Ep 7: Building More Dynamic and Comprehensive Candidate Profiles with Dina Bay

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[00:00:17] Hi everyone. It's Bob Hover. In this next episode I am joined by Dina Bay from Pitch Me. Dina discusses the challenges in talent acquisition, emphasizing the need for better candidate job matching and

[00:00:37] boy do we need that. She talks about the shortcomings of resumes and traditional hiring which is my pitch me created in the AI powered real time profile using her digital footprint. Which sounds very intriguing doesn't it?

[00:00:51] Dina stresses the importance of measuring performance and process metrics and adopting right technology to enhance efficiency and effectiveness. You also discussed the necessity of responsible AI usage and evaluating its boundaries in personal and professional contexts.

[00:01:06] Now of course we go a bit deeper on what else the average person needs to learn, do and be aware of when it comes to elevating one's AIQ. I hope you enjoy my conversation with Dina and thanks again for tuning in.

[00:01:20] Hello everyone. Welcome to another episode of Elevate Your AIQ. I'm your host Bob Hover with me today is Dina Bay from Pitch Me. Hi both. Thank you for having me today. It's a pleasure. Thank you for being here.

[00:01:34] So when we start off just having you give me a little background, you know, what brought you to Pitch Me and how you guys started? Yes, absolutely. So my background is financial.

[00:01:46] So I spent 12 years working in oil and gas doing M&As, but I blamed on my extreme level of curiosity that I ended up doing at each heart attack. We have been struggling throughout all my pretty much career journey.

[00:02:04] I struggle to get a perfect job by submitting CB because I don't have a standard and linear career background. But also we struggle to employ relevant people when I was already working at the oil and gas.

[00:02:19] So I was just intrigued by why such mismatch exists even though there is a fantastic talent below there and a lot of recruiters doing their job. And I decided to do a mocketry search with my future co-founder. And we interviewed hundreds of recruiters, hundreds of candidates.

[00:02:39] And we ended up with probably the root cause and the main problem that we are addressing right now. And it's imperfection of resume and the concept of standard and a for static paper as a professional profile presenting in individual.

[00:02:56] So this is how it all started and a few years later we are building an AI powered real-time professional profile of individual using digital footprint. That's fantastic. There's so much to unpack.

[00:03:13] Certainly I have had my own experiences with you know, Square, Square Paying in the round hole or a lack of internal mobility.

[00:03:21] It just seems so illogical that you wouldn't do everything you could to find the right spot for the people who knew we already smart and loyal and good teammates and things like that.

[00:03:32] And everyone just kept the kept fishing in another pond to try to fill a specific role, which we know is going to more, you know, the minute you get on board it the roles kind of change.

[00:03:44] So it's definitely appreciate that. That's quite a pivot for you from M&A work in oil and gas. My pivot was not quite as dramatic.

[00:03:54] I've had an enterprise transformation, but yeah the talent space is fascinating. I just think there's so much upside and we can fix some of these sort of matchmaking kinds of issues with more data or trusted data.

[00:04:08] And if we do it responsibly of course. So when we think about some of the challenges and talent acquisition, I mean, what's their response when we talk about job description to resume matching.

[00:04:22] I mean, did they acknowledge that and then just don't think that they have the means to change how it works or they're doing it but they're doing it slowly or

[00:04:32] Well, that's a very timely question. So the moment we took our first MVP profile on the market, we saw that, oh this is so innovative. Maybe the market adoption is going to be very bad and very low.

[00:04:47] And we would need to do a lot of marketing and user training. But in reality, what we've heard has been doing prior to us coming to the market is pretty much the same but manually.

[00:05:01] So when they wanted to build a profile of an individual or a candidate, they were looking for social media. They were looking for external sources associated with this individual. They looked at the blogs and personal websites. So beyond resume really.

[00:05:19] And when we took this product on the market, we immediately received a feedback how much time we save and how many hidden resources we actually find in the market.

[00:05:30] Actually, find about the candidate and how much non-compensional data sources we bring into the into the game that would have been overlooked otherwise. So I think from the early days, we kept checking whether it's like if what we're doing is relevant and timely and addressing their problem.

[00:05:53] So the overall concept of real time and constantly updated profile built by big data was very well received.

[00:06:01] Again, it becomes quite noisy right now. And I would say that the main challenge for recruiter right now is first to cut the noise and to really adopt a technology which is going to help an address particular challenge of particularly individual or particular company.

[00:06:20] But this is totally different story and I think all parties and both should really help users to navigate through that endless amount of new tech. It will bring every single day.

[00:06:33] Yeah, it seems like the metrics themselves are not necessarily geared to kinds of sourcing techniques, the type of data aggregation that we blended south to improvements in real metrics.

[00:06:48] I know quality of the virus is a tough subject because not everyone is measuring it and if they are, they're measuring it from different ways.

[00:06:56] A lot of times that's not telling that position from what I've heard in the people I've talked to, telling that position doesn't necessarily feel like they should be tied at all to that metric.

[00:07:06] We did our job and we through the candidate over the fence now they're in a play and if they don't work out, then you know it must be your problem.

[00:07:15] But it starts from the beginning right and it starts from how you identify people in the first place and I think everyone has a role to play that potentially catch a red flag or yellow flag.

[00:07:26] Any potential issues of front. I guess I get concerned when I still see people focusing on time to fill time to hire. This isn't just about efficiency because then you have a revolving door and I described it on having the air conditioning on but getting the windows open.

[00:07:42] So that's a spot on. Trying to have this optimal environment and then it's just you'll never get out of your own way that way. That's a that's a spot on and we're in the same page because I leave numbers you obviously will believe numbers.

[00:07:58] But you will be surprised that even metrics that you just mentioned are advanced so usually I mean we have around 4500 customers that we currently work with and only about 20% actually measure any performance or process related metrics internally.

[00:08:19] Majority of the numbers that we measure against revenue revenue per recruiter task or revenue per quarter or revenue per roll field. But when we talk or when we ask them questions but do you actually measure anything internally about performance time to hire?

[00:08:39] I don't know how you're going to be measuring the error why all this new technology that you are just adopting. On the 20% of the companies actually give us an answer. Majority is so far ask us a following question how are the customers measuring you?

[00:08:56] So we will we will do the same and this is already promising because at least we can educate and align on the key PIs. But quite a few are not even there they say well we will figure out once we will start using it.

[00:09:11] So yeah and it's a very interesting mismatch because when you are going through the procurement process and especially if a decision to adopt a new technology was made by the sea level right but the users are not not do not have a buy in.

[00:09:30] And they don't understand what this technology is actually going to help them with is that internal process is a new revenue stream. Is it I don't know just the improvement of the internal unit economics of placements etc.

[00:09:45] It comes to a very poor adoption rate and it comes to pretty low efficiency of the technology adoption. And sometimes we step in and help customers to actually set practices and place, set metrics and place.

[00:10:03] We insist on the actually requesting reporting requesting the before and after from from their users and I think this culture is slowly but gradually building up. But I would say that this is this is the core challenge right now.

[00:10:22] I guess one of the things just from my brief experience in in RPO. You didn't always have visibility to the rest of the pipeline right I could view. View of absorption at top of the funnel it's another roadblock to try to aggregate that data across the life cycle.

[00:10:38] I guess maybe it depends on how strong the relationship is between the RPO and the talent acquisition team but just in my brief experience it seemed like if you don't have that strong partnership and you're not sharing.

[00:10:51] Data than it hurts everybody because then you're not able to see what needs to be fixed and on the RPO side you're not seeing the fruits of your effort necessarily.

[00:11:04] You didn't have a bidirectional data connection to see what happened to the people that used that all that time is sourcing and screening and it's a two point about performance.

[00:11:15] If you're not measuring that performance and you're just just spending your reels right and you don't know how to improve how to change to make things better.

[00:11:25] So anyway I guess my question is really around like any differences that you see between like the team whether you work with internal teams or external teams. Yes, so we have a limited experience with in-house teams but even based on that it's more about process efficiency.

[00:11:44] So obviously it's how many rules are getting placed within a certain timeframe and how many candidates getting screened in order to make a placement. So process efficiency yes in place. If you like swiping then head over to Substack and search up work defined.

[00:12:02] WRK defined and subscribe to the weekly newsletter. Fundamentally what I think we defined it's come from the culture and this structure of the company so it's a bit if it's smaller business and it's very horizontal.

[00:12:20] And everyone's been doing this job long enough there are not that many analytical tools even in place because everyone have been doing this role long enough the processes in the company are very horizontal.

[00:12:35] And there is mainly one North Star metric which is revenue right and everyone is working towards that North Star metric.

[00:12:45] There is no need they think at least there is not much need to measure anything else like performance or process or unity economics of that placement because the North Star metric is set. Yes, set in stone and everyone is working towards that.

[00:13:02] But what we see happens when the company is slightly bigger than the medium size and especially when it's very to be organized and when the company is more generalist rather than a specialist recruitment agency. So they have different industries, they have different geographies.

[00:13:24] This is where the needs to for extensive analytics comes in play and especially if the companies are right now going through reorganization and looking for operational efficiency.

[00:13:42] And this is where they go first like let's look how we have been doing things, how much it costs us, how it works, what error why we have.

[00:13:53] And on the one hand side I understand that the market recession is not helping to some extent before and grow but it actually helps a lot to rethink and reflect how things have been built in your company.

[00:14:10] So we, we rather take this as an opportunity to change something even internally and we help a lot of our customers to do internal audit to audit there in internal database, the state of it.

[00:14:29] The external tools that they are currently using to assess the effectiveness of those tools or simply to understand use cases and benefits of this because when you can have a very honest conversation internally with your users with your managers about like things are not working and won't exactly not working helps you to better address those challenges.

[00:14:54] And I think I'm able to do that because we are able to do that either use cases where you're helping on the talent management side too or just still just talent acquisition.

[00:15:08] are just doing the recruitment and talent acquisition but even during the acquisition phase we suggest a hiring manager including the talent acquisition manager to assess the team and the team capabilities in the team they're hiring this person for because what we see a lot that candidates are more aware about the career opportunities with the.

[00:15:38] And the organizations and they are going to be asking excuse me how they're going to progress and what learning and development opportunities exist and how they are all is going to evolve throughout the time.

[00:15:50] But by assessing a candidate and not assessing the team itself there is going to be again a mismatch and gap because if you.

[00:16:00] You kind of like put the puzzle together and you understand what skills you are bringing in to solve the I don't know problem now or to complete the project today.

[00:16:11] You are hiring a person for a longer time and you need to envision first how this person will grow within the organization how this role is going to develop.

[00:16:22] But also you need to build a certain plan for this individual and assessing the opportunities you can give this person within the company. So what we have been doing for now we are just assessing the candidate and the team itself.

[00:16:38] So when you say you're assessing the team so you're asking the team leaders and managers to provide a certain you know attributes of each player on the team or is it.

[00:16:51] More let's look at the metrics that you're gathering for the team the team dynamics the team chemistry things like that.

[00:16:58] So we do not interact with people we sit on top of the databases would it be candidate database or would it be employee database and we only work with the data points that the company is collecting.

[00:17:12] We can always go and look beyond internal data so this is what we do by going and looking at the publicly available data about the candidates or employees.

[00:17:22] And usually it is sufficient because you both just mentioned that there are quite a lot of trades digital trades I don't know social engagement or portfolio or example also for or volunteering even which can say already a lot about first plans of this person passion of this employee or next career plans, especially for individual is doing upscaling and.

[00:17:49] Finishing different like online educational courses you can definitely say and predict where this person wants to transition so usually it's more than enough. Yeah, that's great. The AI that you're using it sounds like it's more on the predictive you know last generation of AI not.

[00:18:10] Generally we have a very fair statement well so we have a generative AI for job description text writing and candidates summary writing.

[00:18:22] So this is where we apply the latest telelam and gen AI but for our skills mapping for these skills to explain them is and for the overall kind of exercise and gathering big data and converting it into the profile.

[00:18:39] We're using well the old school AI right and this plugs in I think you told me this once before but this plugs into the HDS or the CRM or both.

[00:18:52] Yeah, so I guess in between the candidate pipeline and recruiting funnel and you have a potential for kind of rediscovery in the CRM but it just seems like you know candidate rediscovery has been this illogically overlooked.

[00:19:08] Opportunity for a lot of companies as well like you just interviewed some great candidates who were silver medalists bronze medalists will have you and they're sitting in your CRM and you.

[00:19:21] You already told them that you're going to keep it with their permission you're going to keep that record in there for six months year why would it be there if you didn't have the intention of looking into that talent pool.

[00:19:35] I'm already expressed interest in working for you and you've already spoken to you know so it the enrichment as well right are people are you're client taking advantage of that capability.

[00:19:45] Yeah, so you asked what the logic and just to have the ideas but not using them and there was logic the logic was that the cost of acquisition of a new lead was pretty decent. So until until probably last year.

[00:20:03] So the new candidate lead might cost between 1.5 to three dollars depending like if you're searching for full number or if you're just searching for an email address.

[00:20:16] And it worked well because if you will place this individual you will get success fee or a salary yes it pays off this acquisition cost right.

[00:20:26] But when we look at the scale right now and the severity of the external market conditions where businesses started thinking about breaking even and being operationally efficient.

[00:20:38] And this cost is super sensitive so the it became a money game so managing directors of agencies or talent partners they looked at the asset that they already sit on and it might be couple of hundreds of thousands of candidates records.

[00:21:02] And it haven't been updated not even for a year but like a few years and they insist for a cruis to go and start using this database but recruiters acknowledge that people might have switched jobs they might become more senior and they might change location so the data is just simply invalid it's a bad data.

[00:21:24] So and then it's a money game so the cost of keeping your database updated and to update one candidate record is substantially lower than to source a new candidate lead.

[00:21:37] So but then you have I don't know 500 thousand profiles of candidates available for you and up to date in comparison to that I don't know 800 million candidates profiles linked in hands they claim which also are partially updated right so you rely on the secondary data you rely on the fact when the candidate will actually update.

[00:22:03] They are linked in profile so you might think you are so saying open to work candidates but they might not be longer open to work so it's it's a money game and I think well on the one hand side it became easier to communicate for this simple example.

[00:22:21] I don't know three dollars in comparison to a couple of cents you understand how to win but at the same time it became a little bit more challenging to convince. And it's a very interesting story that we are still trying to tell.

[00:22:45] And I think what data the value of the data that you're actually paying for in that instance can vary widely right of course if you just want to make sure you have the current email that's important because if you can't reach them it doesn't.

[00:22:59] It's still it's going to take a lot more than that to know if this is even worth pursuing and whether this is even a quality.

[00:23:08] I think you said that you can't rely on LinkedIn I know people who have left jobs and a year later they still there linked in socials that they work there but but I do think one of the underlying premises of all this is everyone's a candidate.

[00:23:24] And so why not put some effort use modern technology to find the best candidates regardless of their status because you don't actually know their status what you do know is whether they've got the right experience and skills and know how and competency to to do the job and to be successful.

[00:23:47] We can that more thing is very big topic but yeah and the conversion rates are extremely low if you do the called out to reach.

[00:23:57] So we also aware of this but we we convinced that the more information you have about candidate the more personalized outreach you can make.

[00:24:08] So you can improve your candidate response rate by just simply at least pitching them or yeah I'm using my terminology, teaching them the right job.

[00:24:18] So if you have introduced them to I mean they didn't hear from you for three years or two years and then out of the blue you're introducing them to some random vacancy which is none of the interest.

[00:24:32] What they will ask you they will ask you to unsubscribe and never reach out to them right.

[00:24:37] So this is what we also are trying to explain that you should look beyond how you acquire the candidate or where this candidate came from rather than what do you know about this candidate in order to make personalized and meaningful outreach.

[00:24:53] No that makes sense just on the AI side you and I talked about you know responsible AI and what that means and the fact that it's not just about individuals as they sort of up skill themselves with working with AI to do so you know responsibly and fairly etc.

[00:25:10] So that's a very hot topic and it's a very big discussion that we have with the US customers with the European customers because I think every jurisdiction is considering to apply certain regulation around AI and especially in recruitment.

[00:25:31] So we made informed decisions to stay away from the background checks, from backgrounds screening and from any data interpretation.

[00:25:51] So but our customers sometimes ask, oh can you tell me that this person I don't know did any fraud in the past or either I any toxic comments that this person has.

[00:26:02] We are deliberately staying away from this so the only sources of data and the only data that we are using primary data.

[00:26:13] So there is nothing secondary only what candidate has control of what they had input themselves so we don't work with feedback so maybe I don't know reviews or endorsements etc.

[00:26:27] Just because we don't want to have any bias, non subjective data be people pulating profiles and in affecting our work.

[00:26:38] At the same time we are very cautious and careful about how we process data and this is why we can only sit on top of the CRR and the ETSs because we don't store we don't save anything on our end.

[00:26:52] We are a GDPR compliant and we are also is a compliant just because we work with a private data. We make every part is involved informed about the ownership and informed about the liabilities so if it's an agency and we are working with their database.

[00:27:11] We insist on the data protection agreement to be put in place and even we are a fully integrated solution I didn't know with greenhouse or boom horn and they are technically the current tours of the data safety.

[00:27:26] We still insist that recruitment agency acknowledges the responsibility on how they obtain candidate leads but they need to have tick boxes and place that initially. We are able to get a candidate up to date to be stored or data being processed by a third party.

[00:27:48] We are able to get a candidate to be stored in the data that we have in the past. We don't want to have our data being processed by a third party that we don't have a control visibility.

[00:28:06] And at the same time you also as a user of the data want to be on a safe side in case there are any claims or in case there are any regulations that are going to be put in place.

[00:28:21] You want to be 100% protected and assured that any third party is that you are using in order to help you with your data management or having all processes in place to be liable and to be compliant.

[00:28:41] I agree that those platforms should own that piece of it and I think obviously there is a one to show.

[00:28:51] And I think that some kind of ranking or score or something like that which certainly is the focus of the not the exclusive focus of responsibility I'm probably but certainly when it comes to measuring adverse impact and how.

[00:29:06] A. I might be part of a decision making process at each phase of the hiring process or elsewhere inside HR those are the important you know aspects that I guess of or of the most interest to you know regulators to make sure people aren't being treated unfairly and.

[00:29:29] I guess one of the questions that comes from that is as you're aggregating data.

[00:29:34] There are filters that get applied to make sure that we're sort of removing any sort of demographic data that could have the decision maker or even a recruiter you know injecting their own human bias.

[00:29:47] So the core product our database enrichment doesn't have any filters so we take a record we enrich it with everything that we can possibly find associated with this person.

[00:29:58] The second product can do so saying this is where we possibly could have had any filters about bias or I don't know background gender or nationality whatsoever. No we only have filters about location skills and years of experience that's it.

[00:30:18] But I wouldn't deny that some clients requested to have more extensive filters applicable but again being fully integrated with the CRMs and operating under the CRM policy.

[00:30:32] We have to comply first with the national regulations and second with a certain codes and principles of the HR systems that we are sitting on top of.

[00:30:44] But again it would be against our core values as as peach me because we want to make recruitment process equal and faithful everyone.

[00:30:54] Our one of the first versions of the product was even anonymous. So we wanted to anonymize candidate profiles to make screening more or less biased but it wasn't really widely adopted.

[00:31:10] Well at least until now maybe things will change. Yeah, no that's fair and I think probably another item that is on the TST or the CRM right I mean that should be fundamental. Whether it's a tick box or something else I mean should probably be by default.

[00:31:26] If those tick boxes jacks you try to mitigate human bias and not focus on whether or not AI is biased because as we know the AI is biased because the data it was fed has bias in it so yeah that's fair when we think about journey day I'm just curious if so you obviously use it in your.

[00:31:51] I'm just curious like you personally as an object or an engineer maybe favorite you know tools or use cases that use. So obviously everyone played and tried open AI right and Chad GPT so I'm using tools which help me with productivity so I do some planners.

[00:32:18] The tools that help me with my email optimization or just simply remind me to do certain stuff.

[00:32:27] If you would say if I am advocate and a frequent user of those solutions no I mean I'm a little bit old school because if I forget about this I am not really.

[00:32:42] It means that it wasn't important for me so I have I have a lot of people reminding me of things that I need to do and I really don't want even AI.

[00:32:53] To keep reminding me of those but to streamline I don't know email out Rachel payments or something like that yes we use that solutions.

[00:33:04] Yeah I'm still trying to get comfortable with how much personal information I want to share with the me I but I know it will help me and certainly an optimist.

[00:33:16] But we'll get through some of these ups in the road and you know happy right you know privacy settings and it will still provide a lot of support.

[00:33:28] So one of the obviously the theme of this podcast is around how to how people sort of up to go themselves with working with AI AI is not going away so when you when you saw that title of the podcast.

[00:33:45] I'll be your Iq what comes to mind what advice would you have I know you still have some reservations about how and where you're using it but what advice would you give for anyone who's. You know trying to understand how this is going to impact their career.

[00:34:03] Well this phrase for me suggests to focus on improving the literacy right and knowledge and proficiency or in artificial intelligence so. But I thought that it means something else and I'm not as a non native speaker I'm just not getting it.

[00:34:21] I think this this is a very accurate title to you it kind of conveys a sense of moving forward and being at the forefront of the technological advancement. And mastering AI concept and applications for your own benefit of the benefit of the organization.

[00:34:43] And by looking at all of that high-perant chat to you that we witnessed from all the last year and actually the very low adoption rate of the chat to you on the organization level.

[00:34:58] I don't know if you have seen the latest surveys but majority of people are surveyed so that they are writing emails with the was charge of it. But there are so many things you can do with this.

[00:35:10] I would say that the main advice is to cut through the noise is to really assess and critically evaluate like where you need help.

[00:35:20] And where you can need like where you cannot walk away without a human help where you just need to make a higher like you just said an intern in India or AI.

[00:35:33] This is this debate I think every organizational has right but on the personal level I would say that you would need to evaluate the aspects of life that you can.

[00:35:48] Give away and delegates to technology and where are those boundaries where you don't feel comfortable technology to to penetrate your personal life. So you both also as a skeptic person I can sense.

[00:36:05] Yeah, I want people to think critically when they're using it and yes there's a sort of responsible responsibility angle to that AI literacy. You don't want to know everything.

[00:36:19] To know what's going to take to continue to succeed and in your job to not be replaced by automation and AI to recognize that you're colleagues. Your manager, your leadership team should be doing the exact same thing.

[00:36:37] I mean it's being embedded in everything and you've probably been using it already. Some extent at least 2.0 or around the earlier generations of AI there's machine learning and all of our virtual assistance which at this point seemed pretty dumb but they're getting smarter.

[00:36:55] You know the series the Alexis but you've got to understand what it's capable of and when yes went to go to that solution or that digital.

[00:37:06] Worker whatever but it's and some ways it's analogous to what organizations need to do as they do their strategic workforce planning we've got work to be done and let's find the optimal way to get it done and the optimal people.

[00:37:19] I get it the podcast just isn't enough that's all right head over to your favorite social app search a work defined WRK defined and connect with us.

[00:37:31] So you're I just think going forward your talent ecosystem is really a matter of how do you optimize who does what when and how do we continue to provide good experiences.

[00:37:47] As you know data moves through a funnel or as people move through a process like hiring and so it's a balance of using technology the right way. Well providing good experiences for all parties.

[00:38:03] Well, Tina this has been great thank you so much for taking the time to give our audience a lot of your insights and we will speak again. Thank you Bob appreciate your time absolutely thanks again thanks everyone for listening and we'll see you next time.