Karim Ahmed Talent Engagement at Hellofresh on Shally's Alley

1 PM on Friday, May 20th, so that must mean it's time for Shelly's Alley. Welcome! I have actually two special guests today, but before I tell you about my special guests, I wanted to show you my, my little artwork that I did back there. Unfortunately, it's not very clear on the screen. It says, sourcing a device for turning caffeine into candidates. Um, but you can't read it, so it kind of lost its joke. Anyway, with me today I have Kareem and Glenn. You guys remember Glenn? Uh, it's been a while since he's been on the show, but he used to be a regular. Um, so really happy to have him back. Kareem, tell us a little bit about yourself and, uh, introduce yourself here.

Yeah, thank you, thank you so much, Shadi, for inviting me. And, uh, yeah, I mean, I I originally come from Cairo, I come from Egypt. Yeah, my experience is like in, or like my educational background was not in HR or psychology, not coming from the classical recruiting or like the authentic recruiting background. I was just more studying economics for 7 years basically.

Right, you have a master's in economics, right?

Exactly, yeah.

So that must be interesting to apply economic theory to recruiting.

Exactly. You know, like I think in economics we do a lot of research, right? You know, like, and we work with a lot of data, interpreting data and see like how this could work. And that was really, really helpful for me when I worked in recruiting and sourcing specifically. So it was very interesting to see how can I apply that on sourcing and recruiting. And yeah, I mean, after I finished my education, I thought like, hey, I love working with people and I didn't feel that much in doing research and just sitting in the back and just doing research and couldn't really see the impact of my research on that. So that's why I decided to go for recruiting because I was always passionate about it.

How did you even find out that recruiting was a job? I mean, a lot of people don't even realize that it's a profession. Much less, you know, want to pursue an opportunity in it?

It was very simple. I was volunteering in an activity at university and I was interviewing people. Yeah, this is how it started and I really enjoyed it, talking to people and just discovering their passion. And this is how I knew that recruiting could be something for me.

Right, right. Wow. So Karim actually is from Cairo. Yeah. And then, well, I actually had a question from that. From, you know, being from Cairo, how does an Egyptian walk? Because there's that song, you know, that says walk like an Egyptian. I never understood how Egyptians walk. It's so different.

Like normal people.

That's what I thought, you know, exactly.

So walk like an Egyptian.

Walk like an Egyptian. So from— yeah.

I think it was very popular in 2011 because there was like kind of a revolution in Egypt that happened. So like everybody was proud of it. So they say just like walk like an Egyptian. This is how it came from.

You gotta promise that that is your last dad joke of the day.

No, I can't. I'm sorry, I can't promise that. Dad jokes are, uh, you know, endemic around here nowadays. Um, but so anyway, so you grew up there and then you went to study in Germany. Yes, exactly. And I think while you were studying in Germany, you must have had some sort of like I don't know, um, traveler's, uh, traveler's bug or something because you went to India as part of the ASEC program.

Yes, exactly right.

So a lot of folks don't know this, but actually I was an ASEC. I went to Australia with the ASEC program, and I don't think it's on my profile, but when I saw that, I was like, oh, another ASECer. It's kind of a— tell us a little bit about ASEC, like what, what was it, what is it? And you also worked for there, but Tell us about the program first, um, because that's more interesting. It's something that you can do while you're in college. So what was your experience like?

Yeah, so just so that people know also, like, it's a student organization. Um, it's like very global and very international. It's the biggest in the world, basically. So what you can do is that you can have, um, exchange programs between countries, and I participated in one of these. So I went to India to volunteer in an educational program. So I was basically teaching math and English at that time, and I spent there 4 months in India. And I can tell you, like, it was like hell of an experience because I had the chance to travel around India and so much big country, you know. Um, so yeah, I really, really enjoyed it that much because I felt that I'm giving something back to the society as well. So at that time, I really enjoyed it.

Yeah, so it's a, it's a leadership building program really, um, is how I would have, I would have put it, right? At least for me it was. And, um, another interesting parallel, when I was in ASEC, it was with economics. So I actually studied international economics during my ASEC exchange and, you know, learned about purchasing power parity from the point of view of the Pacific Rim. So, um, for those of you that are interested, I'm going to put the ASEC, um, I guess website here. I'm not sure what else to call it, but here Just don't put any ISLM curves on the screen. Okay. Hey, you said no bad jokes. Well, technically that's not a bad joke, but anyway. So yeah, so you would just spend time in India and then went back to Germany and then returned to sourcing, and then you got a master's degree and then returned to sourcing again. So you had a job in sourcing, left the job to go to get a master's degree, and then came back to sourcing again. With a master's degree. Glenna asked me, why did he go get a master's degree? So what was your point for that? What— why— already had a job in sourcing, why did you pursue a master's degree anyways and come back to sourcing?

Before I just take that decision, I, I heard a lot about data-driven recruitment at that time, you know, and I was just reading about it. I was like, what was that, you know? And, um, that was very interesting for me, and I, I thought that getting a master's in economics will elevate my research skills and also really can teach me how to deal properly with data. And that was really, really helpful because I think it helped me a lot to visualize and interpret data and also like spotting the wrong numbers when you're speaking about all the reports that we provide to our stakeholders. So it helped me a lot, like when there's a wrong number, I can just spot it and see it and just try to fix it as soon as possible. So it really helped me a lot in that sense.

Okay.

Yeah.

All right. So you can tell patterns and data pretty easily through that. By the way, this was at the Berlin School of Economics and Law, um, where, where he studied the economics. And then I think you were at Wayfair. So from there you went to DrLib, and now you're at HelloFresh, where you're running a large team of Um, somewhere around 6 to 10, something like that, uh, sourcing professionals, and they're working on positions all over the world, right? Not just in the US, not just in the EU, but globally. How many countries are you guys recruiting in?

We're recruiting in 17 countries. So, um, we recruit across all functions in HelloFresh. So that means, um, in business recruiting and executive and tech as well. It really depends on the function. It really, like for example, in business recruiting, we might have a role in Japan that we could support on. We could have a role in Italy. For tech, we, it depends on the tech hubs that we have. So we have one in Canada, one in New York. So it really depends on the role. So we could support all over the world through our 17 countries that we recruit.

70? Wow.

17.

Oh, 17. Okay. All right. Okay.

Now, I don't want to jump the gun here, but I have to say you can't skip over Wayfair that fast because that is a company that has done a lot of interesting stuff around data analytics. I knew the place when Arthur Matasewski was running the sourcing function. So I guess you overlapped with him a little bit and probably Elliot Garlock. What did you learn from that environment that you thought was either cutting edge or adaptable to the stuff you're doing now?

Very interesting question. So I worked in, in Wayfair at that time. I worked in executive sourcing, and I think it was very, very interesting how we— that how to bring market information to the stakeholders and how can we leverage that in our recruiting, you know. I think we had to deal with a lot of cases that we could recruit for Europe, like for example at that time there was no option for remote work, for example. How can we attract candidates? How can we give them the best candidate experience at that time to make sure that they can relocate to our headquarters in Berlin at that time in Wayfair? So I think the biggest key learning for me at Wayfair at that time was more about focusing on talent intelligence projects. And that could be on, think about all the information that we're missing before we speak with the candidates. For example, you know, like you don't know the salaries, you don't know the leveling, for example, that they have. So you could work in another e-commerce company and you can see this someone, it could be a VP of whatever, function it is, but still this kind of could be too junior for you. These kind of things matters a lot in executive, especially if it's a big company like PayPal. So I think Talent Intelligence was the key highlight that I have learned, and I think it helped me a lot in my sourcing and recruiting journey afterwards.

Was that also international?

That was based in Europe, you know, like we were basically recruiting like—

Just in Europe.

Yeah, for Berlin and the UK. But yeah, we were just recruiting everywhere, but mainly it was in Europe.

Wayfair had a big team of sourcers at their Boston headquarters, but they pretty much left Europe to the folks in Berlin.

Exactly. So the sourcing was mainly, especially for executives, was mainly run by the people in the headquarters in Berlin. But we had a lot of touch points with the people from North America as well, because at that time we had to take a lot of projects that they've implemented in the US, and that was very interesting. So yeah, it helped us a lot to scale.

And they serve beer kegs every Friday to the staff. They cut the day an hour early. Did they do that in Berlin as well?

No, not at all. I don't, I don't know yet. I don't, I don't know like what was going on now because I moved in 2019 at that time. Um, I'm not quite sure.

Didn't we have a meeting at one of their, uh, what was that, a Wayfair? No, that wasn't Wayfair. That was another company where we had that big sourcing meeting that you put together.

Well, yeah, the Boston Area Talent Sourcing Association used that as our host for a number of meetings.

Okay. Yeah, right. Okay. That's what I thought. Yeah, I was there. Yeah. Cool. Thanks for bringing that up. So actually, Karim is— you guys might not know this— he's polyglot. A polyglot that speaks 4 different languages. That's a lot of languages. Are you actually fluent in French or is French more like a— you can get by?

Yeah, I just like— I can just understand it, but not really. I'm not really confident to speak French, unfortunately.

Got it. Okay. Yeah, my French is terrible, but I can probably get directions if not maybe a little bit more than that, but that's pretty much it.

Très bien, mademoiselle.

Nice. Merci beaucoup. All right, so let's talk a little bit about the international component here. So right now you're working on global sourcing. One of the things that, and I'm going to just spring this on you. One of the things that I'm looking in, because I'm also, you know, I oversee global sourcing as well. And I think Glen does too. But one of the things I've been struggling with now is the whole idea of somebody who has talent could be anywhere in the world and could be equally as capable at doing this job as someone from another part of the world. So why haven't we really taken advantage of that in recruiting, right? Why now that, especially now that companies are remote, some companies may not accept this because they're not, they're not at the point where they can say, okay, we're, we're going to operate mostly as a remote company, but there are organizations out there where remote working is accepted and it's standard, right? So if you can work remotely, 2 questions. Why can't you pick up and go wherever you want to go and work from there, number 1? Or number 2, why can't we recruit someone from somewhere where we normally don't, right? So why aren't we taking advantage of that? I think I have a little bit of an answer, but I wanted to hear how it, how is working for you guys. Are you able to recruit anywhere in the world and people can work remotely? Or if not, why not?

Well, I would say we currently at the moment, we don't like work with the full remote policy that we have here in North America or Europe. I would say these kind of things, it's like a really, really major decision that it needs to be done through the entire company. So that's something that you can't really test in one function and the other functions are not. You know, so it's a lot of people involved in the decision-making process. You know, for example, in the talent team here, we work from all over the world. You know, like I could work from Berlin, some people could work from the UK, some people could work from different cities in Germany. You know, it really depends on the person who works and leads the team. But when it comes to company policy, it might sound easy that we say like, hey, we will implement this with the entire company, but When it comes to the decision-making itself, it might be a little bit harder. I would say there is constant reviewing of like the policy itself and how this affects our hiring. That's something that we speak on a weekly basis with our stakeholders here, you know, how this could change the way we recruit. And I think one of the reasons that maybe some stakeholders might not be convinced about that, that we don't bring the right data for that. You don't bring the right arguments. So how can this elevate productivity? How this can elevate your, uh, the speed of hiring itself? You know, think about—

we know it can, right? We're experiencing it now. I mean, we know that we're more efficient exactly working from home for the most part, but you're saying that we don't have the, um, we're not— we don't have the metrics to back it up. We don't have the, the case study or the, the evidence to back it up.

Exactly. That's the major thing. Because I think you really need to bring the right argument, the right numbers, the right matrix for the people who want to hire for that. And also thinking about the time, because in recruiting, it's also about time, right? Especially in sourcing, you know, like if I will hire someone now and this person could start in 4 months, think about like the loss that you have from that, even the candidate is great, but It takes a lot of time for that. Recently, it is what was proven, that we can work from home and we can work from anywhere and productivity will still be the same. Maybe it increased, maybe decreased a little bit, but it still could be doable. So I think we've seen now a couple of cases in the market like Apple, for example. I don't know, like other companies, I don't know. They're maybe like started getting to work from the office again and people in tech start getting frustrated and some people might leave. So I think it's not a benefit anymore. It is something, it's a way of working. And I think companies should not ignore that.

It's not a benefit. Okay. It's just, it's just a place to work. So you work from here or you work from there. That's interesting. I like that analogy. Well, I actually have something else that I wanted to address with regard to that, but there was a question from the audience that someone asked from Facebook. When you said what type of number— when you said you had spotted some numbers that other people didn't see, what type of numbers did you spot that didn't look right when you were using your economic analysis? So like, what, what specific patterns did you identify that other people didn't think of looking for, and how can we learn from that?

Well, that's a very interesting question. I think, um, thinking about the like the history of the data itself. You know, like when you, when you see, like, for example, you're looking for time to hire, for example, and you really see, like, there's, like, you have to think about the sequence of the numbers that you have from last year, for example, and you have to cross-check these kind of data. So thinking about all the matrices that we check in recruiting, that could be time to fill, that could be time to hire, that could be the the pipelines that you have, like the whole, the pass-through rates through these pipelines. Sometimes you could have like a tool that could analyze whatever tool you work with. You could do this manually and then you see like there might be some numbers that couldn't make sense when it comes to, um, like some numbers just doesn't make sense when it comes to like time to hire. For example, that's something I spotted recently that we saw that the time to hire is 18 days and we saw that and we just like cross-checked that through all of the data that we have historically. And in order to do that, you need to just think about the patterns that you have here and then you have to recalculate it again and sometimes you have to do this manually, you know, because not all of the time you have to depend on the tool that you're using. So Yeah, it just, it's, it's very complicated sometimes.

Assume that the tools are accurate. Yeah, because there might be something exactly missing, or yeah.

Well, speaking about tools, I'm kind of curious, like, are you someone who actually would create Python scripts and work with the libraries to do NumPy analysis, or you're like, you're using Tableau and I mean, what would be your tools of choice for that kind of thing?

Well, I would say like I'm recently using Tableau and I think Tableau is a really, really good tool for that. The trickiest part here is depending on the ATS that you're using. So you have to make sure that the input of data itself is like the right thing, you know, because you can't expect Tableau to give you the right data and give you the right analysis. If you're not having the right data on your ATS. So I think working with data in general, that's like the key thing here is thinking about the input and thinking about a way to standardize the processes to make sure that the input is right. Then you would get a really massive output from that.

Yeah, but the thing is that you can't go back and tell everybody to correct the input when they already did it wrong. So if you have a year or 2 years of data, and you wanna do a comparative analysis and the information back then was incorrect, you have to basically apply some kind of algorithm to correct the data. Otherwise you're not gonna be able to do a comparative analysis, right? Yes. So you almost have to go back and put a Band-Aid on it and fix it. Somebody from Facebook said Excel macros. Yeah, you know what? I, I, Tableau is great. I, I've used Power BI quite a bit, but quite frankly right now I'm using Google Sheets because all of the information that the ATS has can be output into a Google Sheet using a simple query. And it's just there already, so I might as well just use Google Sheets. And I've got some dashboards that are able to put together several reports because like you said, the ATS is— sometimes they have it, sometimes they don't. I'm fortunate that the ATS that we have is able to push the data out into a Google Sheet. Unfortunately, the downside is there's reports that have some of the information, but not all of it. And nobody thought about it before. And so it isn't designed— the ATS doesn't have a report for that. And there's no custom designed reporting. So I have to take two reports. And what I'm doing now is I've got the first report on one tab and the second report on another tab. And then on the third tab, which is the tab that I'm showing the dashboard, I have a query statement that queries the columns from the first and second tab into one, one sheet. So basically it's like, it's not a merge because it's not merging the sheets. You can't do it because it's separate columns, right? But for example, the candidate ID record is common to both reports. So I query the first one for the information I need, use the candidate ID to query the second one for the other information that I need, put both of those in one sheet, and then I can use a chart or a dashboard to show that information. So it's, you know, sometimes it's a matter of getting creative with combining the data the right way. But the question that I wanted to ask you with that is, how do you identify outliers? Because you know more about economics probably than I do, I'm sure. You've probably forgotten more about economics than I learned in my program. But I remember that there was a chart that had like a distribution And if you look at time to fill, for example, what I noticed was that the time to fill chart is pretty much all over the place, right? But there are clusters. Now, there's probably some name for this analysis, maybe cluster analysis or something like that. You know what I'm talking about?

Sorry?

You know what I'm talking about? The cluster analysis?

Yeah. I mean, I'm sorry, the internet connection was a little bit—

Yeah, I'm trying to explain what I think is a cluster analysis, but I may not have the right terminology. And so it has the, the time and number of hires, you know. So what it shows was a bunch of dots collected over here in the 1 week to 2 weeks time period, and then not very much, and then a bunch of dots over here in the 60-day to 90-day, right? So if you do the average time to hire, it's 45 days.

Exactly.

But there is exactly zero hires that took 45 days.

Yes, I get what you mean. Yeah, it's very— it's really, really, really tricky.

Yeah. So what are you gonna tell the organization? Our time to hire is 45 days. And, and your leadership team goes, no, it's not. My team hires people in 2 weeks. Why are you telling me 45 days? And the other leadership person is going to say, are you kidding me? I have positions that are open for 90 days. Where are you getting 45 days? Well, that's a mistake that we make because we took a look at the average. Instead, you have to figure out what those patterns are. Is it a type of role, right? And I think you should also, and this is what I was gonna ask you, is should you eliminate outliers, right? You have 2 reqs that didn't get filled for a year. Maybe you should throw them out because there's something different about them. And then you have 12 reqs that got filled in 0 days. How does that happen? Well, I know how that happens. The position was posted and filled the same day. So that's a zero-day fill. Maybe you should throw those out because zeros are gonna affect your average too. What do you think about that? How do you clean that? Those types of, do you take them out? Do you account for them somehow?

Yeah, I mean, that's actually an interesting case that I'm working on right now. So most of the tech roles that we work on are evergreen, right? It's very, very hard to test.

You don't fill it. Yeah, you fill it over and over again, right?

It's like it's running the whole year, you know, so it's, it's very hard to, uh, to really think about, um, the right time to fill here. Also, like, if you make an initiative like a hiring day, for example, you, you have to just, uh, like, if you just do a hiring day, for example, how can you think about the time to fill here? If you put it to the whole organization, it would be really, really hard to, um, find the right number, uh, for time to fill. So think about like what kind of things that will mess with the number itself and try to take it out. This is what, what would be my, my whole suggestion here, because, um, it would be way, way more complicated to try. Or maybe you can just divide it into clusters, you know, like as you said, like if you have a hiring day, for example, how can you calculate the time to fill here? It would be much, much easier rather than calculating the higher the, the roles that we get from a hiring day in all the roles that we worked on throughout the year. Um, so that would be the simple solution. And to be honest, when it comes to data, I try not to overcomplicate things because when I overcomplicate it, it gets way, way worse than expected.

Yeah, I see what you mean. Right. Okay.

Yeah.

So basically try to take out as much of the data that isn't going to affect your outcome so you can reduce it and see the patterns easier. And then, but about the outliers, like the zero-day hires, do you remove them?

Yes.

Okay, so you do remove them. That's what I was thinking. And then, and then the 360-day one, which, you know, that kind of thing. Glenn, what do you think about that?

Uh, I think I would hire someone on my data analytics team to figure all that out.

Unfortunately, that's me.

I mean, we do have a people analytics team here that we're full like really focusing on working on that. But yeah, I mean, at the end of the day, you can't do everything in recruiting and sourcing, you know, you can't.

No, no, no, no. Sourcing does typically sometimes inherit the analytics function for some reason, maybe just because they have the technology. And we have the same problem with the evergreen recs, but what we're doing is that people that are ready to go into a hire essentially get moved out of the evergreen rec into a, I don't wanna say a real rec because that makes it sound like the other ones aren't real, but they get moved into a live rec that has an end date, right? So they get moved from here to there. However, that does affect the numbers because when they get moved into it is the point that they're actually ready to conduct final interviews. So that makes it look like it's a much shorter timeframe, but that's kind of how we solved it. We are looking at the total life cycle though, from the moment the candidate actually entered the system to the moment that they closed out whatever they're, you know, whether they got the offer or didn't get the offer. So we're looking at that as a pattern. There was a question from the audience about, here, let me show you. It's, would you not want to flag the data before using it to get a report? I'm not really sure what that means. Do you know what that means?

I mean, it's Probably like just when you generate the report, you mean, or like before?

I think they're saying, would you— this was when we were talking about identifying kind of errors in the data. I think what they're saying is, wouldn't you want to flag the data? And maybe it was my comment about zero-day hires. Maybe, um, they're saying flag it or get rid of it, delete it out of the dataset. But you can't do that with Tableau because you're connecting directly to the source. So you'd have to export it to some sort of intermediary.

You have to do it manually, unfortunately. That's okay.

Okay, that's the answer to the question. You have to do it manually so you can clean the bad data. Otherwise Tableau is going to show you all of the bad data as well. Okay, that makes sense.

Because yeah, once it's, it's on Tableau, like, then you can, you can't just fix it. You have to just do it manually. That, like, spreadsheets, when you said this example, it's very interesting, to be honest. Um, not so many people now depends on, on like like at least the people I know, which is really interesting case from what you said. I think it's very smart.

I don't have a choice 'cause the data is, you know, comes from 10 different places. And there's also historical data where the stages were different back then. They only had 3 stages. Now we have 12 stages, you know? There's another question about Germany that came in, and this is actually something I'm quite curious about myself. The response rate in Germany is, well, they say unique. I'm going to say very low to also sometimes responses can be rather curt or direct to outreach. Is that— has that changed? How do you see that now?

Like in 2021, I would say like it's completely different than we are now. Like I think 2021 was the hardest year for recruiting ever in Germany. Um, the companies here in Germany were recruiting like crazy. Everybody was scaling up. I would say the response rates were very, very low in many aspects. That could be on business, um, and tech recruiting as well. Um, I think now the response rate in, in the business recruiting side, it's like getting higher a little bit. In tech, it's still low. However, you really need— like, when it comes to speaking to the candidates in Germany, I think that's also an advice I got from my, my mentor before, like, when I was in Germany. He told me the first thing that you have to do here when it comes to reaching out to candidates, you have to be a very human being. You know, don't act like a robot. Don't give the feeling that there's something automated or there's something that you didn't look on the profile. Just be a very human being in your concept when it comes to reaching out to candidates.

Is that good advice in general though? Like not just in Germany, but like everywhere?

Yeah, exactly. So that's actually something I would say that I will always recommend whenever you thinking about your InMail. Regards to like the question, if the response rates are like getting lower or higher, I would say in commercial or business recruiting, it's getting higher. In tech, it's getting a little bit lower. The response rate in Germany in tech recruiting is really, really low.

Tech is going down response rate, but everywhere else is going up.

Exactly. I would say a tip to mention for people who are looking to recruit or source in that region is to search for the acronym DACH for DACH region, because that is the part of Europe that is German speaking. And people who have that on their profile obviously will know a little bit more about that region than other people. So that's a good keyword to search for.

Thanks for the tip, Glenn. Actually, I've seen that in a few profiles now. Is it like under 20%, you think?

It is under 20%, yes. It could reach 12% or maybe even 15%. But we tested a new way to message candidates in Germany. It's worked quite well that we are sending the, like if you were hiring for a developer, for example, we're sending the in-mail with their coding language. So that improves our response rate significantly to, from like if you say, if you have a template that have 15%, that could take it to 35% or 40%.

Wow. Okay.

Just try it out. This one, I could share the link here if you want to, or could share even the message. You can just work with one of the developers that you have in your company and send it in. That is really, really good way now for—

Interesting. Glenn, do you remember Glenn and I had a colleague at Microsoft that sent out emails using binary zeros and ones. Do you remember that? Yeah.

I mean, that is similar to the trick of just using like a JavaScript or other programming language statements specific to the type of talent you're targeting and make your recruiting message in that language.

Yeah, he did it with binary and it was like he got some interesting responses. When I was at Google back in 2003, we had a challenge on a website that it was a binary code and if you solved the binary code, it gave you another website that gave you a math problem that was an algorithm and if you solved that, it gave you another website and then eventually it landed on a, you know, on a form where we would reach out to them and contact them. It was kind of a fun game, but that reminds me of that. So that's a good idea. So you're actually putting the language into the email.

Hmm.

Somebody from Facebook said they have an unusual rule in Germany where you can speak for 3 minutes max at work. I've never heard of that before. Is that true? To a candidate? So like when you're recruiting a candidate and they're at work, they can only talk to a recruiter for 3 minutes max. I don't know if that's true or not.

Oh, I didn't even know, hear about that.

You didn't know about that? Okay. All right.

That sounds like a person who works in a very restrictive environment and they should get recruited recruited elsewhere immediately, right?

Yeah, they told me I can't talk for more than 3 minutes at work. I'd be like, um, about what? I can limit it to a topic for 3 minutes. Oh man, we'd love to see that, uh, that email if you want to share it with us. But, um, yeah, cool. Well, it's, um, it's been 30 minutes. I, I— this has been fun. Glenn, do you have any other questions that you want to ask?

Well, I'm kind of curious about that latest question in about, um, uh, German versus English in subject lines. Do you have a thought about that?

Oh, let me see. Oh, here we go. This one? Yeah. Which subject lines are getting you better responses, German or English? In fact, since you're recruiting globally, why don't we just turn into the— into not necessarily German, but Do you get better responses, generally speaking, using the native language as the subject line?

Exactly. I would say, yeah, exactly. It makes a lot of sense. For example, we're recruiting for Japan and if we send an email in English, they will never get responses there, for example. That was something we have tested. So we had to translate it to Japanese in order to get more responses. Same thing here.

Not the whole email, not just the subject line, but the whole email.

The whole email has to be like, it's the same thing in Germany, for example, like when I used to recruit for salespeople and for my previous job, we had to send the email in German or, and not only the subject line because it wouldn't make sense at all if we sent the subject line in German and then the whole message in English. So you have to really think or read the profile and see like if they do speak English or not, because some people don't speak the language or English in Germany, so they have to um, send them the whole email in, in German.

So even if the job requires that they speak English, you still translate it to that, to the language, because it's about, it's about reaching out to them. It's not about the job they're going to do. They do have to have English as a language in order to be able to do the job, but you're not reaching out to them in English, you're reaching out to them in their local language.

Exactly.

They— okay, yeah, that makes sense.

It's always better. It's always better to reach out with the local language.

Okay. Yeah. Well, the one last question that I had was, um, talking about the, you know, internationalization and how can we take advantage of recruiting and sourcing people everywhere. What I've been starting to play around with is PEOs. Have you heard of PEOs?

No, not yet.

Not yet. So, um, basically a PEO is a professional employer organization. It's essentially like a, like a joint custody agreement between an employer and an employee in another country where that employer doesn't have a base of operations. So the PEO is the employer of choice, not choice, right? Is the employer of record, but they lease the employee to— so we could still recruit somebody and hire them, but they actually don't end up working directly for us is not outsourcing, it's just like an employer record. So it's—

yeah, there's a problem here. Do you still pay them this? Like, if I do this for someone, like let's say I'm recruiting someone in Egypt and this person will work for Germany, would you still pay the same salary as the German market?

That's what I was going to ask you. I mean, I'm just starting to explore this. Yeah, yeah, that's a good problem to answer. That's a problem. So what is the answer?

I get what you mean. I would say like what's currently happening now, if you would do that, you would— most of the companies who do that now, like, um, they would not pay the same European salary, like the average salaries in the European market. They would think about the average salaries that, let's say, in a country like Egypt, for example, the developer salary could be €1,000. Like, they might pay €1,500, and this is how it works.

So you translate the salary not by converting it dollar to currency, but by converting it to the purchasing power parity.

Exactly.

So you basically say, what is the, you know, the, the income for someone who does this job? Almost like doing a, a compensation analysis in your own country.

Exactly.

Do a compensation analysis and you pay them their salary in their language. Okay, so that solves that. What else should we not I mean, should we think about that we're not thinking about with a PEO?

Um, I think it's, um, it's, it's mostly also like when, when you think about that, like some companies now what are you currently doing, like when you relocate people to, to Europe specifically, we're getting deeper to Germany, it takes at least 3 to 4 months to relocate someone, right? Sometimes what we're currently doing now is like, okay, can we let them work from there until they relocate? So I Actually, it's a really good option. We do it sometimes with this kind of concept, but it's, yeah, it's like kind of a painkiller for the problem that you have. Is it a way of working? Would companies accept that? Like, just say like, hey, hire people from abroad and let them work from there with their current salaries? I think that that could be like a tricky question. Yeah, it was in Germany.

Could be a game changer all over the world, but I'm sure there's plenty of problems that we aren't even beginning to think about. Like, you know, do you translate the compensation? Is that unfair to somebody doing that job, the exact same job, same amount of work, same knowledge level in another country, getting paid more economically just because of where they live? You know, are you gonna adjust for, managing in time zones. And, you know, so if you have meetings every day at 12 noon, and that's great for the US where you can be there, you know, in California and in New York at a convenient time, but now you have all these people that live somewhere else, that's their daytime. Now you're like, how do you do that? You're gonna, you know, run the same meeting twice, or how do you accommodate? There's some countries where there really is very little overlap with the time zones with, you know, other countries. So it's like our regular 8 to 5 workday does not at all overlap with their regular 8 to 5 workday. It's completely the opposite. So how are you gonna reconcile that? I'm sure there's lots of other things.

Well, I think, you know, the problem here is that something needs to change, to be honest. When you do this with candidates, you let them know that you will work with the European time zone, right? Either they accept or not. Some companies are flexible. For example, I could be in my team very flexible when it comes to working hours. You do your job wherever you want to do it as long as the delivery is there anyways.

Except for the meetings and whatever you have to— there are things that are synchronous. Exactly.

Some things, for example, we have here the team is split between Berlin and New York. Sometimes we have meetings that the people in New York couldn't attend, right? So sometimes, like, it's also, it's, um, it's all about the compromise here between both parties, I would say. So, um, back then the employee always compromised, but it's not the case anymore.

Not anymore, right?

Yeah, the employers also should compromise sometimes. And, you know, like, you have to make sure, like, okay, like, there are things that we could agree on, like, weekly standup for, for the team, for example, that's something you couldn't skip. But maybe like another meeting that could be recorded, for example, um, could that be an option? And then you have more flexibility in the workspace. So it's a really, really big topic, and, and there's so many things that could be done around here. But the problem in, in that sense, um, when it comes to the, um, like working from abroad is more about some managers, like, they feel like we don't have a current team, we don't have this culture thing, right? And I think it's time that people have to rethink what is culture fit or what is the team culture itself. Does it come from—

and how do you adapt your culture to include other ways of working?

Yeah, exactly. So it's—

I was also thinking from an economics point of view, and this is something that I did remember from my economics school, is that when you, you know, you introduce the cost of producing something, um, and there's tax income and, uh, sorry, income tax and other considerations. If, you know, if, if the cost of production of a good in the United States is X dollars and that's an intellectual property, it's not a solid good, there's a whole bunch of, you know, accounting ways to, to deal with it. But now you're adding a cost factor from another country with a different type of, you know, tax system. So how are you going to reconcile that, right? It's like you can't just say, well, we're going to make this product with parts from here and there, except the parts from there we're gonna pay less for because they're from there.

Exactly.

And so now it's, you know, now you're like, well, there's two, but what do you do, take the average of the parts? I mean, that, yeah, I don't have an answer, but I remember that from economics was like, the question is how do you balance the exchange essentially, you know, in everything that includes taxes and everything.

I would say don't also forget the, uh, political influence of, for example, um, either tariffs or inspection of goods in order to be shipped, um, My wife's business does a lot with China, and I can tell you that there's a big problem in the supply chain, not from the speed in which they can create stuff, but because there are all kinds of restrictions on the fiber content of things that you're shipping in fabrics between certain locations. And the rules are a little different actually, depending on where it's being shipped to.

So there's more to think about, more to think about. Exactly. Well, this has been very enlightening, very educational. Thanks a lot for taking the time to, um, come on the show today. I appreciate it. Glenn, great to see you again, man. I hope you can join us more often now.

I will try.

Glad that you're doing much better. All right, guys, have a great weekend. Thank you very much.

You too. Thank you.

Thank you. Bye-bye.