Specialist Guide to Attracting the Top 20% of Tech Candidates

With AI technology at the core and humans at the helm, scaleXT is here to deliver precision hiring intelligence.

For the purposes of this specialist guide we will be referring to AI Recruiting as being AI at the core with humans at the helm to help companies attract the top 20% of tech candidates for their roles.

According to scientific research, when companies attract the top 20% of candidates based on AI powered precision hiring intelligence, they have:

1. 17% higher productivity – As proven in laws such has the 80:20 Pareto Law, Prices Law and even as seen in the Square Root Law – which are seen in nature, wealth distribution, sports, even the size of cities and also in business – a disproportionate percentage of outputs come from the top 20%.
2. 10% higher customer metrics – Working back from the customer the top 20% just like clock work have 10% higher customer metrics.
3. 21% higher profitability – When your customer metrics are higher this almost inevitably cascades into higher medium to long term profitability.
4. 59% less turnover – Top performers are also more likely to stay longer with a company and role that is in sync with developing and investing in their talents.
5. 41% less absenteeism – Also with top performers less absenteeism which correlates highly with less turnover are seen.

There are some key things you need to consider when trying to attract the top 20% of candidates to your current recruitment process;

• Your data sources are important – The overwhelming volume of candidate data being produced is growing exponentially every day faster than ever. At the moment only 5% of all data is ever used. The first step is ensuring that are accessing the right data sources are critical to help you separate the signal from the noise.
• Increasing the sample size of potential candidates for your roles – Once the right data sources are locked in you then need to make sure that you build your datasets for each role to represent the full universe of potential on-market (active) and off-market (passive) candidates. If you only have 30 to 50% of the total talent pool mapped then how do you even know if you are tapping into the top performers?
• The right messaging channels – Insights into the full universe of potential candidates is not enough. The right messaging channels are critical as well, so it is probably best not to rely on a single LinkedIn ‘Recruiter’ InMail alone for your approach to potential candidates. In 2020 – and beyond – optimal engagement occurs through the right messaging across a mix of Platform/Email/Mobile and of equal importance for conversion rates the sequencing and timing of these.
• Addressing your time to hire metric – The traditional do it yourself time to hire for tech roles is 63 days. When you are trying to attract the top 20% who will no doubt have numerous other suitors pursuing them addressing your time-to-hire metrics will be another thing that you can address that will really move the needle.
• Think about how you are going to transition your culture as a busines to this goal – A culture that doesn’t just exist but wins for your organisation is one you must intentionally create. Strong organisations understand their unique culture, use multiple methods to continuously monitor the state of their culture and align the culture they want with business performance priorities – like attracting top talent.

There are three main options you will probably want to consider before you get started;

1. Do it yourself – Do you do it yourself the traditional old fashioned way which still requires a high degree of manual labour intensive work. The standard opportunity cost per hire of this approach is usually [ ] (+ opportunity cost i.e. daily revenue per employee X average days positions unfilled = total revenue cost per unfilled job) with 5 to 7 data sources and an average time-to-hire of 63 days.
2. Recruitment Agency – Do you outsource this to a recruitment agency where the average cost per hire for a $100,000 role is $18,000 (+ opportunity cost i.e. daily revenue per employee X average days positions unfilled = total revenue cost per unfilled job) with 5 to 7 data sources and an average time-to-hire of 31 days.
3. AI Recruiting – Or you may want to consider tapping into AI Recruiting which has: 67 data sources and datasets of 95.7% of tech talent mapped to help many of New Zealand and Australia’s leading tech companies access the top 20% of potential candidates and an average time-to-hire of 18 days.

Now you have a clearer understanding of the edge you are able to now get from AI recruiting and have reflected back on your defined need you are now in a position to develop a plan to stay in traditional recruitment mode by doing it yourself or with your recruitment agency professional or now tapping into AI to help you attract the top 20%.

We’d like your NEXT STEP to be with scaleXT so you can explore how they can help you get what you want, quickly and effectively.

How does scaleXT work?
With scaleXT you can now tap into the best of both worlds. AI powered precision matching intelligence at the core with humans at the helm.

Why should I care?
Now you can tap into AI to do a lot of the heavy lifting sourcing work so you can focus on more higher ROI activities like candidate experience, screening and on-boarding. Recruitment is on the cusp of becoming hi-tech industry in a similar way to what we have seen in other areas like sales and marketing automation with the new Salesforce.com AI product Einstein.

How large are your datasets?
We have datasets of 95.7% of tech talent mapped across New Zealand and Australia.

How fast is scaleXT?
Typically you will get a high-quality Candidate Data MAP™ within 24 Hours and Interested CV’s to your INBOX™ or ATS within days, rather than weeks or months.

What powers your AI?
We use a proprietary AI powered algorithm called Enigma – which has processed millions of real world job scenarios – that it is able to replicate the way that the human brain works to improve decision making at a scale previously not possible. This uses Natural Language Processing (NLP) versus traditional Key Word search. First words from data for each unique candidate are mapped relative to each other. Then data for each unique candidate is sorted in space with data from other unique candidates. The platform then uses feedback to distinguish matching candidates and non matches. From here our dedicated team of humans at the helm at scaleXT take it from there to increase the precision matching even further still!