PhillyTech CEO, Michael C. Bertoni, discusses the importance of team strength, another critical component of the Hiring Differentiation Index (HDI).
Candidate Data - An Essential Part of the Hiring Differentiation Index
PhillyTech's wealth of candidate data helps clients to hire more efficiently.
Michael C. Bertoni, Founder and CEO of PhillyTech, sat down with Consumer51’s Justin Butler, Director of Strategic Development, to discuss PhillyTech’s candidate data, another critical component of the Hiring Differentiation Index (HDI).
PhillyTech is an industry leader in assisting Software as a Service (SaaS), and Hi-Tech companies hire talent, generate leads, and drive sales. This is the fourth blog in a series introducing the Hiring Differentiation Index and why it’s critical for SaaS and Hi-Tech companies.
Justin: So we're sitting down with Michael C. Bertoni, the Founder and CEO of PhillyTech. We're digging into an exciting part of the Hiring Differentiation Index: Candidate Data.
Michael: This is a powerful component of the HDI. We build data sets for every position within a SaaS company. Ruby on Rails, for example, we have built a dataset of over 2000 engineers. This unbiased data is developed from our independent research and is tailored to the needs of PhillyTech’s SaaS clients.
Imagine you had to hire five Expression Engine Engineers for Consumer51. We already have a dataset of 500 pre-screened and pre-targeted people on our list. One of the biggest problems in the industry is poor data when targeting candidates. Using our wealth of data is much more efficient, especially when you have multiple positions to fill. We make hiring more efficient for our clients when we build these datasets.
Justin: Today, we're discussing something that has been alluded to in previous conversations. And that is candidate data. This is essential and yet a little more obscure to some people. Can you give us a quick overview?
Mike: So a big differentiator for PhillyTech is candidate data; which we refer to as datasets. I have been working in SaaS for over 20 years, and PhillyTech has been building and honing datasets in the SaaS space for the last eight years.
Let’s say we have to hire engineers or developers for a client. We pull an existing data set and cross reference that against a more current data set based on the client’s specific needs.
The same goes for sales positions. Directors, account executives, SDRs, and development reps, are all available through our data sets. This means that we have prime candidates on hand for our clients. An efficient hiring process ensures that no time is wasted and our clients can hit the ground running.
Justin: And especially when a company wants to do a large-scale hiring operation–what’s the advantage of working with somebody that already has datasets in place?
Mike: Speed and efficiency is the unbeatable process that PhillyTech has created. When we work with a client, we have a kickoff meeting to understand the specific needs. We then build the job requirement for approval.
Most companies typically want to hire as quickly as possible. While we are building the job requirements, we simultaneously build out the datasets so we can instantly generate leads and conduct a targeted campaign for relevant candidates. It's not just a process; it’s a methodology built on a foundation of accurate data.
Justin: Let's dig into the people who make up the datasets. Who are you targeting?
Mike: So we have an existing database of roughly 50 million. We further segment that database into datasets for each particular role. When clients tell us what they want, we have a system to find the best candidate.
Imagine a client is looking to hire a senior Java engineer, AWS, or DevOps engineer. They have a long list of requirements, that we analyze in order to hone in on five technical things that would best serve the client. We target people that have usually been working for at least two-plus years and start introducing them to this new job opportunity.
Justin: So you don’t necessarily target people who are disgruntled with their current situation, but rather those who could be enticed to a better position.
Mike: Our team looks for specific triggers. We know that if a high-caliber candidate has been in a position for two or three years, they might be looking for something new. There are other signals as well. Have they updated their LinkedIn profile recently? Are they posting and expanding their network? We identify potential candidates in our database, and then we pursue them. We get them interested in applying for our clients’ jobs.
Justin: So instead of doing a passive job posting, you're hunting and hyper-targeting for unactivated candidates.
Mike: Correct! Our approach is a mix of datasets, scanning job boards, and hyper-targeting, whose foundation is a network of 50 million candidates. Having 21,000 LinkedIn first connections gives us access to roughly about 60% of the entire LinkedIn dataset. We've already collected and segmented that data for our clients. Using LinkedIn, Sales Navigator and Boolean are naturally the next steps in narrowing down great candidates.
Justin: It seems like an incredible opportunity to get access. So how does the client determine if it's the best fit for them?
Mike: The easiest way is to reach out to us and set up a meeting on our website. If someone is looking to hire talent, you have to hyper-target. You must have datasets if you're looking to do it at scale. You need a company with datasets and with relationships. All clients have to do is get in touch with us so that we can get started finding them the best employees.
Justin: So, contact Mike Bertoni to access those datasets.
Mike: Yeah, all you have to do is connect with me on LinkedIn or go to our website, PhillyTech.co. You can set up a meeting right there and do a free assessment. You could also go to our Hiring Differentiation Index and set up an appointment there. Just get in touch with us and we'll get you connected.