Shaker Unfiltered: Ep.2 – Chris Cicmanec

think we will have better connected data, right? That’s something we’re working on very hard and things will right now it is a lot of human lift to connect A to B to C and be able to tell that story. I think we’re gonna have just this holistic story being able to be told through the data without assumptions without manual interventions without um you know tweaks being made to the data or gaps being there. I think you know like like we talked about let’s start all the way at the and 3 months ahead of before the peak season we started with an awareness campaign all the way to those last minute hard to fill roles that we needed to get filled on you know the Wednesday before Black Friday right type thing and we’ll be able to to connect those dots so much more seamlessly and just look at it holistically and really understand not just we did this last week so this happened but we did this 6 months ago and this happened. >> This is Shaker Unfiltered where we break down the full world of recruitment marketing from talent attraction and employer branding to martekch and analytics. Join us for real stories, bold ideas, and practical insights that help you stay ahead and take your talent strategy further. Hello there and welcome back to Shaker Unfiltered. I’m your host, John Graham, VP of innovation, inclusion, and growth at Shaker Recruitment Marketing. If you’re returning, welcome back. Thanks for joining us again. Uh and if you’re new here, welcome. We’re glad you’re here. Pull up a seat to the table, make yourself at home. Uh this is the podcast where relationships matter is more than just a phrase. It’s how we connect the dots between our people, our clients, our partners, and the industry voices shaping the future. Each episode, we cover the biggest topics in recruitment marketing, from employer branding and talent attraction to martekch and analytics, so you can take these things away as practical insights that’ll help move your strategies forward. Before we dive into today’s episodes, I want you to take a moment and hit the subscribe button so you don’t miss an episode. Subscribing helps you stay ahead with fresh ideas from industry leaders and it also helps us grow this community of forwardthinking talent leaders. Today’s conversation, we’re going to go from data to decisions and we’re going to explore the next frontier in talent analytics and I have a very special guest to bring us uh through all of the ins and outs of this space. Today we’re joined by Chris Sismanic who’s the vice president of analytics at Shaker Recruitment Marketing. Chris brings more than a decade of experience in recruitment marketing following an earlier career uh on the consumer side where he partnered with major brands like USPS, State Farm, and Sprint. Over the years, he’s worn many hats across uh strategy, media, and content. But one thread has remained constant. His commitment to using data to uncover insights and guide decisions. Chris’s perspective bridges both consumer and recruitment marketing, and his leadership and analytics is helping shape how organizations understand and connect with talent. Today, I’d love for you to give a warm welcome to our friend, my friend, your friend now, Chris Sisman. Chris, welcome to the show. >> Hi, John. Thanks for having me. Hey man, absolutely. Usually Chris and I uh are are somewhere in a corner while he’s doing origami, which you’re probably going to see him do during this episode. Uh the professor, as some call him. Uh the origami is legendary, but Chris is uh one of the people at Shaker that I love talking to because of his perspective uh on how important analytics are and the data. not just the data but the insights that help drive uh our clients forward in their talent strategy. So Chris, I read your background. People got that high level, but you’ve had a career uh that has spanned both um the consumer industry and the recruitment marketing space. I’d love to get right into it, man, and just see from your perspective how how have both of these sides of the fence, as it were, shaped your approach to analytics and talent attraction today? I mean, it sounds a little harsh, but I’ll be frank in saying that recruitment marketing tends to actually lag a little bit behind the consumer space, right? We’re always playing catch-up and then the rules around privacy and regulations um add that much more, right? Like above the line, so to speak, marketing is um far more powerful. You know the example here I just read an article about um how the I forget who it was from Instagram came out and said no we’re not listening to your microphone right we are absolutely not listening to your microphone we and it’s true they’re not right we would notice that people would catch that and it would be caught but we really do operate in such a ways that data is able to get us they’re like oh look there’s a married 30 a 35year-old who lives in Chicagoland Oak Park and there’s a bunch of them talking about this. We’re going to make sure we get this in front of him specifically or her specifically and it’s the power of the analytics and the level of data being collected that makes things like that possible and recruitment is getting there. Um but you know it also rightfully so strong limitations on being able to use demographic data. It’s we’ve seen again and again that it can be abused and will be abused by bad actors. So let’s just take it out of the equation and make sure that that we keep a playing field level. And aesides from that >> I know at least I get very irritated when a Microsoft project when a Microsoft product tries to make decisions for me and we’re doing a little bit the same thing for candidates if we really were to be focusing down their opportunities what we thought were right for them. Those are the kind of decisions people need to be able to make on their own. That’s so you’ve started with a lot of hot button issues that I would love to dive deeper into, but you bring up a really good point. And the first one I would love to start at is I think you’re absolutely right and funny enough Instagram saying we’re not listening uh to your technically they’re not Apple or Samsung is and they just happen to be beneficiaries of what’s heard through your devices. But fine, wink wink, we’re not listening. All right. But but even right even if we take that at face value there is a ton of data being collected on us that we are giving away and when we hit terms of service acceptance and all that stuff fine but I wonder even in the talent space right and and you stated that we’re lagging behind which I couldn’t agree more with but why is that is it that we don’t have the data on talent as talent as candidates or is it that we don’t really connect consumer data which is available um to them as candidates uh for employment. I think first and foremost it’s just the money the budgets aren’t there right I mean when I don’t know 20 years ago 15 years ago when I worked at foot cone and building and we won the Walmart business for all of a week I won’t go into that story um it was like $600 million of media spend for a year [laughter] of course there’s a lot of opportunity to spend on building different models and building different data warehouses to store any level of data if you’re spending that much money and you know so um I I think first and foremost that’s the big difference the budgets just don’t align the other part is you just don’t have as much data people shop for a coke or a car or new shoes every day they look for a job you know they might have a year-long period depending on the state of the economy where they’re looking but then hopefully they’re not looking again in three months right so um I think that’s part of it too and >> careers change I mean I yes I’ve worked in advertising my whole life but I have been a creative I have been in IT I have been um on the strategy side and then the media side now I’m on the analytic sides >> truly end to end so so I I got to believe though and and I know this to be true. I’m leading the witness, y’all. Um that brings a very unique perspective to what you do day in day out. So, I’d love for you to just share with the with the viewers uh a little bit about your secret sauce and how you’re approaching talent analytics um knowing what you’ve you know knowing what you’ve acquired in knowledge from these other areas of of your professions. Well, I I I would love to say I have some secret sauce or special sauce. I don’t know about that. But I think the important things to remember are you got to ask the right questions, right? And the right questions are what are the client’s goals, right? What are we that’s the first and foremost that we need to ask. And then the next question to ask is what questions are the clients going to ask, right? What questions are they going to ask? what questions are they going to be asked so that we can really start equipping them with the answers um databacked answers as well to really help make more powerful and quicker decisions. >> Yeah, I I love that you brought that up um in our previous episode uh with Susan Lamont. She she was very dialed into this notion that data doesn’t help make decisions, insights do, right? the data is abundant but unless you parse it out and really bring bring it together in some you know digestible way that’s a valuable insight. So and and I think you mentioned that as well is helping our clients make decisions. So with that being stated, there’s no shortage of data that’s coming in and you know whether it’s you plugging into ATS’s uh getting feeds from you know this platform, that platform, whatever the case may be, but still you’ve got to like dig into all that. You and your team have to go through all of that data and then make sense of it. Where do you see the I’ll say maybe the um the the process slowing down in terms of making decisions for TA leaders even with the insights. What do you think that is? Because some will say we don’t have enough data. Well, we have enough data. Some will say we don’t have the insights. We’ve given you insights. Where where is there still a disconnect for you? >> Um data cleanliness is a huge issue. Um being able to get it right. like sure I have an API that will connect to a ATS and I can pull all the jobs. I can pull all the um application dispositions. Nothing about the candidates but the application dispositions, but everyone does it slightly different. It’s not like when you’re on workday, you do it the workday way up to a point, but there’s lots of variation in there. if only because you might be a national and or international company. So you have regions and divisions and you need to be able to split things out and you have multiple languages um versus you know some a health care system that’s more regionalized. Still tons of roles and lots of activity but they aren’t necessarily hiring everywhere. And it’s just trying to correlate make sure that you understand how their data is being made available is what it really comes down to. You can’t say, “All right, we have a one-sizefits-all solution.” It’s never that way, right? Like uh everything kind of has to be bespoke because the data coming in tends to be customized and very specific to that particular client. And >> yeah, >> when you think about e-commerce data, that’s not necessarily true, right? That going back to your earlier question about why are we lagging behind it? It’s because there is there’s so much variation that um you have to stop and think and then there’s the technical limitations as well, right? Like some clients, I am able to get pixels on their ATS’s. So I’m able to confirm that a conversion took place all the way through a completed application, right? And that’s a more immediate decision that gives us data the the media platforms in which to make decisions right then and there, right? If somebody completed the application versus having to wait to get that ATS application and then find out if they were quality and find out if they were hired. Um, but some ATS’s don’t allow that. And so then we’re looking at an application start and it’s just it’s never a onetoone is what it is. Everything is um bespoke and just kind of um unique to that particular data set. >> Yeah, it it is maddening in the sense that we’re, you know, in 2025 and there’s still not this, you know, what’s the word? um I want to say panacea but you know this this onesizefits-all solution that can tell uh that can tell you from end to end when candidate saw x y and z promotion uh or advertisement or job clicked here went here apply like there’s no like linear tracking per se right you get like bits and pieces of it but to your point there’s so many connection points how do you make sense of all that knowing right that a client is coming to to us and saying, “Hey, we want to be able to not only track uh these these specific actions, but then make sense of where our spend is making the most impact and then make decisions to optimize, change, pull, push, whatever. How do you how do you make sense of that knowing that there’s so many tech stacks? >> Then there’s so many there’s all these inputs. How do how do you make sense of it all?” First and foremost, it’s making sure we understand the tech stacks, right? Everything that is available to us and how it’s functioning. I was just earlier today having a conversation about our clients that we’re going to have to report conversions about apply starts versus apply completes, right? And sure, we’re going to make a dashboard that talks about conversions, but you know, I can make a dashboard that, you know, in Facebook, you can have 40 different conversions being tracked. If I made a dashboard that showed each one of those potential conversions, 98% of them would be blank. Um, and then they’d just be be meaningless. So, you really need to be able to focus the data. And the thing trick is to understand what it is and what’s important to the clients and what’s important to the the actors who are managing um the day-to-day spend and strategy so that you can really enable them to make more fluid decisions. >> So knowing all of that then you have the the data cleanliness challenge right. So what I what I would love for you to do in this moment is we dream together out loud. Chris, what what is an example of like an ideal client setup where you can get clean in easy flow, great analytics, and then insights provided. And I know we have examples of that, but we can leave clients nameless, but just give me give them give the people an example of what like the ideal state would look like. I think it would be you have a career site that allows the placement of pixels and the um and activation of uh site reporting um the G Google Analytics 4 Adobe I’m much more versed in G4 so I’m much more comfortable that I’m going to to talk about use G4 as the site tracking data right so you have that level of data and that gives you something. And then an ATS that also allowed GA4 and the placement of pixels so that you can actually start to see that connection between campaign GA will recognize a campaign coming in. It will track it. It will log it. Uh if you you can actually connect your GA4 data set so that then you can actually see that campaign data come through. Right now lots of times when we’re trying to track it’s we have to go from G4 parameters such as UTMs and then convert to ATS parameters like source. There’s just always these points of failure. So, we have to be able to say, “All right, we’re running a campaign. We’re going to tag it with a UTM_ource, you know, Shaker Media. And by the way, when they click on apply now on the career site and get taken to the ATS, that needs to be converted to [clears throat] Google search, right? Type a more specific name that the ATS itself can recognize. Um, and then so that’s part of it, right? Like consistency about what’s allowed and not allowed. And this is dreamy. I don’t even want to say we’re dreaming. feel like I’m calling out platforms and it’s not even their fault, right? They they were built for different things in mind. The same way we talk about how we build things to do accomplish a certain thing. So, but in a perfect world, we’d be able to have site data and um pixels on both the career site and the ATS. We would also then be able to get consistent reporting out of them so that you know like you can easily say in Google you can name whatever um pixel conversions you’re tracking whatever you want on Facebook you have a bunch of canned ones you’re supposed to use. Oh but there are some custom ones you can use too. Instagram and Tik Tok do it a little bit different. It’s just there’s a lot of variation. Now I feel like I’m just whining but >> I’m here for it. This is we can make this a therapy session. I will be I will be your therapist here. >> I might need it. Um >> no [laughter] >> and then the only thing I’ll add is >> please >> um how you look at the end state of the data too, right? So even from the here’s another thing. So an ATS is not the end all beall. We don’t necessarily record hires in the ATS. Somebody has made an offer and they’ve accepted. So they’ve been marked as hired and they’re supposed to start in two weeks and then they’ll be in the HIS system, right? and but maybe they don’t show up. >> So, [laughter] right, >> that does happen. >> That’s less likely in today’s marketplace, but three years ago it was really common. People would get another job and be like, “Yeah, oh well.” And so, we’d be counting something as a hire >> and they wouldn’t show up, so they wouldn’t be hired. And it was a huge problem because so the like making sure that there’s this interconnected pathway of what are people engaging with on the career site, what are they engaging with on the ATS and then are they actually engaging with the employer in the required way. Um yeah [clears throat] would be kind like a perfectly clean way to execute that would be very nice. >> The way you describe it sounds so dreamy like it sounds amazing. What if we could just do that? >> But but I think you you’ve pointed out that there are so many choke points or points of failure as you said. Yeah. Because there’s so many platforms and it’s not to your point, it’s not the platform’s fault like we use these things in different manners, but nobody built their platforms thinking about the connectivity necessary for clean you know talent analytics. So I get that >> workday is a great example. Workday is a comprehensive system for all aspects of a business and the ATS is just a tiny little part. Um, so they’re not really fond of adding tracking to that because they just want to keep workday working. They don’t really care about what’s happening on that ATS part. And again, I’m speaking for workday, so I’m being unfair, but >> to my observer eye, that’s what it looks like. So >> Sure. Sure. Yeah. And we have no bias against any of the platforms. It is what it is. Um, I I I I feel like in postedit I want to go back and take the section of when you took everybody through like the ideal dream state and have one of those like high school uh health uh you know uh inductions. Let’s talk about your ATS [laughter] somebody pointing at the chalkboard. That was fantastic. Um yeah so so let’s talk about like um we we know the reasons why this is so challenging and I think some people you know I’m sure you can attest to this some clients come to us and you know they expect it to be a very easy process right we want clean analytics I heard I heard you mention pixel placement as like a prime like success driver but I know and I’ve sat on enough calls with with you and clients that you can’t just put all the pixels you want on there. Why Why is there a limitation on that knowing that this is probably the best way to get clean data and visibility into the process? >> It’s probably more about security than anything else. Um, like one of the bigger issues is image pixels versus JavaScript pixels. Workday, I feel like I’m picking on workday. Most of our clients are on workday gorilla. I mean a lot >> workday does not support JavaScript. Image pixels are very old technology and they pass a limited amount of information back to the platform. JavaScript will do more. You can get down to the specific job ID. So it’s not just hey somebody completed this particular action. They’ll actually say somebody completed this action on this job. Right? So that you get a little more clarity on it. Workday doesn’t allow that. So it limits the clarity of the data that we can pass through. Now, we kind of know that if candidate A came through on an ad for job A and on the career site they were on job A and they clicked apply now on apply A on job A, then they and then they completed an application. Yeah. Okay. Then they probably completed an application for that job 98% of the time. That’s true. [laughter] Um, >> yeah, that’s that’s a small margin of error. I’ll take it. >> But, you know, [laughter] we would love it to be 100% true all the time is what it really gets down to. >> Yeah. >> So, that’s where pixels are important. Pixels are also much more real time because they give the platform and the people managing the the spend on the platform the information to make decisions right away, right? Like so we have media a we just spent $100 for them today and I had one um application uh one uh I had a 100 clicks but only one completed apply. So I have a a cost per click of a dollar and a CPA of $100. Then I have another media that their cost per click was $20 each. Um and I only got five but I got three applications. Where should to tomorrow, where should I spend my money? I should probably spend it on the $20 cost per clicks because it got me three times as many applications at a much more affordable price. So, that’s where the pixels really come in handy. Now, the ATS data lets us see the quality of those people, right? Like, if you’re advertising a nurse job and you’re having truck drivers apply, um, that something’s wrong there. that application doesn’t matter what you paid for it does you no good and that’s why you need to continue to look deeper too but that takes time to kind of process right just because somebody applied doesn’t mean they got reviewed doesn’t mean they got interviewed you want to be able to take time the media pixels give us that instantaneous feedback >> to start making decisions and start getting better >> yeah well I think it’s only a problem if you hire the truck driver for a nurse role like that’s that’s more problematic in my mind. But you but you’re right and and you mentioned CPA, right? And I think I saw uh our friends over at Abcast just put out some data showcasing that the cost of CPA has gone up like 27% this year alone. What do you what’s driving that in your mind? Like why is it costing more to get these applicants up? I think there’s less jobs out there, right, overall being advertised and um there are less people looking for jobs than were a couple years ago, right? Like we keep saying, “Oh, it went up over last year.” Well, you know what? The two years before this one were banner years, right? And last year was nothing compared to the year before that. So trying to say year-over-year, that’s one reason, right? There were so many people out there looking for jobs um that CPAs went down. That was that wasn’t the period during COVID where there weren’t enough people. This was the period when everyone’s like, “Oh, I’m going to go find a new job and I have I’m just going to switch jobs every six months.” So that part of it’s the marketplace in that sense. Um, but I also think that lots of platforms are getting media platforms are getting better about targeting overall. Indeed’s making huge changes and I think that’s going to make a huge difference. Um, and it will reduce the number of applications, but they’re going to expect to be compensated for driving those more qualified applications. Rightfully so, right? that uh there’s a limited amount of those applications of qualified applicants and it’s kind of a resource that Indeed controls I wouldn’t say owns but controls and so they’re going to make sure that they’re maximizing their earnings off of those um qualified applications and also they’re trying to make sure that those qualified applications continue to come through them. Um, you shouldn’t have to go apply for 20 jobs, right? Um, you should be able to go find the job that’s the right fit for you and maybe you’ll do a few, but you know, you should be able to be directed to the job that’s right for you, which is of course countermanding exactly what I said at the start when I’m like, I hate it when the platforms make decisions for me, but >> well, >> levels it out. Yeah, >> that’s a fantastic segue into the uh obligatory AI conversation, [laughter] >> which I have I I don’t know. You can tell me I’m crazy, but I think what you’re saying I think as as you’re saying it, why aren’t the right jobs coming to find me if I’m an applicant, if I’m a candidate? With all this data, why do I even have to look for a job at this point? I mean, I know we’re not there yet, and that kind of sounds like gat, right? But >> I think we’re on the cusp, right? I I think all the um especially job boards are really starting to build out that information, right? Like Indeed apply is the answer to that. I hate to say they have company millions of people and they have your resume and they know where you live and they know how many years of experience you have. So, they’re going to be able to say, “Oh, look, John is an employing brand expert who lives in Texas. Um, and we just happened to see that so and so is looking for this. We should probably make sure and we know he’s looking. So, let’s make sure this gets in front of John when he’s logged in.” And, you know, I think, you know, you would uh then click on that job if you were looking, but you’re not because Joe would be very unhappy about that. I am firmly secure in where I am. Shaker all day. Let’s go. Yes, you’re absolutely right. Um but that but that still would require me to be on their platform. I don’t know. I’m I’m kind of like envisioning this future where you just get an email one day and you know, yes, you lost your job or maybe downsized or you or you chose to leave and then you get an email and says you start at this this company in two weeks. Congratulations. I don’t know. Like I know there’s a lot in between now and then, [laughter] but I could see it happening mainly because our um so much of our lives, our professional lives, uh and our personal lives are online to be, you know, analyzed and predicted and all these other things. And now you throw in the mix of a AI. Anyway, >> there was a great short story by Bruce Sterling, sci-fi writer I read back in the 80s or 90s. I don’t remember. I’m very old. And um [laughter] >> not that old. >> It was it was supposed to be >> this little story about how humans had kind of evolved and AI was making our decisions for us. So, one day a guy got sent tasked by his AI, “Oh, go deliver this jar of pickles to the park.” and he got there and he found an upset pregnant woman and she wanted she she’s like, “Oh, I did want pickles. Thank you.” And you know, maybe someday we’ll be in a world where there’s some benevolent people helping us like forecast everything, >> but um I think that’s a long ways away. >> If somebody said, “Oh, by the way, now >> here’s your new job, Chris. You start in two weeks.” I’d be like, “WTF?” >> Yeah. [laughter] Listen, that’s why I said there’s a long way to go before we get there. But what you just described about the jar of pickles being delivered to a pregnant woman in the park who wants pickles. Like Amazon does this now, you know? Like there’s [laughter] shows up to my door. I’m like, damn, how did they know? Of course, I ordered it, but they put it in my car to whatever. We’re close. We’re close. I think there’s some regulation that would need to be >> Maybe Alexa was listening. >> That’s what I’m saying. That’s what I’m saying. So, it’s not far off. They’re there. It’s It’s coming. and and everybody can say you heard it here first on Shaker Unfiltered. John called it, Chris confirmed it. >> Uh I want to talk about optimization. Um because this is this is a big thing that you do and I think some of the value that uh a lot of the value that we drive uh your team specifically in helping our clients spend their their dollars better when it comes to job distribution and programmatic. I would love for you to sort of talk a bit about that and what you’ve seen uh in terms of the evolution of optimization and where we are uh today. I would say that you know really this all started with programmatic right programmatic jobs was the first major optimization we were able to make once upon a time it used to be like hey it’s almost it’s Q4 what are we going to spend next year on platform A platform B platform C we got to go sign the contracts and lock ourselves in it was really fairly detrimental um to being able to effectively use your money. Um, so and this is old hat, right? Programmatics old hat, but I’m just saying that ability to allow the flexibility of your budget to move where it’s doing best at that moment makes a ton of sense, right? Like example, um, you know, in Q4 you’re going to have to spend a lot on retail jobs, but in Q1 you’re going to spend a lot on corporate jobs. do, but you you would have to go lock in contracts for the job board that like again I don’t like dropping names. LinkedIn, you’d have to lock in for that Q1 need and then snag a job for Q4, but it was for the whole year still. So, it was very tricky. I think that’s something that’s made a huge difference that being able to move your dollars to where you are going to be able to derive the most benefit from them I is huge. Um the next thing that I would say about optimizing is being able to get that feedback, right? Like once upon a time digital media was, hey, we had a million impressions and you had so many clicks and that’s still better than we ever got off of a billboard. But that’s not enough. Now we want to now we have pixels and now we can know how many people clicked and then completed an event after that. We have site tracking software that will tell us which content’s being consumed, right? So we have a lot more factors in there that can help us decide. And then finally, it’s that um well, there’s two stages actually. I would say the next stage then is just continuing to move towards the bottom of the funnel, right? Like it’s great to know that somebody applied, but in that same example I gave, if you’re paying a lot for if you’re paying very little for CPCs, but a lot for the CPA and you’re paying even more for the the hire, then um maybe that’s not the most effective place to be spending your money. So, being able to see that much to the bottom of the funnel um makes a huge difference. I I tell this to everybody. I would love to get post hire data and see if there’s a variation in candidates from different sources, right? Does is the tenure dramatically different for a candidate who came from media A versus media B? Because let’s be honest, we’re talking about higher and lower cost per hires and cost per applications and the media spend, but that’s just a fraction of the cost it actually takes to hire somebody between the um the expense of ramping up to hire somebody, the manager’s time to do all the interviews, the recruiter’s time to do all the screening, the training time to get somebody up to speed, let alone the downtime when you don’t have that employee doing their job, so it’s not being efficiently done. Those are all things that I’d love to be able to start digging into too to really understand how it impacts the business as a whole. >> Yeah. >> Um I’m getting off track. Um optimization. >> Wow, this is good. This is rich. >> The the final thing I will say about optimization then is um and this one is still tricky. Just tools out there like double click. It’s attribution, right? Did my display ad that nobody clicked on when I ran it for three months have an impact on building awareness? And I think that’s huge. Um, and I think things like doubleclick and Google Analytics up to a point will help you with attribution. It’s just it’s so imperfect and the numbers never align with the ATS data that bottom of the button. Like you might be able to say, “Oh, double, you know, you’re always going to see a delta of like 10 to 20% every month, one way or the other.” One month, Double Click’s going to say, “Hey, you got this many hires because of us.” The next month, and the ATS is going to say, “No, no, it was only this.” And the next month, you’re going to say, “The ATS says there were this many.” And double click like, “Oh, we think we only got that.” In order, we focus on ATS information because we see it as a single source of truth. I very much feel that we need to move towards attribution um being brought into place so that you can understand those longer term spends as well, right? Like if you r and we’ve seen this anecdotally versus markets with spend versus markets without spend and you can see that in the test we did for one of our clients we did an awareness campaign for three months um in marketplace A which was very similar to marketplace B but we didn’t run that awareness campaign before that they had very similar statistics x number of applications per month standard cost per CPA a that aligned once we ran that awareness campaign in marketplace A, there was a huge decrease in the cost. So yeah, you spend a bunch of money. I shouldn’t even say a bunch. You spend some money on awareness campaign that has no results directly attributed back to it, but you see a reduction in your cost going ahead, especially if you can time it right. So that you know your peak season’s Q4 for most retailers. You started in Q3 so that people are already thinking when it’s time to look. They’re like, “Oh yeah, I’ve heard of that brand. I’m going to apply there.” Not, “I’m going to go to the brands I know.” Make sure that your brand is the one they know. >> Yeah. That that’s that’s fascinating. And and I think there’s a conversation that often isn’t had between sort of the the power of branding and connecting that to the outputs of programmatic job distribution. Where do you see the connectivity? Because oftent times, right, that that ad does have a subconscious uh effect, right? Whether they engaged with that ad or not, but seeing it creates recency and uh recall at the time, whatever. >> Where where do you see the the obvious, but then also the not so obvious uh in impacts or uh connectivity between, let’s say, programmatic and employer branding itself? >> I think it’s trying to think of the best way to put this. the I it’s really in the volume of activity, right? So, if I run an awareness campaign and I think of an awareness campaign, here’s the real reason to run an awareness campaign. You’re in a marketplace with a tight labor pool, right? and like and you can pull um talent neuron reports and LinkedIn talent connect reports and stuff like that that will let you know how many possible candidates are available in a particular area. If you’re in a very tight area, what you need to do is expand your pool of people that you might be talking to. If you’re advertising for a particular like warehouse job, those people are getting messaged to already. So maybe you need to reach out to people who hadn’t thought about a warehouse job. Maybe there’s people who are ice cream clerks or um doormen or you know part-time college students and you just need to get you need to get them the brand in front of them so it’s added to their consideration set so that when they do get served something they’re willing to actually connect the dots and say, “Oh yeah, I should do that.” >> And to your point, the employer brand is so much more than just company XYZ is hiring. It’s company XYZ is hiring and they do cool stuff and they are have a fair wage and they’re growing and that’s why I should apply there. So >> absolutely it. So one of the things that we always uh are chasing as the holy grail from an employer branding side was you know that ROI connective tissue between the employer brand campaign awareness campaign as it were the storytelling and tying it back to hire. Now we have our thoughts in that community about whether we should even be measured by uh hiring outcome versus right versus awareness and consideration and so forth. But do you see I mean maybe you’re seeing something that that the rest of us aren’t when we talk about ROI of these campaigns and how do we explain that as far as connecting it to hiring outcomes or can we >> I think we can up to a point right but what you’re executing at the employer brand level I think should be measured across multiple things to gauge the effectiveness as well right like especially things like it’s not just Did we make more hires? Where do those hires come from? Are there more employee referrals? Are there is there increased tenure? Are um there particular neighborhoods that people are coming from that, you know, might be making a difference and people are starting to understand that there’s an opportunity here. Those are the really getting down to it. And now we’re starting to talk demographics, which we shouldn’t, but you know it. [laughter] >> No, of course. Look, people people are hiring people still. So, there’s a demographic component to that. But I I understand what your point I understand the point you’re making and it is [clears throat] I I don’t know that we’ll ever get I think you brought up attribution earlier, right? And that that would be the blue sky dream state if we were able to truly attribute uh not only how you got to the to the career site or to the job, but then also what was the driving influencer um you know and it could be many. It could have been you know a conversation you had at a cocktail party, let alone you know a creative ad that the company ran on LinkedIn. So >> yeah, >> maybe that’s not worth trying to solve right now. So I I do want to get us back to AI um because it is a especially when we talk predictive analytics I’ll I’ll I’ll zoom in the conversation on predictive analytics and I’d love your take and you know especially over the last two to three years um there’s been this massive boom uh not only in the conversation but also the capabilities um how are you and your team bringing in AI to uh to not only get insights faster but deeper insight ites that maybe otherwise you wouldn’t have been able to collect before. >> We’ll admit that I am not 100% an AI convert and maybe it means I’m a bit of a lite. I do feel that um >> give me time, Chris. Give me time. I I got you, buddy. I got you. [laughter] >> Um I I I worry that it doesn’t have the depth. It just it’s we haven’t taken the time to train the models properly is what it really comes down to is what we would have to do. we’d have to have the the right inputs to train them. I look at AI as an amazing tool for adding value if only to clean data. So, for instance, we’re talking about all of our jobs um that we that we that come into the programmatic platform. They come in a multitude of ways. Um they’re not always right. um human air. There’s plenty of jobs. There’s plenty of people creating those jobs on the ATS platforms that we’re getting the data from. So, I don’t want I’m not saying I’m calling people out, but that’s the kind of thing where I really see AI being powerful in being able to say, “Oh, look, you have um Seattle, Oregon, and a Washington state zip code here.” Really, you meant Seattle, Washington, and this zip code, right? We can’t correct for that except for one by one going through them. AI can go through and find that stuff and fix it and just make sure. It’d be really be a shame if you thought, “Oh, I want a job in Oregon.” And you get one. It just makes everyone look bad. Like, I I’m searching for a job in Oregon and now this is in Seattle, Washington. What What are they doing? I know they’re right on the border. probably a bad example, but [laughter] you said like Oregon, Maine and it was like, you know, but no, I meant [laughter] >> Portland. >> Yeah. Yeah. No, that’s Yeah. No, that’s fair. I like that. It is better at cleaning data for sure. >> And then >> mixing and matching the data too is very hard um for us, right? But we it AI is much more capable of being able to go in and correct things. For instance, job titles, you know, somebody advertises for a registered nurse, somebody register advertises for a registered nurse overnight, third shift, you all these variations. AI is very good at going in and say, “Oh, this is a registered nurse. We’re going to bucket those all into here.” And that then gives us the ability to say registered nurses at a at a much more refined way. Registered nurses cost an average CPA of X in Y place as opposed to getting some of them because we didn’t know that a registered nurse six exclamation points was also a registered nurse. Silly example, but you know what? >> Yeah. No, naming conventions matter for sure. Yeah, I think that would probably be uh an ideal um innovation on the ATS itself when the jobs are being put into the system cleaned up and that way you’re getting clean out. So, >> some of them have some of them don’t >> some of them are doing it. >> Yeah, >> imagine that. >> Yeah. [laughter] I also wanted it kind of done at a >> um national level. There are things like um SOC codes and a NAIC codes from the Bureau of Labor Statistics. Um I’d like to get it down to that level so that we can really speak about industry specific things. Um and AI is going to help us do that. And then the final thing I’ll say I think of AI being very helpful for is just monitoring, right? So AI, you’re going to watch this for me and let me know when this happens as opposed to that’s where the humans fall down. I think humans still do a better job in an analyzing and pulling insights out. Um but the the dayto-day the boring stuff um that’s where I think AI is truly going to be powerful and making >> Absolutely. >> Yeah. I love it. Hey, look, may that vision come to life. Speaking of vision, uh I want to be respectful of time, my friend. You have already given us a lot to think about. Um you know, and as Shaker, we are big on vision. Um shouts out to uh the vision lab. Um I’d love for you to take us five years into the future, Chris, and let us know what the analytics function at Shaker looks like. Uh and how will we have reshaped uh the brands that we engage with as a result? I would say, how is my job gonna look different in five years? Um, I’ll be retired. No. Um, >> no, Joe, he’ll still be here working diligently as always. We’ll we’ll scrub that in the post edit. Don’t worry. >> Thank you. Um, [laughter] I think we will have better connected data, right? That’s something we’re working on very hard and things will right now it is a lot of human lift to connect A to B to C and be able to tell that story. I think we’re going to have just naturally this holistic story being able to be told through the data without assumptions, without manual interventions, without um us, you know, tweaks being made to the data or gaps being there. I think you know like like we talked about, let’s start all the way at the and three months ahead of before the peak season. We started with an awareness campaign all the way to those last minute hard to fill roles that we needed to get filled on, you know, the Wednesday before Black Friday, right? Type thing. And we’ll be able to to connect those dots so much more seamlessly and just look at it holistically and really understand not just we did this last week, so this happened, but we did this six months ago and this happened. M folks, [snorts] you’re hearing the dreams of a man desperate for technology to catch up with his visions. I’m telling you, this is gold. Chris, I I want to thank you so much for for taking some time out. Um really appreciate your insights and thank you for stopping by. >> Great to connect with you, John. Great to uh be asked some uh some very uh thoughtful questions. Thank you. >> My pleasure. episode. Thanks again. >> Take care. >> All right, folks. That wraps up today’s episode of Shaker Unfiltered. I want to thank Chris so much for joining uh joining me uh joining us, sharing his insights, perspective, and vision for the future. Uh it’s conversations like this that remind us why relationships uh not only matter, but they’re so important. um but even more so how data and creativity and strategy work together to move uh the industries forward. Uh if you found this episode valuable, here’s a few things that you can do. They’re quick, they’re easy. First and foremost, subscribe to the podcast so you never miss an episode. I promise we’ve got some really great conversations to add on to the ones that you’ve already seen uh coming to you soon. Next, just share this conversation with a colleague or a friend uh in the industry who you think would benefit from hearing it. And lastly, connect with us at Shaker Recruitment Marketing if you are ready to take your employer brand, your talent attraction, or your analytic strategies to the next level. You can find all of the resources, what we do, and how we can add value to your journey uh in this space at shaker.com. Um, and so we’ll also have the link in the description to make it easy. Uh, but I just want to thank you again for listening. Um, this has been another episode of Shaker Unfiltered. And until next time, keep building, keep innovating, and keep putting relationships first to bring the vision to life. We’ll see you soon.