Having been a CHRO of a listed company in my last role, I can empathise with the confusion and exhaustion that comes from navigating the myriad HR tech products flooding the market whilst trying to manage many ongoing HR change initiatives.
Last year, as CEO of an HR tech start-up I did what most do in that role — I spent a whole lot of time talking to customers, CHROs, heads of talent, recruiters and business owners, listening to their challenges to build a product that works for them. There are a few themes I picked up on through these conversations.
‘What’s the right tech stack for my team and our company?’ and ‘how do I integrate all these technologies?’ are questions every CHRO of any sizeable company is grappling with. And the answer is more complicated than committing to a new HRIS.
Whilst I am not a tech expert, I spend many hours a week thinking about one critical part of the HR function that is ripe for technology innovation — recruitment. In that vein, I am sharing some things I have learnt which I hope will be useful to your investments in your tech stack in 2019.
There are HR tech products that give you insights on engagement hot spots, employee sentiment, and screen applicants for roles by scraping and analysing people’s personal profiles or communications. If you believe (as I do) that transparency enhances trust, especially when it comes to anything coming out of HR, these tech products could undermine organisational trust and maybe even your employer brand. Look beneath the hood of a tech product to validate how it works. AI and the concern of algorithmic bias is one every CHRO needs to be ready to talk about. Understand the source data and how it will be used in the solution. For candidate selection, any front end testing needs to not only be valid but feel valid to the user. That’s why we use relatable and valid questions to assess candidates in building our predictive models. No CVs, no video and no games.
Any extra discretionary effort by employees is going to be heavily influenced by how much trust your people have in you. Better to invest in tech solutions that allow for more transparency around how decisions are being made, that use reliable, objective and valid data.
Think of the people analytics generated by HR today — turnover reports, engagements stats, culture diagnostics, exit survey analysis, 9 box talent management. All of it is backward-looking reporting on the past performance of talent. Much of it also subject to the vagaries of human analysis, therefore biased insights. How many of your organisations use data to validate the placement of people on the ‘potential axis’ of a 9 box? Or use NLP to extrapolate the key themes from engagement surveys and exit survey verbatim?
A bigger challenge for all of this backwards analytics is connecting the dots — how does a culture survey actually move you towards and predict a different culture? My colleague who spent his early years building up the data science team for a leading engagement survey platform and led the benchmarking analysis for their clients observed that year after year the same companies were in the top and the bottom quartile of engagement.
Changing culture is hard unless you change the people — the people you hire and the people you promote.
The best investment you can make to change the culture and help the organisation move towards forward-looking predictive analytics is to start to capture data from the outset — from your applicant pool, through to the people you hire.
Having a data DNA profile of your applicant and hired pool means you can better target your employer branding, you can identify with high accuracy the profile of the stronger performers, the people who are high flight risk in the early months, the talent that moves fastest to productivity. Knowing these profiles means you can seamlessly feedback into your recruitment a better hiring profile.
This is the power of predictive analytics over psychometric testing which has no feedback loop back to the business on whether the person with the high OPQ test was any good in the role.
‘Garbage in garbage out. This is usually a reference to a data quality issue.
Data can take many forms- it’s not always hard numbers (more on that later), it can be data that is structured and regulated by you vs data that is unstructured and not regulated by you, such as CV’s. The former is always better — closer to the objective source of truth, usually owned by you, and less prone to gaming.
CVs are a poor man’s data substitute and rarely indicative of anything. A CV is a highly gameable type of data and relying on CV data to select talent exacerbates the risk of bias, as was experienced by Amazon when they built their hiring models around a 10-year database of CVs (mostly male).
I won’t spend time on the risks of bias in CV screening as enough has been written about that, other than to share this from a blog post which quotes academic research that ‘both men and women think men are more competent and hirable than women, even when they have identical qualifications ‘, and that ‘resumes with white-sounding names received 50% more calls for interviews than identical resumes with ethnic-sounding names’. https://www.lever.co/blog/where-unconscious-bias-creeps-into-the-recruitment-process.
Removing bias in the screening process is no longer about social justice, now it’s about commercial outcomes — McKinsey has documented each year since 2014 that companies with top quartile diversity experience outsized profitability growth https://www.mckinsey.com/business-functions/organization/our-insights/delivering-through-diversity
There are a plethora of surveys that make the point that HR functions are starting to invest in the power of people analytics.
Making data more visual has been a big driver behind the success of engagement analytics companies such as a Culture Amp, Glint and Peakon, transforming ugly engagement decks and the traditional circumplexes into insights-driven real-time dashboards. Visualisation offered by tools like Tableau is table stakes these days for HR.
Data doesn’t always look like data in a traditional sense. Take textual search data, human behavioural tracking data for example. Google has been making money off that data strategy for years and there are now books written about how google search terms are the most accurate mirror to our true beliefs and values (Read Everybody Lies for a fascinating insight into the power of text).
Tracking human behaviour has been mainstream in marketing teams for years, but has been slower to be leveraged in HR. In consumer marketing, no one cares why a person is more likely to buy an item, they are only interested in optimising for the outcome. There has been some interesting research applying consumer behaviour analysis to HR with fascinating insights, for example, that your choice of browser in completing an online assessment is a strong predictor of your performance in the role.
In consulting there is an often-used accusation of consultants ‘boiling the ocean’, which usually refers to those 100-page decks with chart after chart, visualising every data point possible as if the sheer weight of the deck is somehow testament to its accuracy.
Most junior consultants aspire to write the ‘killer slide’, the elusive one slide that crystallises the strategy in one data visual that will transform the company’s trajectory.
As HR teams start to produce more output on people analytics, there is a risk of ‘boiling the ocean’ on people analytics — quarterly engagement surveys, monthly churn data, diversity reporting. Figuring out the ‘so what’ of the data and using those insights to move the needle on business metrics that matter is harder, but also necessary. For HR integrating non-HR owned data is also important to get a fuller picture, especially for sales led businesses. For example, if sales drop off at the 2-year mark, what can HR do about that? What HR processes change as a result of seeing high correlations between sales trajectory in the first 6 weeks and tenure greater than 6 months.
HR’s role is very much one of building bridges across the organisation — taking a helicopter view of talent, ensuring that the needs of the business will be met in 3 years, 5 years by the people in the business, in enabling communication and collaboration channels across teams and geographies.
Building a single source of truth about their employee base often justifies HR’s biggest tech investment in helping achieve those objectives — the so called ‘one size fits all’ HR system. Yet it’s a big step to assume that even with the HRIS in place that HR has all the data it needs to do its job. Every function is making similar investments — sales & marketing into CRMs, operations teams into rostering systems, LTI and OHS data that might sit in the BU or a separate OHS team.
Last century, HRs accountability might have ended when they filled a role. Today, HR is accountable for ‘talent optimisation’ and that means ensuring people’s success through their career with the organisation, and often even beyond. Knowing how that talent is performing on the job– roster adherence, injury patterns, call centre metrics, sales performance — are integral to optimising that talent pool.
Capitalise on these various streams of data!
I encourage HR leaders to be expansive about what is performance data, especially objective performance data, and being relentless in sourcing that data from their non-HR colleagues internally.
Data generated within HR can help drive broader organisation decisions. B2C companies with large volumes of sales and marketing applicants can leverage the power of those volumes for the benefit of the rest of the business.
Big brand companies can receive half a million-plus applications in one year, often engaging meaningfully with just a fraction. Technology allows you to test and engage meaningfully with every one of those applicants. Instead of thinking of that pool only as a candidate pool relevant to recruitment, for a B2C business, that pool is most likely also your consumer base and a rich source of data for your business.
Customer acquisition cost (CAC) for product and services like travel, retail, software, financial products range from $7 to $400, with companies committing substantial advertising budgets to reach that kind of audience, yet over in recruitment, they are engaging with them for free, at a point where the candidate/consumer is at their most willing and motivated to engage with you.
Imagine what consumer data you could capture from that applicant pool for the benefit of the business?
Transparency and authenticity, forward-looking predictive data, business impact first, think creatively and broadly, and HR as a data generator. These are 7 themes that can transform your organisation in by leveraging the data hidden within HR through the efficient use of technology.
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On 26th August, our CEO Barb Hyman facilitated a webinar on “Hiring with Heart” in collaboration with The Recruitment Events Network.
To our surprise, Jeff Uden who is the Head of Talent and L&D for Iceland Foods also joined the webinar.
During the session, Jeff offered some wonderful comments. We took a transcript of Jeff’s input and have jotted it here. It offers insights on dealing with enormous volumes of candidates, offering positive candidate experience and communicating culture from a candidate’s first experience with a brand.
Thanks for your insights, Jeff. Incredibly valuable.
At Iceland Foods, we have started working with Sapia. That was as a result of a couple of things. One was the element of the mass recruitment that we were doing. Just to put it in perspective, in the first four months of this year, we received over five hundred thousand applications.
We wanted to find a way that delivered a level of fairness, a level of consistency around how we sift those applications that then enabled store managers to reduce that amount of time that they are spending on doing the recruitment.
The other thing that we wanted to do was significantly enhance our candidate experience. One of the challenges that I had around the experiences that we had within the business is that it felt like it was really standard. It felt like it was cold; it felt like it came from a computer. We wanted to change how we did that and more importantly give something back to the candidates.
Often nowadays people apply for jobs, and there’s the standard ‘bulk’ response that says if you haven’t heard anything from us in two weeks take it that you haven’t been successful.
As big companies or companies of any size we have a duty to help those individuals to understand why they haven’t been successful and to help them to be successful in the next role for which they apply.
The fact that they won’t be hired into your business is probably the right decision because they wouldn’t have been the right fit given the testing that they have gone through. However, that doesn’t mean they are a bad individual. What we need to do is to help them to understand where their strengths are and where their development needs are, and certainly, that was a massive appeal of working with Sapia.
Going through and reading some of the feedback that we’ve had from the candidates, it’s having a huge effect on the candidate experience.
We had a swift implementation planned. But probably one of the lengthiest parts of it was about actually getting the questions right and getting the language right. We really did spend a decent period doing that.
I just had a quick look at one of the pieces of feedback here, and this is completely unedited:
That’s what’s coming over from the way in which we put the language across within the questions.
We are genuinely really chuffed about how they are engaging far more with us as a brand and how they are feeling like they are getting something back. They genuinely don’t feel like this is a computer process in any way whatsoever; they genuinely feel like they are talking to people.
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If there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.
Over a six-month period, Sapia gave personalised, same-day feedback to 250,000 candidates after each completed a text-based interview using its AI platform, says CEO Barbara Hyman.
The candidates ranged in age from 16 to 80 and included people from non-English speaking and Indigenous backgrounds. The feedback highlighted their strengths, as well as tips on areas for development.
The outcome, Hyman tells Shortlist, is that 99% of candidate reported satisfaction with their interview experience; 70% said they were more likely to recommend the company as an employer of choice; and 95% reported they loved receiving their feedback and “found it empowering, constructive and ‘scarily accurate'”.
Recruiters using Sapia gain insights into each candidate’s personality and the quality of their response to behavioural interview questions.
Sapia realised candidates would benefit from receiving some form of feedback and insight into their traits as well, and so it began rolling this out 15 months ago. The feature has since won a UK-based candidate experience award.
The feedback specifically does not include information about whether the candidate is a good fit for the role, “because that’s not our job – that’s the client’s job”, Hyman says.
For AI to be trusted, she says, the candidate needs to trust it, and so the candidate needs to get something out of it – including “the ability to understand themselves”.
Candidate experience isn’t simply an automatic email that says, “thanks, we’ve had lots of applicants, but we may not get back to you”, Hyman says.
Rather, a good candidate experience is “when everybody gets something out of it”.
“There really isn’t any excuse now for ghosting. And the feedback that companies give when they do it through humans is not that constructive. Getting a phone call saying you’re not a great culture fit – what’s that telling you? That’s a big cop-out.”
When Sapia first deployed the candidate feedback feature, its clients were initially too scared to use it, says Hyman.
“They thought that if you give candidates feedback, you’ll risk a whole lot of candidates calling up and asking, ‘why didn’t I get the job?’ or candidates would disagree with it and it would undermine their trust in the process. This might diminish their employer brand,” she explains.
But these fears proved unfounded when recruiters started reading the responses candidates were invited to give about their feedback, which included whether they agreed with the feedback and whether they would recommend the organisation as an employer or retailer (most of Sapia’s clients are consumer brands).
“The fact we were able to show to clients what candidates thought about it really disrupted that fear and killed the notion feedback is a ‘risk’.
“In fact, what candidates feel is feedback is a gift, and that gift is really playing out in terms of employer brand,” says Hyman.
Reference: Shortlist 2020 | https://www.shortlist.net.au
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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 .
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.