The Secret to Ongoing Engagement With Candidates, That Lasts Years. An Interview With Woo’s Founders



Woo’s founders, Liran Kotzer, CEO and Ami Dudu, CTO, discuss how they created Woo, the challenges they faced a few years back and the ones that are just around the corner.

How did you two meet?

Liran: We go way back and share a similar path. We served in technological military units, and worked on various projects together.

I was always more focused on management, whereas Ami was more interested in the technology itself – the classic combination.

After serving in the military for many years, we joined the founding team at Illuminator, which was later acquired by EMC, and in 2007, after the tech industry recovered from the recession, we founded a recruiting firm called SeeV that specialized in the tech arena and was later acquired by Matrix.


Why did you start Woo? 

Liran: We decided to create a technology that’s based on generating long term relationships with candidates. Because we understood that most candidates who aren’t actively looking for a job are open to new opportunities.

Our experience founding SeeV made us aware that recruiting firms tend to lose touch with candidates after about 3 months of joining.

They essentially lose a major percentage of their candidates without being able to re-engage with them. So they end up stuck in a perpetual cycle of chasing new candidates and losing them.

On the other end, candidates have to start every new job hunt from scratch and don’t really have anyone to stand by their side and support them throughout their career, like an agent. It’s an unpleasant experience, having to explain yourself repeatedly, which deters them from starting the job hunting process to begin with.

This makes it challenging for recruiting firms to reach those 80% of candidates in their database who are open to hearing about relevant opportunities.

We understood that a new solution is needed. And we knew the most efficient approach was to create a virtual sourcer, we call Helena.


What was the main challenge you had to deal with when first building your technology?

Liran: We started with a team of 20 recruiters and worked manually while our tech team studied them and built our machine learning driven recruiter, Helena. It took 3 years to build but now Helena has taken over the recruiting. And the recruiters now work at configuring Helena.

Ami: The Woo platform and machine learning algorithms are heavily based on data that is gathered over time. Earlier on, when we created our MVP, we had to first validate the solution before gathering information and developing the complete product.

The initial infrastructure was mainly human-based, which allowed us to follow the process very closely, collect data, and prove our concept – all before building the technology that took nearly three years to create.

This approach saved a lot of precious time and allowed us to immediately get in touch with customers and build a relevant solution.

As the technology and machine learning capabilities have improved, the need for human intervention has taken a step back.


What are some of the current technology-related challenges you’re dealing with?

Ami: To help people, you must understand what they really want and need, which is a challenge, because people don’t always know what they want.

That’s why the platform doesn’t just ask people what they’re looking for, but rather studies their preferences through machine learning algorithms that are based on history, behavior, similar profiles, and a range of elements that influence decision-making.

This isn’t a one-algorithm-fits-all kind of situation. The different algorithms we’ve created look into various aspects and support one another.

Each one is focused on a specific part of the process, such as job titles, profile structure, related content, and even inconclusive or vague details that still matter.

When brought together, these data branches paint a clear picture of what the candidates’ next role should really be.


How does Woo help improve the recruiting process?

Liran: First, we continue to help source candidates with precision, so that they invest less time screening candidates and creating a bad experience with those who are found to be unfit for the job.

Second, we help them become better recruiters through data. Candidates feel comfortable to share their feedback with us and we use this information to help recruiters improve.

We let them know where they can be more flexible, how much they should offer, and more.

Ami: We walk them through this entire process and not just provide them with the technology and leave them to it. Some departments here at Woo are fully focused on communicating with recruiters. It’s necessary, at least for now.


Diversity is a big topic in recruiting. How do you address this issue when building the platform?

Ami: We noticed that employers are more interested in diversity, and a platform like ours simply isn’t biased the way humans tend to be. Helena doesn’t make decisions based on age, gender, etc.

One thing we keep hearing from people is that when technology is in charge, it already feels less discriminating and biased. It solved a lot of problems, both in the actual process and the general feeling.

Liran: We also researched diversity in recruiting and learned that things are far from great. There’s a major gap in favor of men in the field.

Candidates on Woo share their salary expectations, and we’ve noticed that earlier in their career, men and women share similar expectations. As their experience grows, so does the gap, reaching 20% around the age of 35.

Men value themselves more. We also see that when it comes to startups vs. corporates, more women prefer working at a startup. Contrary to what we might assume, they’re not looking for the easy path. They’re willing to work harder but still ask for less. It’s very disturbing.


What are your resolutions for 2019?

Liran: 2019 is the year in which we feel confident in the product we’ve created and the value it brings. We are focused on making our technology and unique experience available to as many candidates as possible. Our goal is to achieve this by branching out to new channels.



About Woo

Woo specializes in connecting experienced tech professionals who are discreetly exploring new opportunities to companies with the right job for them . Its machine learning technology matches criteria from both candidates and companies, resulting in an efficient process and the highest conversion from introduction to interview in the market.  Since 2015, Woo has worked with more than 500 customers including Lyft, WeWork, Samsung, Audible and Amazon  successfully bringing them quality hires.
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