Ouch, Crush, Bang, Ack, Urg – Talent Sourcing can be painful – even with today’s AI tools.
“This platform gives me too few candidates without all of the skills. That list is full of people who just started at a new job and probably won’t move. This site only shows senior people for this mid-level job. Bottom line, why do the available tools bring me so many irrelevant profiles?”
As talent acquisition professionals, we know we lack time. We are super busy searching, evaluating, scheduling, and mediating with candidates. On top of this, we have to manage the hiring managers: their requirements, changing minds, interview pipelines and more. There isn’t enough time, especially for us to do what we do best: using our unique perception skills as super-connectors to relate to and interview candidates.
It is often surprising to step back and think that this general pain, lack of time, is often exacerbated by the technologies that are supposed to be making our lives easier. The below focuses on specific pains we encounter when using talent sourcing tools when searching for ideal profiles.
The lack of time is our major pain, but it stems from the following contributing pains.
Our 5 Biggest Pains when Talent Sourcing Include:
#1. False Positives – Irrelevant Profiles without ALL our must have skills
Often when we build our searches in sourcing platforms we get hundreds, sometimes thousands of candidates in our search results – Most of which MISS the skills we require. Scale: Very Annoying. Frequency: Far too often.
Why Does this Happen?
Many AI sourcing tools (excluding LinkedIn) DO NOT have the ability to create “AND” scenarios meaning it reads your key words as OR only. For example in the case you are looking for a sales person with Phone Skills “AND” Negotiation Skills “AND” Closing Skills, you will receive talent with AT LEAST only 1 of these skills as opposed to ALL 3. Meaning most of the talent presented to you in the search results will MISS the other 2 required skills. An example of a proper “AND” filtered search would be on your favorite hotel booking site. You want a 5-star hotel “AND” Breakfast “AND” a Pool. And you expect that you will be only shown hotels with all of these criteria. How angry would you be if you turned up at a 1-star motel with no pool that provided boxed cereal next to the kettle in your room for breakfast? That’s what you get in most recruiting tools.
2. Talent isn’t Prioritized
There is talent which exists within the search that matches our job requirements, but it is located incredibly far down in the results list. For example, in the case in which you are looking for a software engineer with AWS, Java, and HTML, 5 candidates may possess all 3 skills, but in a search list of 1000 people they could be positioned as number 3, 72, 177, 533, 842 etc. It is totally Random. So you need to kiss a lot of frogs to find your prince. Or, in this case, to find your Purple Squirrels. Scale: Very Annoying. Frequency: Far too often.
Why Does this Happen?
Unbelievably, most AI Souring Tools DO NOT have an intelligent way to prioritize the talent. It can be done, but most current tools have not been developed enough to do so. It’s misleading in some cases, as tools actually ask you to differentiate between “Required” and “Preferred” skills – leading us to believe that this would eventually result in prioritizing profiles accordingly. But NO!
There is a 2nd reason. Many AI sourcing companies are not able to prioritize their talent lists because they do not actually have the full list of candidates at the start. Instead, they lure you into a false sense of security by giving recruiters a partial pool of candidates upon launching the search. Yes this instant, un-prioritized list allows sourcers to work right away, but they are ultimately time wasters. Beware! How can they prioritize the best talent if they don’t have the full list at the time they give it to you? Again, more ugly frogs to kiss.
3. Profiles with Outdated Data
We often see that profiles in sourcing tools are outdated. The profiles miss skills, have wrong current companies, present wrong titles, etc. All this misleads us. How can you, as a recruiter, make decisions on a poor data set? We end up chasing and engaging people who are irrelevant dead ends. How many times has this happened? How much time is wasted here? Scale: Very Annoying. Frequency. Far too often.
Why does this happen?
Most AI sourcing Tools have agreements with large databases that provide resume details. Through these agreements, AI sourcing tools either receive a data dump once every 12 months or are allowed to “ping” their data for new data every 6-12 months or so. To put this into perspective, if a database dump is performed into the sourcing tool’s database on Jan 1st, and a profile is changed on LinkedIn on the 2nd of Jan, then for 355 Days you will be receiving outdated, incomplete profiles. To discover whether your sourcing platform holds fresh or outdated profiles do the following:
Take 10 profiles in your sourcing platform and find the same profiles in LinkedIn. Check their current Job and ensure the job title and start and end dates match up. If they don’t, you can’t trust it.
4. There is not enough talent on the list
It is an all-too-familiar situation: you have all of your requirements into a system and hit the button, and you are presented with so few prospects that all the time inputting your criteria feels like a waste. Suddenly you have to spend even more time reworking your search, trying to figure out the magic combination that will yield an appropriate number of candidates. It’s like picking a lock. All by guessing. Scale: Very Annoying. Frequency: Far too often.
Why does this happen?
Most searches rely on Boolean searches of Keywords. Keywords have to be input by the searcher, and they have to have been written by the prospective candidates on their profiles. Boolean is basically binary. A match or NO match. Many candidates, especially passive candidates, do not have extensive online profiles. They do not list all of their relevant skills, or their updated skills. And these tools do not extrapolate skills from titles. That leaves the recruiter in the predicament of either having a long list of irrelevant candidates or a list of candidates which is too short to return an ROI on the search. Boolean is therefore limiting.
5. Candidates are not ready to move
You have CRACKED it! After hours, days, sometimes even weeks of searching, you have identified a short-list of candidates who would be PERFECT for the position. The problem is that most or all of them are perfectly comfortable at their current jobs. Scale: Very Annoying. Frequency. Far too often.
Why does this happen?
Most tools don’t have a way to identify whether passive candidates are also passive job seekers. They use a single source to retrieve profiles, don’t follow online fingerprints of candidates, but most importantly don’t have a predictive algorithm that accurately calculates a candidates propensity to move. Using multiple sources, collating their activity and historical movements, combined with a solid algorithm, shows you who could be ready to move. Get there before other recruiters! And getting those passive candidates can be key to finding the very best talent more quickly.
So how to eliminate these pains? Well that’s why Talenya was born. That’s what our product solves. We’d love to show you how. Speak to us.