You know the common definition of insanity? The one where the same thing gets done over and over again, but the end result doesn’t change? It might not be a big deal when talking about your daily commute, but taking the same old approach to hire key personnel could be an expensive mistake.
Industry studies estimate bad hires cost up to 2.5 times the dollar amount of that person’s salary – and the damage doesn’t end there. Mismatched employees disrupt workplace chemistry, productivity, and profitability.
In response to poor hiring decisions, a growing number of companies now employ predictive screening and hiring models. Engaging predictive analytics and artificial intelligence (AI) – or algorithms that ‘think’ like humans – to help with the legwork historically performed by recruiters.
AI and predictive analytics look at historical data and then apply the learnings to new data to predict future outcomes. So, predictive hiring models can predict who will make it through the interview process, outperform their peers and still be around a few years down the road.
Progressive HR professionals have realised the potential of predictive and data-driven hiring, and hiring managers seem to agree.
“Today, HR has a seat at the table, and in order to maintain that business partnership, you need to have an analytics framework.”
Andy Kaslow, CHRO, Cerberus
A 2016 survey revealed a strong desire to drive talent acquisition through data and analytics. Two hundred executives at large U.S. firms want technology to play a bigger part in the hiring process. And the clamour for analytics isn’t confined to a younger crowd. Two-thirds of decision-makers who desire data-driven solutions fell between the ages of 45-64.
So, why isn’t everyone doing it?
Although there is a general consensus that data-driven and predictive hiring will make hiring decisions more accurate, many HR professionals still view it as cumbersome and costly to implement.
And it can be true.
Understanding the data needed to make an impact, and figuring out the best techniques and algorithms to use is difficult.
And it can be expensive to hire data scientists, and other key technical personnel needed to implement a full scale HR analytics system.
But, there’s no need to go it alone or to do it all at once.
Rather than setting up in-house HR analytics teams, most companies opt to engage a vendor who specialises in custom predictive screening and hiring models. Finding a vendor that works with you to solve your hiring challenges will significantly cut cost and time to implement.
So, if you are considering implementing predictive hiring, we have put together a few tips to ensure you get off to a great start.
1. Define what you want to improve
The crucial first step of any successful project is to define what that success looks like. And implementing predictive hiring isn’t any different.
Have a think about the biggest issue your organisation is facing at the moment that better hiring decisions will solve.
For example, you might have the issue that a lot of new hires are leaving your organisation after a few months. Or you might have a company culture in need of strengthening, and need to hire people who fit with your ideal culture.
When you have honed in on the issue you want to solve, you also need to start thinking about the data that will be required to solve your challenge.
To give you an indication of the type of data you might need, consider these examples;
- If you have an issue with turnover, the data needed would be employees’ start and end date.
- Or, if you were looking to improve your customer service level at a call centre, you would need some sort of customer service KPI data – for example NPS.
(These indications are based on the data required if you were working with us at PredictiveHire)
2. Find a shortlist of vendors
After defining the issue you want to address with predictive hiring, it is time to find a shortlist of vendors that can help you achieve your goal.
Make sure you look for vendors who are able to build predictive hiring models focused on your specific issues, whilst making sure the candidate experience isn’t compromised.
3. Perform your due diligence
When you have your shortlist of vendors narrowed down, make sure you perform your due diligence. Some vendors will be a better fit for the challenge you wish to solve with your predictive hiring model.
Make sure your shortlisted vendors address these key questions;
- What are they basing their predictions on? Predictive hiring experts should be able to tell you the scientific basis of their predictions.
- How do they address the potential of existing biases being incorporated into the algorithms?
- How do they fit in with your current hiring process? Can they fit in without causing too much disruption/change?
- What is the user experience of their product like? How about the candidate experience? For a predictive hiring solution to be useful it should be easier to use than not to use.
- How do they support you during implementation, on-boarding and roll-out of the tool?
- What is required from you in order to maximise the project outcome?
Ai for Hiring – Buyers Guide: The 8 Questions You Must Ask
All of these questions are important to address to ensure the project’s success.
Implementing new software and processes will always require some level of change management, for example; following the ADKAR or Kotter change management approaches. Make sure you are comfortable with the level of support the vendor will offer you during the roll-out.
Following these three steps will ensure you are off to a good start with your predictive hiring project – and can start reaping the rewards quickly.
Predictive HR analytics is here to stay.
Resisting this change may put your company at a distinct disadvantage in the marketplace.
A recent MGI study found that AI can significantly improve the bottom line for businesses willing to incorporate them into their core functions. And the time really is now. Early adopters will enjoy a significant data-advantage in only a few years.
“[Leading businesses] use multiple AI technologies across multiple functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.”
McKinsey Global Institute, June 2017
In the words of Gartner Research’s senior vice president Peter Sondergaard, “Information is the oil of the 21st century, and analytics is the combustion engine.”
You can try out Sapia’s Chat Interview right now, or leave us your details to book a demo
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Mirrored diversity: why retail teams should look like their customers
Walk into any store this festive season and you’ll see it instantly. The lights, the displays, the products are all crafted to draw people in. Retailers spend millions on campaigns to bring customers through the door.
But the real moment of truth isn’t the emotional TV ad, or the shimmering window display. It’s the human standing behind the counter. That person is the brand.
The missing link in retail hiring
Most retailers know this, yet their hiring processes tell a different story. Candidates are often screened by rigid CV reviews or psychometric tests that force them into boxes. Neurodiverse candidates, career changers, and people from different cultural or educational backgrounds are often the ones who fall through the cracks.
And yet, these are the very people who may best understand your customers. If your store colleagues don’t reflect the diversity of the communities you serve, you create distance where there should be connection. You lose loyalty. You lose growth.
We call this gap the diversity mirror.
What mirrored diversity looks like
When retailers achieve mirrored diversity, their teams look like their customers:
- A grocery store team that reflects the cultural mix of its neighbourhood.
- A fashion store with colleagues who understand both style and accessibility.
- A beauty retailer whose teams reflect every skin tone, gender, and background that walks through the door.
Customers buy where they feel seen – making this a commercial imperative.
How to recruit seasonal employees with mirrored diversity
The challenge for HR leaders is that most hiring systems are biased by design. CVs privilege pedigree over potential. Multiple-choice tests reduce people to stereotypes. And rushed festive hiring campaigns only compound the problem.
That’s where Sapia.ai changes the equation: Every candidate is interviewed automatically, fairly, and in their own words.
- Bias is measured and monitored using Sapia.ai’s FAIR™ framework.
- Outcomes are validated at scale: 7+ million candidates, 52 countries, average candidate satisfaction 9.2/10.
- Diversity can be measured: with the Diversity Dashboard, you can track DEI capture rates, candidate engagement, and diversity hiring outcomes across every stage of the funnel.
With the right HR hiring tools, mirrored diversity becomes a data point you can track, prove, and deliver on. It’s no longer just a slogan.
Retail recruiting strategies in action: the David Jones example
David Jones, Australia’s premium department store, put this into practice:
- 40,000 festive applicants screened automatically
- 80% of final hires recommended by Sapia.ai
- Recruiters freed up 4,000 hours in screening time
- Candidate experience rated 9.1/10
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
Recruiting ideas for retail leaders this festive season
As you prepare for festive hiring in the UK and Europe, ask yourself:
- How much will you spend on marketing this Christmas?
- And how much will you invest in ensuring the colleagues who deliver that brand promise reflect the people you want in your stores?
Because when your colleagues mirror your customers, you achieve growth, and by design, you’ll achieve inclusion.
See how Sapia.ai can help you achieve mirrored diversity this festive season. Book a demo with our team here.
FAQs on retail recruitment and mirrored diversity
What is mirrored diversity in retail?
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Why is diversity important in seasonal retail hiring?
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
How can retailers improve their hiring strategies?
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
Blog
The Diversity Dashboard: Proving your DEI strategy is working
Why measuring diversity matters
Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.
Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.
Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.
What is the Diversity Dashboard?
The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.
Key features include:
- Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
- Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
- Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
- Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
- Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.
From insight to action
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
- See if your EASL candidates are applying in high numbers but not progressing to live interview.
- Spot if candidates with a disability report high satisfaction but have lower offer rates.
- Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.
Built on science, backed by trust
Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.
Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.
Book a demo to see the Diversity Dashboard in action.
Blog
Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.