: Benchmark your candidate experience
As the saying goes, what gets measured, gets managed. Lars recommends starting with a basic benchmark for your candidate experience. This need not be difficult, and you don’t necessarily need a fancy tool to start gathering these data.
Simply ask your candidates: How likely are you to recommend our company to a friend or colleague? This is, in essence, a Net Performer Score (NPS) question, and the scale (1 to 10) should reflect that.
Ideally, you should be gathering feedback on your candidate experience at each stage of the application process, but to begin with, ask the question at the very end. And to get the best, least-biased data, you need to ask all applicants whether or not they’ve been shortlisted or hired – if you only ask those who have been shortlisted, or the few people who have been successful, you’re likely to get magnanimous results that don’t reflect your true candidate experience.
The NPS tracking question is easily configurable and embeddable into automated emails, meaning it can be set up through your ATS with little additional work.
When you begin to analyze the data, keep things simple: Dump the data into a spreadsheet, and look at your average numbers. If your score is below 0, you’ve got work to do – if it’s 0 to +30, you’re doing well. 30+ and over, well done!
(If you’re reading this, it’s probably not likely that you’ll get a 30+ score on the first go-round. That’s okay – the goal is to find out how much work you’ve got to do.)
The benefit of benchmarking NPS is that it gives your business a single, easy-to-understand proxy for the health of your candidate experience. Once you’ve got the number, you can start to make small changes to your application experience and see how that affects the overall number.
For example, you might consider making the following changes to improve your candidate experience:
- Maybe we don’t need to ask for cover letters. Most candidates don’t like writing them (statistically speaking), and they’re a time-suck for our team.
- Maybe we can shorten the content on our careers page. It’s long, and difficult to navigate.
- Maybe we should consider using Easy Apply functionality.
- Maybe we are running too many interviews in our process. Let’s do fewer, and see how that works.
At the same time, you might consider looking at your candidate abandonment rate – we’ve got a post on measuring and improving it here. Candidate experience scores and abandonment rates are almost always linked. Improve one, you improve the other.
Step 2: Gather more data on each step of your job application process
Our joint report with Aptitude Research uncovered some interesting data on the importance of two-way feedback between candidates and employers.
Gathering and acting on mutual feedback:
- Boosts quality of hire from 36 to 58%, on average
- Boosts candidate experience from 34 to 44%, on average
- Improves first-year retention from 35 to 50%, on average
Feedback is critical. And, to make it as accurate and indicative as possible, your feedback should ideally be gathered at each stage of the application process: Application, screening, interviewing, assessment, offer, and rejection.
By doing this, you’ll know exactly where your candidate experience is lacking – and you can make fast, effective changes.
Multi-step candidate experience feedback may not be easy to do with your current setup, but it is relatively simple to configure if your ATS/chosen software solution has the capability.
Step 3: Get your team around the candidate experience data
Generally speaking, the task of improving candidate experience is that of your entire talent acquisition or recruitment team. But it’s a good idea to appoint an internal candidate experience champion – someone who is responsible for collating the benchmark data and regularly reporting on it.
What’s the reporting cadence? Depends on the amount of applications you have, and the length of your application process. A monthly score update check-in works best for most. Monthly measurement will likely give you an insightful trendline.
While the task of improving candidate experience is never done, it needn’t require an overhaul to your entire recruitment business. Start small, make iterative improvements over time, and focus on making at least one more candidate smile.
(P.S. Candidate experience is a natural benefit of using our Ai Smart Interviewer – in fact, more than 90% of the 2 million candidates we’ve interviewed have loved our process, and 84% of them are highly likely to recommend you to a friend or family member. Reach out to us here to find out more.)
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No amount of surveys will change your culture
Just as no amount of diversity training will give you people who are less biased.
As a company, you are unlikely to move the needle much on engagement or performance if you are hiring the wrong people.
The only way to change culture fast is through your people decisions – who you hire and promote.
Our Principal Data Scientist knows this from analysing data at his old employer Culture Amp for three years – it’s often the same companies in the top and the bottom on engagement year after year.
This is why highly engaged companies remain engaged as they hire like-minded people. It’s a virtuous cycle.
Build trust through inclusiveness and transparency
Transparency and inclusiveness builds trust. We know that from our own relationships and it applies equally in the workplace. Healthy cultures thrive on people feeling heard and leaders being transparent on what’s going on in the business.
Making unbiased hiring decisions
The greatest algorithm on earth is the one inside of our skull, but it is heavily biased. Most decisions related to people are heavily flavoured by emotion, aka bias. Biases are difficult for humans to remove even when we are conscious of them. We need technology to help us – to de-risk the bias and change mindsets.
Are these connected themes or unrelated?
Here’s our formula for the Right Culture: (Inclusiveness + Transparency) – Bias = Trust
What do you think? We invite you to join the conversation on LinkedIn.
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Why Machines make better decisions than humans (oh and why I hate Simon Sinek )
This came up in my feed last week prompting me to share my own 2 cents on why machines are better at hiring decisions than humans.
Did you know that the Wikipedia list of cognitive biases contains 185 entries? This somewhat exhausting article lays out in excruciating detail biases I didn’t know could exist and arrives at the conclusion that they are mostly unalterable and fixed regardless of how much unconscious bias training you attend in your lifetime.
I get asked A LOT about how I can work for a company that sells technology that relies on ‘machines’ to make people decisions.
I will keep it simple … 2 reasons
Because as per above, our biases are so embedded and invisible mostly we just can’t check ourselves in the moment to manage those biases. (I would rather hire women, ideally, mums, who like the same podcast series as me and straight through to offer stage if they like Larry David humour )
And Machines can be ‘trained’ …humans can’t, as easily or efficiently
But the myriad and ever-present news articles about ‘algorithmic bias’ has lumped all machine learning into one massive alphabet soup of ‘don’t trust the machine!
Really? Are we also biased against machines now? I saw Terminator 2 as well and worry about machines taking over the world ….but that’s a massive leap from the practice of bringing data, objective data into the most critical decision you will make as a people leader, who to hire. The divorce rate is for me the proof point that humans suck at making critical people decisions.
I’ve been in the People space for a while. I was lucky enough to work with 2 organisations BCG and the REA Group that value their people above all else. They also value making money and having your engineers and consultants sucked up in recruiting days and campaigns is a massive investment of your scarce and valuable capacity. I have found most companies don’t even know how much it costs to hire one person because no one is tracking the time investment.
We are all time poor and so we often default on hiring based on ‘pedigree’ . Someone has GE on their CV, they must be great as GE only hires great people. That’s a pretty loose /random data point for making a hiring decision
So here is a non data scientist view of why you should trust machine learning to find the right people and when you shouldn’t
First credit to this post which helped me put this into non tech speak .
https://medium.com/mit-media-lab/the-algorithms-arent-biased-we-are-a691f5f6f6f2
Why use Machine Learning at all for decision-making ? Because it underwrites making repeatable, objectively valid (ie data based) decisions at scale.
Value to the organisation:
• Use less resources to hire
• Every applicant gets a fair go at the role
• Every applicant is interviewed
• Hire the person who will succeed vs someone your gut tells you will succeed
How do you ensure there is no or limited bias in the machine learning ?
Take a look at:
– what’s the data being used to build the model
– what are you doing to that data to build the model
If you build models off the profile of your own talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self reinforcing hiring
If you are using data which looks at age, gender, ethnicity and all those visible markers of bias , then sure enough, you will amplify that bias in your machine learning
Relying on internal performance data to make people decisions, that’s like layering bias upon bias. The same as building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.
Reality is that machine learning is by its very definition aiming to bias decisions, and removing bias is driven by what bits of training data you use to feed the machine. This means you can make sure the data you train with has no bias.
Machine learning outcomes are testable and corrective measures remain consistent, unlike in humans. The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.
Tick to objective data which has no bio data (that means a big NO to CV and social media scraping )
Tick to using multiple machine learning models to continuously triangulate the model vs rely on one version of truth
So instead of lumping all AI and ML into one big bucket of ‘bias’ , look beneath the surface to really understand what’s going into the machine as that’s where amplification risks looms large
Oh and the reason why I hate Simon Sinek …
I don’t actually at all, but if a candidate said that to me in an interview I’d probably hire them for it because I would make some superficial extrapolation about their personality based on it:-
• first it would tell me they watch ted talks and so that eeks of cleverness and learning appetite
• second it would tell me they are confident to be contrarian and that would make me believe that they are better leaders
• third I would infer they are not sucked into the vortex of thinking that culture is the panacea to every people problem.
See how easy it is to make an unbiased hiring decision?
Soon (maybe already) you will be putting yours and your loved ones lives in the hands of algorithms when you ride in that self driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data. Even in hiring.