Precision at Scale: How to Maximize Demand Generation ROI with Clemens Deimann

Hi, I'm Michael Hartman. I'm Naomi Liu. And I'm Mike Rizzo. And this is OpsCast, a podcast for marketing ops pros and rev ops pros created by the MoPros, the number one community for marketing operations professionals.

Tune in to each episode as we chat with real professionals to help elevate you in your marketing operations career.

Hello everyone, welcome to another episode of OpsCast brought to you by marketingops.com, powered by all the MoPros out there. I am your host, Flying Solo again, Michael Hartmann. And once again, I will say I look forward to having Naomi and Mike rejoin probably as we get closer into 2025. It's this, this may be the last, one of the last ones we're recording in 2024. So looking forward to celebrating our 4-year anniversary in early January 2025. So joining me today is Clemens Dimon, an AI growth consultant at Elgo Marketing. Prior to his role at Elgo Marketing, Clemens Has held various marketing leadership and demand generation roles at several companies, and he started his career as a foreign language teacher and performance coach, which I don't know that we'll have time to get into that, but I might have to follow up with him just one-on-one about that. So Clemens, thank you for joining me today.

Thank you, Michael. Thank you for having me.

And so for our listeners, he is staying up late on a Friday to do this. So I appreciate that, Clemens. All right. So you have worked in marketing and demand gen for many years, and you told me that repeating what worked in prior years usually does not work long-term, which is very much aligned with what I've said before, that the term best practices is, well, I hate, I just hate that term because it like, it implies that there is a way you could just, you know, cookie cutter replace, copy and paste and repeat what you did somewhere else. But When you say that, what does it mean to you?

Yeah, that's a great point, Michael. And yeah, I just a bit about, yeah, my background. I worked in smaller scale-ups starting from 100 people and also large enterprises, had the opportunity to work for LinkedIn and Google. And what I noticed is that the marketing planning process is very much similar, no matter if you work for a small scale-up or a large enterprise. And the way it works is that normally marketers look at the performance from last year and there is a knock-on effect for how marketers plan to operations, which I'll get to in a minute. So marketers look at the past year and see what works leveraging historical analysis. And then they go ahead and plan their campaigns for the following year and their channels. And the challenge they face very normally is that the targets tend to increase, not decrease year on year, of course. And what worked last year, even though if you repeat it and double down on it, it might not get you to the targets in the upcoming year. So there always tends to be that gap in the planning process that marketers need to make up with by placing gaps— bets, sorry, placing strategic bets on and hoping that they will pay off and that these strategic bets or campaigns that are strategic bets get them to their targets. So there is an element of hope involved in the intuition involved.

Yeah.

And intuition is great, but if you're relying on hope, then that can be a business risk as well. And what that means then over the course of the year is that, yeah, if the targets then are not attained or the pipeline, the marketing pipeline is falling short of expectations, that marketers just keep on adding new campaigns to make up for the gap in revenue or performance or pipeline performance. And that really puts then marketing operations teams under pressure because marketing operations teams start scrambling with all the additional ad hoc campaigns.

Yeah. And I mean, my experience is when you get that like high volume pressure, but with high volume, it's like, hey, we need to just generate a massive amount more leads or whatever it is that your goal is, but also the increased volume of activity. is when things start to break down. So if you're really— the discipline about tracking what works and not work— doesn't work, right? Kind of goes by the wayside and often gets missed as well. The one thing you said is that, you know, these marketers tend to look at past performance, and I think that's true to some degree, but I don't think in a lot of places that actually happens. So like I've been working at places where, well, either that or they don't look at, here's one of the challenges I see is like the performance is really sort of a basket of metrics, right? And there's not, I don't think there's a single metric. And so if you're not looking at that basket of metrics to see what's performing, you know, in terms of each different sort of, I don't wanna even say dimension, but even maybe different components of a customer journey, then I think you're gonna potentially make decisions based on, you know, incomplete data. And I've said many times, right, I don't believe it's quote right or wrong data. It's just incomplete. But I think recognizing that, so I, what I've found is it, a lot of people just don't, they actually don't rely on past data. Have you seen that too? Or am I, is it just my like bad choices of career stops? I don't—

Yeah, it's definitely true. I think the historic challenge really is that if you want to make data-driven decisions, which of course is better than not making, or let's say you increase your chances of success if you make data-driven decisions, historically speaking, and I'm speaking kind of up to AI, possibilities that AI provides to us. So, before we had those possibilities, which we'll go into in a second, I believe, all you had really is historical data to make assumptions on and to inform your decision-making. Of course, there is also the other model of skipping that step, not looking at the data, And just trying to do more and more faster. And I think, yeah, what you mentioned is what data points to look at. Of course, if you're only fixating on pipeline, which tends to happen under pressure, you forget about the other metrics. So of course, what happens is if you keep on adding a lot of ad hoc campaigns, then the execution gets scrappier. You expand your target audience, which within your database, you get less engagement to your campaigns and the database is becoming less responsive, which of course hurts you because you accelerate database decay and that creates a longer-term problem for your marketing organization as a whole. So yes, the challenge is very much that if, yeah, up to now, like, or if you used to rely on historic data, That's probably better than not relying on data at all.

Sure.

Yes.

Agree.

It doesn't, even that never enabled you to close that or made it very difficult for marketers and marketing operations leaders to close that gap in terms of revenue required.

Yeah. And, you know, you use the word, I think intuition and talking about like what happens either in the planning stages or in reaction to underperformance is that We'll tend to lean on our intuition or, you know, in air quotes, right? What has worked in the past, right? And chasing some metric that we've committed to. What, so I mean, I feel like it's a pattern I think a lot of teams get into and probably have more so in the last few years as there have been challenges in the economy and everything else. I mean, I, I'm curious then, so I think there's a place for some intuition, right? At the same time, I think I used to, like, I see pattern, this pattern play out over and over where like, we're not making our mark. And I think there's also this assumption that you mentioned pipeline. I would back it up even like if, if, if the marketing team's goal is based on say MQLs or sales accepted leads or something like that. It's relatively easy to generate more MQLs or sales accepted leads, but the quality and the conversion rates downstream then tend to not be as good. So, but yeah, I, what have you, do you see this pattern play out too in, you know, either your work in, you know, some of these organizations or in your client work with AlgoMarketing or what's, and then what, Have you seen anybody sort of like recognize that they were doing that and then be able to change course to be a little more data— I don't like data-driven either, but data-informed on their decision-making?

Yeah, I personally, what I found or how I personally try to address that challenge, because I believe I completely agree with you, it exists in every organization. And to your point, the tension always comes in. At the MQL to SAL conversion rate or MQL to opportunity conversion rate where the handover to sales happens. And the very common friction there is that if the pipeline, if sales doesn't generate the pipeline, then of course the challenge that is posed to the marketing organization or like the frame, the conversation can easily be framed that the MQLs aren't of high quality enough or that the quality of MQLs isn't high enough. So, and the way I personally always try to address the challenge is by looking at the data and looking at what the data tells me. So, for example, one way I address the challenge is when there was a drop in conversion rate because the sales organization scaled very quickly in one of the organizations where I was leading. growth, the growth marketing side of things. What I noticed is that the conversion across channels was dipping. So it didn't matter which channel the MQLs came from, whether they were organic or paid or direct or referrals, irrespective of the source of the channel, the conversion rate dropped from MQL month on month with the quickly expanding sales team. So, in other words, I was trying to use data or I used data in the conversation. So, rather than— I think those conversations can get emotional sometimes, right? And data can be a good way to find objective hints at what's actually going on in the organization. And Effectively, the point that I saw, or what I believe the data told us in this situation, is that because the conversion rate dropped across all channels, it seemed the challenge seemed to be more in the quickly scaling sales team than in the quality of the MQLs, because it would be a big coincidence if across all channels, whole MQLs lost a similar amount of quality over a couple of months.

Sure. Yeah, it's interesting. It brings to mind, I remember working at an organization where, in this case, the like SDR, BDR function was under the marketing leadership. And there was, organization was, had a pretty solid structure of capturing sourcing of, we'll call it pipeline, but it was very much built on this very linear model from MQL to SAL to qualified pipeline, qualified pipeline to to opportunities and so on, right? And to your point, right, goals didn't go down year over year. They went up in, in the aggregate. And the, one of the challenges was that we had to, you know, when we were getting in planning for a new, the upcoming year, we already knew that we had been under that pressure, done a lot of volume stuff. And I was trying to convince our leadership team that what we needed to push back on was not, it was, I was like, focus on the end, right? We want revenue to grow, right? That ultimately it's like, you know, if you work backwards from historical conversion rates, it's going to drive to a larger volume, just simply a larger volume of MQLs that we'd have to get. But to get that, we could totally get that. But like the quality overall is going to go down. So like then the conversion rates won't match and we're going to get in this sort of Disappointment cycle. And that's, that was my concern there. So like how, you know, so I guess this is it, right? So you get into this, into it, you know, this cycle where it sort of, you're like, you're, you know, chasing something that is not really going to ultimately benefit the organization potentially. Yeah. And you could argue it's marketing, sales, like where's the process or where's the challenge? But yeah, to me it also adds in operational risk. We touched on this a little bit, right? So, you know, you make mistakes and things like that, which exacerbates the problem. Do you, I mean, I, what I just described, right? That scenario at that company, is that a common thing that you've seen with your clients or in your own space where that planning process is based on historical performance, but you know that going forward, like that's not going to Yeah, exactly.

So the channels that you outline, yeah, that's, I believe every marketer or marketing operations person has seen that. And yeah, that's just generating more volume of MQLs, which if you know kind of how the system works, you can always hack your way to more MQLs. I believe it's kind of, it might be a tactic that's working short term, but I think it's creating then challenges in the long term, which we already went into. I do believe the opportunity is to look at the full funnel or definitely up until the pipeline. So deeper into the sales cycle. And yeah, from that really understand what, yeah, how to generate the right MQLs that generate opportunities. So you don't end up with vanity MQLs, which don't Yeah, which effectively deteriorate the quality of the marketing. So yeah, I believe there is also an opportunity for anybody in marketing operations, for instance, to step up and take more of a revenue operations lens and not try to necessarily defend marketing, but really objectively look at what's going on across the funnel and where improvements can be made, not as a— by a side, because the goal is to assign blame. It's more to really understand what the challenge is and how to resolve it. So marketing operations teams that, or individuals often have that opportunity to take on that neutral perspective and also elevate their profile, quite frankly speaking, by bringing in those data-driven analyses and conversations.

Yeah, I think this hits on like 2 points that I try to encourage people in marketing ops to do, which is one, understand the context of how marketing fits into how your company goes to market and how it makes money, right? It's like if you can understand that better, it will give you a different lens into, you know, prioritization and how you make decisions about the finance, about how you You know, support or not support things that you're asked to do in marketing operations or come up with better alternatives. The second is this idea of understanding how other teams work, right? So building relationships and maybe even doing shadow days or whatever in with your counterparts in say sales operations or customer success or sales even, right? And I think there's a lot of value to doing that. So if you don't understand that other stuff, it's really easy to discount the challenges that they're going through and think that you're the only one who has the challenges. So off my high horse for a minute here. So I guess really what we've talked about, right, is this reliance on historical data and analytics is part of the Part of the challenge that we have. And then if I understand right, you and I've talked about before that you believe that you can, we could now, and maybe this is because of AI or other tools, right? That you can incorporate predictive analytics into what we're doing in terms of campaigns and tactics with a goal of improving whatever key metric it is or set of metrics that we want. So maybe first for our audience who may not be familiar with what that means, like what the term predictive analytics means, if you could provide a definition, and then maybe how that can help teams that are doing, you know, whether it's planning, actually executing on, you know, doing the, you know, campaign projects or tactics, things like that, and getting the right performance. How, like, it's a long-winded question, but if you want to take a shot. Yeah, sure.

So, I mean, predictive analytics is really a technology or a way of analyzing data that is forward-looking in nature instead of backward-looking in nature. So making predictions on outcomes of your campaigns based on historical data. And of course, with the integration of AI or like the foundational model for this being AI-based, what this enables you to do is, or what, well, there's 2 sides to why it works or it's adopted very quickly by different organizations at the moment, is because one, with AI, you can integrate data much faster. So all the manual integration that makes it normally very challenging and complex to to come up with holistic reporting that can be done much faster now. And then by looking at what has worked in the past and just aggregating more data than before, you can look at opportunities or closed-won opportunities from the past that were generated through marketing and create lookalike audiences and have those lookalike audiences be based on criteria that your database segmentation captures. But even beyond your database segmentation, let's call it dynamic segmentation that is informed by AI, it would allow you to just identify in a much more targeted and better way audiences that tend to convert really well and the messaging that that allows these audiences or that speaks to these audiences in a way that enhances their funnel conversions effectively. And yeah, with this resonating messaging and by identifying the audiences, you can then build campaigns around or your campaign plan around that. So yes, there is an element of knowing what worked in the past, but you can also become much more hyper- focused and personalized by specifically addressing those audiences and building campaigns and channels out speaking to those audiences. So you can not necessarily, again, to the previous point, generate more MQLs, but generate the right MQLs that have a high likelihood of converting, and which again is very likely then to get the organization to their revenue target as a whole.

Yeah. So at the risk of using a analogy that might not come across well is like going from using a shotgun to a sniper rifle, right?

Correct.

Yeah, so the only, so I'm familiar with the term predictive analytics going all the way back to my first start into marketing is in database marketing at a large organization where we had what today would be called data scientists who were doing predictive modeling. So I think the challenge I think people would run into is, and maybe you've seen this, is that any predictive model is going to have assumptions built into it, right? And if the assumptions are flawed, the model itself will be flawed and its predictive capability. So I know that what I would tend to want to do if I was doing a predictive model, say I was building it on my own, was I'd want to do at least say 3 scenarios with different assumptions built in so you can kind of see what, like, you know, are they consistent? Are they inconsistent? What's the range of outcomes that we might expect as opposed to this is exactly what we expect if we do this thing, right? So I mean, do you have that same kind of viewpoint? And I'm thinking really more like as like human-based predictive modeling as opposed to— we can get into AI here. I think I'm curious to hear that.

Which is exactly why the way to go is with a human-led and AI-assisted approach.

Okay.

Because the knowledge of, yeah, the overarching knowledge that a marketeer or a product marketing operations professional has aggregated over the years, yeah, is critical to success and may not be taken into account by AI, or there might be some false assumptions that the model needs to be trained on. So, I would approach it— what I think the near future will be is that instead of necessarily expanding our team exponentially all the time, which is difficult anyways because the budgets don't support it and people are very overstretched, The realistic scenario will probably be that there will be different AI models that would be the direct reports of marketing and marketing operation professionals. And just as any person that you have on your team, you would need to train those models as well and make them better at what they do. So the feedback there is very important. And I definitely agree as well that the expertise or the human expertise is critical, especially when it is about outcome projections to look, for example, are we actually looking at the right metrics here? Like what does really constitute success in the long run beyond our annual KPIs, let's say, or OKRs? So those are important questions that again, a seasoned professional will be able to answer and have a very thorough understanding of informing the AI model to behave in line with that, right? So where the managerial responsibility comes in. Yeah. So in short, I do believe that there is always, as a professional, we need to take into account that it might take some training time. I would always go with the with the worst outcome prediction and not with the best outcome prediction to be on the safe side.

No, I agree. It's interesting that you did this analogy of like training the model is not all that different than training a human resource, which is, I'd never thought of it that way. But it seems to me that the risk is that if we're, there's a fine, it seems to be like there'd be a fine line between training the model and ignoring the model. Right? Or totally discounting the model's output because we didn't like it through our own biases or our own fear or whatever. Like, it literally just occurred to me, so I'm like, it's bouncing around in my head right now, but it seems like that would be a challenge as well. So you, and I think I've heard this from folks at Algo Marketing, as well before, but I hear the term next best action. It's sort of that, is it? It seems like that's the outcome you're striving for from predictive analytics, whether it's AI-driven or not. But I have in my head what I think that means, right? Next best action means, but what is that like? How do you think about that? And if you have any examples of where that's come into play, I would— Be curious to hear about that.

Yeah, sure. I think there is a head start when it comes to next best actions, probably on the sales side at the moment, because it's just very easily applicable to the sales side. So, the way I would explain it is that sales have to follow up with a lot of MQLs or have to do a lot of outbound prospecting. And for them, it's very difficult to decide, okay, or to, they practically don't have the time to look at every lead individually and see what industry they're from and have a custom follow-up to that lead, what decision-making level and so on, and all other criteria that are important. There's just not the time to do personalized follow-up. And so there tend to be these standard outreach cadences that MQLs are being put into.

Oh yeah, I get them all the time. Yeah.

And also, there's often a disconnect from marketing. It's not always clear what the leads experience on the marketing side. So the sales follow-up can be disconnected from the marketing side and what the actual experience was for that person. And that creates a friction, of course, in the customer experience or in the user journey of the prospect. And that's where a next best option model, what it can effectively do is there's again the predictive analytics aspect to it to decide, to understand who this person is, what that person experienced, what that person is looking for based on lookalike audiences that have become customers in the past, and then create a path for them that is personalized to a much higher degree than the standard outreach it is. So How that would look like is that an SDR, instead of getting a lead and putting it into a sales loft cadence, it would pre-populate a cadence outreach email by email and effectively tell the SDR, okay, based on lookalike criteria and what has worked in the past and personalized messaging, You should follow up with these 10 or 20 leads today and, or in the next hours or so. And then they would go into these MQLs and then open them and then open the Salesloft window for them basically, or any kind of interface that allows them to directly message them. And that would already be pre-populated. So instead of coming up with a personalized message from scratch, It would just take loads of information into account, even search online on that company and what they're going through at the moment and what their priorities are, and then pre-populate a message that seems relevant. And then the sales rep can, of course, edit that message or change it, but that makes it very quick and repeatable to reach out to prospects in a way that is way more personalized than the standard sales loft outreach cadences. So, of course, that use case is very sales-centric on that one-to-one communication, but it kind of shows the potential or the potential for making personalization scalable. On the marketing side, it probably won't be to that degree that if we translate that concept, it won't be to that degree that every person has their personalized journey, but you can through elements of AI generation of assets and copy, generate many more variations of one campaign or one email than you are able to do with people resources or just by your own. So what that would mean then is that you can maybe have like instead of One email, you can have 20 emails or 30 emails, which are all auto-segmented. The first draft is being robo-built from a campaign perspective, but also from a copy and asset perspective. And then instead of, again, I would refer to the manager analogy, instead of being the doer that has to write all the copy or build all the campaigns, the The person managing that process would become more the manager of the process rather than having to build everything from scratch. So reviewing the copy, QAing the campaign before it goes live. And yeah, that would enable that next best action model to be translated to the marketing side where also the timing then is adjusted to the, yeah, what, which leads are the priority leads that need to And what do they need to experience and see next in order to make the conversation very relevant and convert them down the funnel?

So I want to get back into this sort of robo-build stuff, but I have a follow-up question about this. So I love the idea of what you're talking about for on the sales side. My experience has been mostly on sort of large ticket items that have a relatively long sales cycle. And one of the things I found is whether it's through churn and turnover in the sales team or just because there's more of a focus on things that are near-term to close, that ongoing— I want to use the word nurture, but like from a sales standpoint of these opportunities that are in early stages doesn't seem to get a lot of focus or activity. And so I could see a lot of value for something like that. Do you, so, do you think that same model applies well for both sort of sales processes or customer buying cycles that are really long because we have an expensive product that is a long-term kind of thing versus those that have a shorter one?

Yes, that is a great point and question. So definitely I would say a difference is when you have a your kind of high velocity, lower sales price or annual contract value, then it tends to be more of a volume game. And therefore, of course, you have a lot of data to inform your decisions and to hyper-personalize and so on. When we go more into the into the high ACV or annual contract value products and with longer life cycles, probably also larger enterprises that are target customers. There is, I think it probably moved a little bit in reality from next best actions or like the next best action can be still there, but I think it would be more a recommendation basis. So Mm-hmm. What, what, because often also the, the salespeople that are involved in that are very senior sales executives that have a lot of experience in that particular industry, maybe even longstanding relationships with the prospects that they're working with. And there, you probably want to give less direction. You want to give more relevant insights. So what that means is, You aggregate a lot of information, and I think that's what account-based marketing is often trying to do. But it's very difficult, kind of again with traditional methods, because it's very easy to aggregate information, but the quality of the information might not always be there, right? So again, I would apply a similar analogy of a sales executive training assistance, so to speak, on aggregating information and what information would good information looks like, what not good information looks like. So they effectively personalize or specialize the products directly focused on their client base and prospect base over time to provide them with the relevant information or train models to provide them with relevant information that really allows them to move conversations forward with relevant insights.

Yeah, I think I like that. So it's a little less prescriptive and more of a Well, like, I, I, the word that's coming to my head is a nudge, right? Yeah. You've got this opportunity that you, you don't think it's going to close for another 12 months, but you haven't interacted with them in 6 weeks. You know, you should probably go reach out. Something like that. Is that kind of what you're— Yeah.

I mean, that, that could be, I think what, what, what, like this could even, I would imagine be possible with static reporting that just alerts you. But I think what it can do, yeah, that, that would be one component. I think the other component would be. to come up with a compelling message. Like, for example, you get an insight that a decision maker has changed in the company, or there was a reorg, or there was a shift in their go-to-market, or anything that might be important there. Or it's just like without having to go through everybody's LinkedIn profile and so on, right? Or reading the news yourself.

Right, right, right.

You basically get relevant talking points of why you're re-engaging instead of just re-engaging.

Got it. Okay. That makes, okay. That makes a lot of sense there. Okay. I get it. So, so going back to this idea of, I think you used the term robo-build of, of multiple variations of stuff. Can you talk a little more about what, like, are you actually doing this with any of your clients today? Or like, how does, what does that look like?

Yeah.

So Yes.

The next best action on the sales side is something we recently implemented with Cisco. And yeah, that was, yeah, or is going very well. I'm not sure how much I'm allowed to reveal about the numbers, but yeah, like that is a successful pilot program for them that they're looking to scale now. And Yeah, that use case was an inside sales team or is an inside sales team that has every sales rep, it's truly a sales rep and each sales rep has thousands of accounts and they tend to get drowned or the challenge is how do you see the trees with all the forests around, right?

Yeah.

And yeah, and so for them, it basically is a way to prioritize which leads or which accounts to follow up with or to reach out to. with what message and to streamline that process, almost kind of coach them and mentor them through this sales cycle. That is one key, yeah, like, or like the start or like one example of how we specifically at AlgoMarketing implement next best actions. On the marketing side, I think, yeah, it often starts with insights. So one thing we saw is it makes a lot of sense to kind of start with insights and actually look at the data and aggregate all the data in marketing that sits across the organization and not only have the results of what the performance looks like as outputs, but also have a narrative to it that is AI-generated. So to not only know why MQLs are going up or down, like, no, that, and you not only know that MQLs are going up or down, but why they're going up or down and have that.

Yeah.

Or look at a specific region and have nuanced explanations and you can drill further down and have additional explanations and so on. So that gives executives and regional marketers and yeah, a lot of insight into what's happening and why it's happening in real time instead of having to do manual analysis. The analysis is much more custom and generated on the spot. And also, there is the data storytelling aspect to it. So that, yeah. And then you, again, in the role of more verifying, does that sound accurate or is there something that the model doesn't take into account, of course? But yeah, starting with insights, that's a common approach. And then moving from insights to what we call direction, where based on what you're seeing and what's working or not working, what should you be doing next? So this would be very helpful for any marketing managers that are in the position of planning their programs, planning their campaigns to get recommendations of what they should be doing next. So that sits mostly on the analysis side. The opportunity Or like the way you can take it full circle then is to integrate it into marketing automation so that you actually, from insights direction, you get into action where campaigns are robable for you. So whether the direction or the recommendations are actually implemented for you, where the audiences are pulled in, where the emails are generated for you, the campaigns or programs are generated for you, and you then review them. That is the deepest level of integration probably, which is why we see organizations often start out with the insights and direction first before they move into the action element of next best action.

Right. And going back to the analysis and insights piece, something that you talked about with me is sort of differentiating between process and funnel type metrics, but I'm not sure I totally understand the difference between those 2 and why that's an important distinction. So if you can elaborate on that.

Yes, I feel like in especially marketing operations, that can be quite helpful to distinguish it in a way. If you imagine the quadrant and on the upper side, oh, how would it look like, you know, like the x-axis or the horizontal line. would be like high impact or low impact on the business.

Yeah.

So let's say you get your operations requests in or tickets in, so you can sort them by how, like, if the impact would be big in a positive way on the revenue or any MQL or pipeline that would be generated, if that would be a big impact or low impact, right? And then the other side of the quadrant or the y-axis would be, is it easy to do or hard to do? So, ideally, like, if easy is on top and high impact is on the right, what you probably want to prioritize is requests that are easy to do and have a high impact on the organization.

Yes.

Probably that's how I would outline the difference between process and metrics. That from a process perspective, it can be easy to do, but it doesn't have a high impact on the organization or the metrics, how it impacts the metrics, what you're doing, right? And I feel like when I was more on the marketing operational side or managing that side of the business as well, that's at least how I try to manage the amount of requests By acknowledging that, yeah, these are like things that should be done, but they just don't have the impact or they're just very hard to do. And that's why they should be deprioritized probably.

Well, or you could take that and say, you want us to do something that is a high cost slash level of effort, right? But it's going to end. We believe we all agree it's going to have a big benefit, but maybe there's a way to do it. that gets close to the same benefit for a much lower cost, right? And this is where you can get into sort of consultative approach to how you react to those. Okay.

Absolutely.

Yeah, yeah, that makes sense. So let's kind of wrap up here. Like one of the things that I think is a challenge for a lot of people is that, and you and I kind of talked to, hit on this, right? Even at the beginning of this, right? That You know, very often we're asked in marketing ops to do analyses and, you know, we turn those around fairly quickly. If we're lucky, there's a data science team, but usually the data science team is tied up with senior executive requests, right? And we don't really have the kind of cycles or things with them. So is there, is there, you know, you used the term, I think, of democratization of some of these capabilities. So, how is that going to help our audience who are being asked to do more and more analyses and insights with larger and larger volumes of data and not the time or the skills sometimes to do those?

Yeah, that's exactly— yeah, that's a great point. And yeah, I know the word democratize is probably a little bit overused, but I think the Scenario to imagine is that if, let's assume your organization is taking steps towards implementing insights so you can get through chat interface and you're prompting this engine to produce reports for you and explanations or narratives around, or data storytelling narratives around the trends that you're seeing that basically enables or allows everybody to have data analysts in their pocket or like, yeah, as an agent working for them, right? And that is exactly the challenge that I experienced, definitely, that is just the access of analysts. And because data pulling takes so long and having data storytelling element to it, that what naturally happens is that the analyst resources are reserved for the very senior folks in the organization. And by having access to this AI analyst, it allows just everybody from a specialist level upwards to do their own analysis or impact analysis that maybe they didn't have access to. So, like we were speaking before about whether you should leverage data or you shouldn't leverage data, to inform your decisions. Often challenges that I also had and many people have is they just don't have access to this real-time data or they don't have the time to analyze it and inform the decision-making. But by having that real-time capability of understanding what's going on and why it's happening, it allows everybody really to come up with their own analysis and also formulated in a way or articulated in a way that it is understood by your manager and your manager's manager and their manager. So, what I believe will happen is that it doesn't matter so much anymore how senior you are in the organization. It enables bottom-up communication that if you have a real good insight, you are able to communicate it upwards very easily. Um, and yeah, it doesn't, yeah, it doesn't get lost or there isn't this disconnect anymore with senior management looking at data and everybody else underneath is just executing and focusing on process only.

Yeah, I think, I think a lot, I, I'm very hopeful for AI tools to enable faster, more repeatable, more, uh, uh, I don't want to use the word elaborate, but, uh, more, maybe more complete. I'm not sure what the right word is, uh, analysis. So that people can focus on the storytelling piece of it. And then also naturally what happens with a lot of these things when you start reporting this stuff out is there's follow-up questions, right? To go either drill a little deeper or go somewhere tangent to that. And I think that, you know, it's just another thing that adds time that's drawn away from, say, these other priority items. And so That's what I really hope comes out of this. I still believe that we still need to encourage people in our listening audience who are in ops, if they have not gotten themselves familiar with analytics terminology, data management, statistics, they still should do that because I think that's still going to be a critical skill even with these tools. So again, off my soapbox, high horse, whatever, but it's a message that I feel is really, really important for our audience to hear on a regular basis. All right. So, we cover a lot of ground, Clemens. I know we're kind of up against the clock here, but any final thoughts about all this that we haven't covered yet?

Yeah. Maybe leading on from what you were just saying that I definitely see the ability to have real-time data insights and storytelling tools around those data insights can be a massive accelerator to everybody's career because what you're suddenly doing is you're speaking business instead of process language and familiarizing yourself with those business metrics and the terminology around them and how you communicate success on a business level rather than let's say on an operational process level, that I think is the opportunity or the shift that allows any marketing professional, marketing operations professional to accelerate their career most quickly because executives will never know how Marketo works or how a marketing operations platform works. What they care about is outcomes and outputs. And yeah, of course, the quality of these outputs as well. But Yeah, that, that I think is the, the opportunity to elevate one's profile inside the organization by speaking the same language that business executives are speaking.

Yeah, it becomes an and decision, not an or decision, right? I could either learn more about process and marketing technology, or I can learn more about data and analytics and storytelling. I think now it becomes a, I can do both.

Yes, exactly.

Which is great. I love that. So thank you, Clemens, for sharing. I know it's staying up late on a Friday. I appreciate that. If folks want to connect with you or learn more about what you're doing or what you're doing at AlgoMarketing, what's the best way for them to do that?

Yeah, please send me an invite request on LinkedIn. You can, of course, kind of contact AlgoMarketing as well, or us through AlgoMarketing if you're interested. in predictive analytics or next best action or hear anything more around that, please contact us through algomarketing.com. If you want to have a chat with me, I welcome anybody that would like to have a chat with me. Please send me an invite through LinkedIn and quickly say that this is where you heard about me. And then I'll look forward to having a chat with you.

Fantastic. Thank you for that. Thank you again to our audience for continuing to support us and provide your feedback and ideas. If you, as always, if you do have a suggestion for a topic or a guest or want to be a guest, reach out to Naomi, Mike, or me, and we would be happy to talk through that with you. Until next time, bye everybody.

Thank you. Bye-bye.