How to Maximize Your Budget With Machine Learning [Tips]

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At Talentify, we are big believers in the power of data. To know is to be able to act in a deliberate manner, and that is far more likely to yield results than guesses, no matter how educated.

However, as human beings we don’t always know how to read that data, nor do we necessarily have the experience necessary to understand how best to react. That’s one of the main reasons why it’s important to rely on tools when you have to. And the best, most advanced tools available today incorporate machine learning.

Talentify is one of the most advanced machine learning, AI-driven recruitment solutions available. Sign up for an intro call so we can show you how it can benefit your business.


What Is Machine Learning?

If you are reading this, you are either a search engine crawler bot, in which case this does apply to you, or you are a human being. Presumably, you’ve been a human being for a while now, perhaps your whole life, and you’ve realized that humans learn by remembering past experiences and adapting their approach next time to be more effective. We’ve all heard that quote misattributed to Albert Einstein, “The definition of insanity is doing the same thing over and over again and expecting a different result.” Those of us who are sane learn by doing different things until something works.

Machines, on the other hand, take instructions made by humans and run them. While this is great for some procedures, if there is an error in the instructions or the situation changes too drastically (as things tend to in life), then it all goes off the rails.

But what if a human could teach a computer to learn from its past experiences? Turns out, we can do exactly that, and we call it “machine learning.”

It’s complicated to get too much into the weeds on this, and you’re not here for a computer science lesson, but suffice it to say that the computer can take the results of various tests, rank them based on a number of different factors such as location, source, time, position, and so many more, then determine what changes should be made in order to get better results.


How This Helps Your Hiring

So, you have a learning computer? What does that do for you?

Well, for one, it helps to improve your brand. “Branding” here means more than just your color palette or slogan. It can extend outward to what it’s like to work there, what the hiring process is like, and whether the expected compensation is worth it.

For example, one time when I was looking for a job, I got a call back from a medical company that I had applied to...two weeks earlier. Having friends who worked there, I knew that this company was pretty bloated and worked at a snail’s pace in most areas, including hiring. I took a phone interview with them, but by the time I heard back after that, I already had another offer that I was intending to accept. The commute for this medical company was much, much better and I would have appreciated that, but it wasn’t enough to wait on an offer that might not be as good (likely wasn't based on reputation) and could take months to hear back about.

machine learningI also happen to know, again from friends who work there, that they have a very hard time finding people and retention is terrible. According to work done by SHRM, a poor brand results in low levels of employee and candidate engagement, which can result in high turnover and candidate ghosting.

Machine learning also helps to automate mindless or tedious recruiting work. Sifting through resumes takes an exorbitant amount of time, especially when many of them are not qualified. A quality AI-driven solution can do most of the resume reading and pre-screening for your TA professionals, leaving them more time to engage prospective candidates, interviewing, and developing effective strategies.

Spend less time on tactical tasks and more time on strategic ones by leaving the tactics to Talentify. Reserve your demo today!

Most exciting, machine learning can improve the way that you spend your budget. As a human (again, presumably), you are limited by how much and how often you work. You can only respond so quickly to changes in bid structure and it takes you a lot more time to process information than, say, a computer.

That’s why it’s great to have an AI platform that will pay attention to how much you’re spending on job advertising 24/7, looking to see what is working and what isn’t, and making changes that will ensure that you’re spending your money in the most productive ways based on the available data.


The Bottom Line

Now that we’ve spent a lot of time on the carrot, it’s time for the stick.

If you don’t adopt a programmatic, machine learning system soon, you’re going to be left behind. The technology is solid enough that it’s moved past the early adopter phase and is becoming a mainstream way for companies to source top talent from around the world. Sleeping on this technology is only going to hurt you in the long term.

That being said, machine learning recruitment platforms are more affordable than ever, especially when you consider that you can take your existing talent acquisition budget and simply move it over to the new platform, then be confident that you’ll be spending it more wisely, getting more qualified applicant for less. You can even check at our Cost Per Qualified Candidate calculator to see approximately how much you could be saving.

There are so many benefits to the adoption of machine learning into your recruiting process. Why wait when you can have the system running and saving you money almost immediately?


Christopher Hopkins

Christopher Hopkins is a marketing specialist at, a mobile-first Results-as-a-Service recruitment platform that helps recruiters reach and attract modern-day job seekers. He lends nearly 20 years of expertise in using online tools to finding valuable data, bringing people to the information that they need. He spends his time sharing what he's learned in order to help people to improve their hiring practices and enhance their recruitment strategies. When he's not doing that, he's fencing, cooking, or looking too deeply into dumb movies and smart comics.