Speaker 1: 0:23

Hello everyone, welcome to another episode of OpsCast brought to you by MarketingOpscom. Powered by all those MoPros out there, I am your host, michael Hartman, flying solo today, at least for the near term, but I am not alone, totally. I am joined today by Stefano Mazzali, a marketing technology and operations professional with 10 plus years experience in B2B SaaS products. Stefano is currently Director of go-to-market data strategy and governance at Demandbase. Prior to joining Demandbase, he held marketing operations leadership roles at several different companies. Before he made the move to marketing ops, he led marketing at two different companies and his early career was actually in engineering development. So, stefano, thank you for joining us today. Thank you, michael, for having me here.

Speaker 1: 1:10

Yeah, did I? Did I? I know I tried multiple times before we started recording to get your name right. Did I? Did I get close?

Speaker 2: 1:17

That is great. Thank you so much, I appreciate it.

Speaker 1: 1:20

Yeah, okay. Well, so Stefano, I I did a, I did a very. I'm not even sure it's a quick run through, but a quick run through of your career up to this point. One of the things that we find interesting is that there doesn't seem to be one career path that gets people into marketing operations, and your story is really no different to that. That said, you do have a bit of an eclectic background, starting as an engineer and then moving to marketing and then marketing operations. So maybe if you could share a little more about your career journey and I'm always curious about pivotal moments like a decision you made where your career you feel like, if you look back on it, like that was a key decision or a key person that changed the trajectory of my career.

Speaker 2: 2:09

If you could walk us through that, I'd appreciate it Absolutely. No thanks, michael, and definitely a great question. I definitely agree on the multiple paths to marketing operations career in general. I think there's not one unique way to get there for everyone. I think from my specific case, I used to be, I think, a bit of a math nerd back in high school, so I really enjoyed that.

Speaker 2: 2:30

I was going to go for something like math or physics back in college, but then I guess I opted to study mechanical engineering because I saw more like maybe like more real world applications. After that it's just in hindsight maybe I'm not sure it was the best decision, but I think at the time it felt pretty true. And so I studied mechanical engineering and while I think graduating, I had the chance also to do some pretty interesting thesis on numerical simulation of some manufacturing processes, so basically trying to understand basically how a ring will deform under very extreme heat and pressure and predicts basically its final shape using some numerical algorithm. And back at the time we even used some rudimental AI to perform this. So the neural networks, I think, have been around for 10, 15 years. Then we're talking about mid-2000s and the problem was that at the time, both the computing power and also especially the training data was somehow lacking, so they were giving some approximate answers. You have to run all the simulation on your local machines. It would take forever to do it. So I think it was definitely a different experience all in all, but I think I got my hands dirty into those kind of computation. You have to book, set up the simulations pretty carefully to avoid blocking a machine for weeks or so, and so we also had some cluster machines available, but those were a bit tougher to book in general, so we had to book them well in advance and all of that.

Speaker 2: 4:22

But so, yeah, I think that was my initial foray into numerical simulation. I had the chance to. I got really into that. So, like I was looking for a job into that specific industry, I found one in Livermore, california, in the East Bay, which had a company that was doing software development eventually was acquired by ACES and I spent there about like like five years working on simulation data, like assisting customers and basically qa in some of the data that we had here. So uh, it was, it wasn't. I really enjoyed my time there.

Speaker 2: 5:01

I think we got exposed to a bunch of different uh simulation, things like, especially in aerospace, like burst strike simulation. Things like fan in aerospace, like burst strike simulation. Things like fan blade out understanding how maybe the engine needs to be dimensioned in a way that would absorb the impact of such high speed impacts in general. Things also like commercially, how to study the drop test of commercial detergents or something like that. So anything that would any kind of simulation or object that would change shape very, very dramatically in a very short amount of time. So things were going well.

Speaker 2: 5:43

I think I stayed there for like five years. At the end of the five years I was kind of like feeling ready for a new challenge. Somehow. I was uh learning a bit less every day, felt a bit, I felt busy but at the same time a bit bored in some ways, and so that's. I think that that's what gave me a bit the insipid of like uh doing like an mba to broaden my business horizon, leaning into more the dark side of business in some ways.

Speaker 2: 6:18

I did an MBA, came back, started working in the same industry for a while and I picked up marketing, more as an intuition, I think, first of all something that I thought I would enjoy.

Speaker 2: 6:32

So I tried doing different things in marketing for about two years.

Speaker 2: 6:37

So things from PMMs to events to brand digital a little bit of everything in some ways, and we were also kind of bootstra like a small startup back at the time. So there was a lot of things going on at once. But I think that experience was very valuable to me because it also gave me the foundation to relate better to marketers in general in the future, in my future job. However, during those two years I think I realized how I was always driven, kind of like was always seeking more, seeking the analytical part of marketing more. On the upside there was no marketing operations, I guess official tag job description back at the time we're talking about 2013, 14 or so. So I was just taking maybe courses, online, things like data analytics, analytics, some web development, machine learning stuff, all of those things. So in hindsight, I don't think one definitely needed an MBA to get into marketing operations, but at the same time, I think if you come from very technical niche backgrounds like mine, I think it might make sense to just kind of broaden your horizon overall.

Speaker 1: 7:52

Yeah.

Speaker 2: 7:55

But most. I mean to answer your question. I think most of my experience as marketing ops and business operations in general has been an Instapage, which is a company where I stayed around four and a half years and, kind of like, pivoted from B2C to B2B, while I stayed there and I would say like I had the chance to work from multiple areas of the business, from business intelligence to lifecycle marketing, to marketing campaigns, sales ops pretty much everything.

Speaker 2: 8:28

Lifecycle marketing to marketing campaigns, sales ops pretty much everything and so I definitely appreciated the aspect of getting first-hand experience in implementing things from scratch overall.

Speaker 1: 8:39

Yeah, that's interesting to me. I was sitting here nodding my head and smiling, especially about your educational background, because I also was in an engineering school. Now, my friends who were electrical engineers and mechanical engineers would call me a pseudo engineer because I was in what the graduate program would be called operations research, right, so sort of an offshoot of industrial engineering, and but it's all about, like I did a simulation project my senior year right Of a factory floor, but it was more of an odd like the automation, automation right, the, the thing that would move the robot early days of a robot that would move around. So, um, it was a demo space and so every time, uh, this was back when it's pre-accenture as anderson consulting, they had a space and every time that, uh, they had a client that they were going to do a demo with, they would call me and my partner and we would go down with stopwatches and we were timing stuff and then we built a simulation of it, right, so a little different than the kind of simulation you did, but I think that that, like you, right, I think that idea of understanding how these things work, understanding that there's, there's can be variances and that there's back, then I wouldn't have been able to articulate but like that. The world's full of trade-offs, right? So I'm sure in your space, which comes, I think, is a lot of what we deal with in marketing operations, right, as being kind of a guide.

Speaker 1: 10:04

Well, that's interesting. I'm always. It sounds like the and, like you, I never I didn't go back and get my MBA. That's. That's interesting. I'm always. It sounds like the and, like you, I never I didn't go back and get my MBA.

Speaker 1: 10:16

But I believe that that broadened experience also long-time listeners here know that I'm a big believer in understanding finance, understanding statistics, understanding some of those things that may not be day-to-day core to what you do, but it's serves you well, to the point where I have teenage boys and one who already finished his high school math requirements coming into his senior year and I told him, like they have an agreement with the local community college, I said you should take a statistics class, and that's what he's taking out right now as part of his senior year.

Speaker 1: 10:47

And then I'm like, do I take a finance class too? Right, if you can do it, because I think those are valuable down the road no matter what you do, almost so, um, one of the things it sounds like you know, you, you kind of learned along the way, whether it was through the combination of your own experience plus the. The mba is that you have an ability to translate between marketing and engineering or marketing and technical people. Can you like? Can you maybe go a little deeper, like, what did you mean by that? And, um, if you have any examples like of where you've had to help bridge that gap between those two audiences?

Speaker 2: 11:24

no, no, absolutely, yeah, I, I think, yeah, maybe I I don't pretend I have that ability completely figured out, but I think it's just that what I probably meant, I think it was I can zoom in pretty those requirements into technical specifications overall. So I think this came out in multiple projects through my career. So, understanding, first of all, the voice of the marketing team and interacting with both, either marketing or sales or other stakeholders, other stakeholders and based on our own law not just my own but within the team, like the knowledge of the limitations of each technology that we deal with on an everyday basis, we kind of like we can match those capabilities with the sort of like with the top-down business requirements that we receive, right with the top-down business requirements that we receive. So I think that's always served me well overall through the years and I think, overall, I think the flip side of that is also very important, obviously.

Speaker 2: 12:45

So, like one says, to not only being able to zoom in into a problem, but also the ability to kind of like abstract and like zoom out in some ways to uh from the details to the, to get the larger picture, uh, which I think is something that maybe comes a bit less naturally for for engineers in general, just because they they have a more the, their mindset is more like take the you know, take the broader problem, breaking down into as many possible subsets, and just tackle each one, each one, one by one, right. But I do think, however, that you know that's the zoom out part can definitely be taught and just something that I I focus also a lot on my own learning in these last few years around that, and I think it's super valuable if you want to get into really a more like strategic mindset and all of that. So I think both of them are valuable and I think they should be. I see them as necessary and complementary for sure.

Speaker 1: 13:45

Yeah, I feel like I have a similar mindset. Do you find that that helps you? I think one of the challenges that a lot of people who are listening would say they've experienced is they feel like they go to one of two extremes. Right, they either get a request that is complimentary, one that, as they understand it, requires I'll call it heroic efforts to make happen, and they go through that heroic effort and they enable it, and then people are like okay, the next time they're going to just ask for more right. And then they feel like they can't push back, or they go to the other side, where it's just they say that request is stupid or it can't be done.

Speaker 1: 14:24

Technology doesn't do it. And what I found is there's a medium place that you have to learn to negotiate with Like, what is it that they're really trying to accomplish when they come to the request, and can you find a way to get them close or a step in the direction towards it? Do you find like your experience has enabled you to have those kinds of conversations, or or am I the only like? Is it just my own sort of crazy mind where I'm thinking about that?

Speaker 2: 14:49

Like, is it just my own sort of crazy mind where I'm thinking about that? No, yeah. No, I definitely agree on the necessity for, like a compromise in general and being able to just you know, obviously you know try to make our internal customers as happy as possible and just being able to really focus on their own problems. So, like you know, all in all, you know we don't, we know the limitations of our tools, but also we sometimes we have ways to obviously bypass them, maybe using some, you know ways to do some like custom integrations, or a way that, like using some like layers of technologies that can, or a way that, like using some like layers of technologies that can, you know, kind of like overcome the limitation of each individual tool, I would say themselves.

Speaker 2: 15:37

So I think, it's important to kind of just blend these two together and just being able to come up with a solution that makes sense.

Speaker 1: 15:44

Yeah, no, I like what that always feel I feel like that leads to is a fragile tech ecosystem, you know, when you start doing those workarounds. So I you know as much as anything. I want to try to avoid that. I'm big on simplicity where we can. Your background is that you work. You work both in a, in b2c and b2b. I would say I don't have any hard data on this, but I would. I would venture to guess the majority of our listeners and certainly the majority of our guests are more b2b. Uh, marketing and marketing ops folks. Um, since you, you've worked in both worlds. I my first foray into marketing was in a B2C world, but what was your experience? What do you see as the big differences between those two worlds in a general sense?

Speaker 2: 16:39

Yeah, yeah, no, I think definitely there's large difference, quite big, although I feel like over the last few years things have been converging together and each field is kind of learning from one another. So I think, from B2C, I think definitely the focus has always been more on the speed and speed of execution and scale and more obviously focus on the individual record, while B2B has been more around, um, obviously, account based transactions like slower velocity and like lower, like longer life sales cycle, like not just, you know, matter like days, but let's matter like months. So that that's a that's a big difference. So, um, I I think, like adapting, uh, it's just, I think the two systems of the two frameworks are very different. So, one, it takes a little bit to adapt from one to the other.

Speaker 2: 17:41

I'd say, from a data perspective, I think if you come from b2c, you are a bit spoiled, so, so you have a wealth of behavioral data usually in your hand and you can think that you can personalize pretty much everything, until you get to B2B where you say, hey, wait a second, you have to definitely make some tradeoffs. You don't have all that data available in real time. It becomes a bit more difficult, right? So, at the same time. And the thing with B2C I also think it can become a bit addicting, the way that you can run campaigns, so like I think you see the effects, the results of your campaigns pretty much immediately. So that's also very, I would say, compelling, so you can see immediately the impact that you had.

Speaker 2: 18:34

And sometimes you tend to probably to overestimate your capabilities in that sense because you draw some causation maybe when it's not there. It's more like correlation between things. So understanding what really makes the difference, I think is a bit difficult to do. But yeah, I think the two are definitely different, I think they are converging together and I think that's the best part of that, so you can actually learn multiple things from both and just be informed of taking the best from both worlds, just be, uh, be in form of like, yeah, taking the best from both worlds it's interesting because I I tend to agree with you on all that.

Speaker 1: 19:14

I think I think the the volume and the pace, which I'm sure will be surprising to people in b2b uh world who feel like their pace is as fast as it can be. But, um, I think also you're, you're talking about the pace of at which you get, deploy, get feedback, deploy, get feedback right, as opposed to we're deploying and deploying and deploying. Hopefully we're getting feedback in the B2B world in a reasonable amount of time. Yeah, but I I remember so in the B2C world, complexity to me was always in the volume of data, especially at the time when I was doing that, where the technology really struggled with that kind of volume of data, and so I actually outsourced a bunch to a third party that had a bunch of mainframes and they did monthly updates and quarterly refreshes on our demographics and all that, and they did our householding, which is sort of the equivalent of, say, lead to account matching right that you would have in a B2B world, and even that was relatively straightforward. It's a pretty well-known thing, lots of standard ways of doing it.

Speaker 1: 20:25

I think B2B is much more complicated, not only because you've got these more complicated structures and relationships between the people and the organizations and entities, but also because on your outbound stuff you actually usually on B2C, there's rarely a sales person right. Person right, it's almost always either direct or, if you're a, say, a cpg company, you're doing promotional activity and then the activity happens at a retailer right. So you don't, you don't have that, whereas in b2b you've got the extra added complexity of you've got to build pipeline. You get call it lead generation or lead demand generation, whichever term you want to use that ultimately leads to a salesperson getting involved and there's that process and they're not really, I was gonna say, not trained or not don't care, but like they're not compensated on data quality, for example, right, generally speaking. And so at least a whole different set of complexities that are different than volume but, I think, equally as challenging or interesting, absolutely yeah.

Speaker 2: 21:40

Things like you should never, I think, assume that someone within your sales team is going to help you in adding data, or just so. You should start always considering the worst-case scenario and just putting up using technology in order to make up for some of those data deficits.

Speaker 1: 22:00

Yep, so I wanted to get into a little bit of your role at Demandbase, and I think, if I understand it right, your role at Demandbase and I think so, if I understand it right, your role at Demandbase is focused on how to leverage data from your go-to-market activities, so broadly, marketing sales, maybe, customer success, maybe product data, and that you're then building things out for data quality and governance and things like that. But what are some of the things you're doing and what does that mean to you? What does data governance and data quality mean to you?

Speaker 2: 22:44

Yeah, no, absolutely so. Yeah, I think it's a great question. So, yeah, I think it's a great question. I think we, on one hand, we have, you know this last few years we have this proliferation of data, so like we have ways to like capture data pretty much everywhere, so like things from like systems that can record, like all this raw data in multiple systems. So there's like the problem there is, like you know, it's kind of a blessing and, of course, both times right. So you have a blessing's. It's kind of a blessing and a curse at both times right. So you have a blessing because you have the ability of like extract information from that, extract insights, do even predictions, whatever you need. But in that, that is really the enabler for that to happen going forward At the same time.

Speaker 2: 23:26

The curse is that you need to. There are multiple ways that those data can be, you know. You have to assess the accuracy of the data. You have to make sure that that data is not hindering the functioning of your system. It needs to be governed in some ways right, in some ways right. So there's a lot of potential both like good things that can happen from data and bad things overall right. So like avoid all things like garbage in, garbage out sort of situation. So I think my role specifically at Demandbase is really like understanding, fixing the sources of like bad data first. I would say that can be either the quality, I would say, of a specific data source or the process that moved that data around. So because those kind of processes, if left unattended, they can create relevant overhead on the systems that are connecting the data together, generating things like a lot of timeouts, 500 internal errors, that kind of stuff, affecting also data storage in your CRM or other databases.

Speaker 2: 24:43

And even preventing users from accessing the UI in the most extreme cases.

Speaker 1: 24:48

May I interrupt you a little bit? So I buy into all that that it's important for all those reasons. How do you identify when there's that kind of data quality issue?

Speaker 2: 25:04

Yeah, I think setting up specifically monitoring systems is sort of key. So like understanding, like, how do we what is the percentage of utilization of a certain platform. Understanding like what is the you know what are the thresholds, the limits that we are. Understanding, like, how we are approaching those limits and like and some sort of like a general understanding of where we are for it to be your CRM or be other integrations to that is always important. And overall, setting up letting every stakeholders that are governing because obviously as a team, we set up like some guidelines, but there are also specific teams that maybe are the specific admins for that platform. So like setting up recommendations for, like how to handle API calls, for example, like what kind of volume we should expect, or like what kind of volume we should expect or what kind of limits we should not touch, and so forth. Some of them are built into the platform, Some others. We need to be more descriptive in making sure that everything works together well, but I think we try to give as much guidance as possible.

Speaker 1: 26:32

Gotcha, the reason I ask that? Cause I'm a big believer. Let me back up a little bit. I, when it comes to like reporting and analytics and everyone who's listening has been asked to do some reporting analytics, some more than others and, uh, very often I hear people say our data's crap, like we can't trust our reporting. But I'm of the opinion that you should start doing reporting, even if you know or think that your data is not I don't even like to use the word right, but your data is not where you want it to be, it's not as good as it could be because that will help you identify where there are issues or gaps and then help you identify how do you resolve those. So, are you finding some of that same thing? Are you using reporting and analytics, or are you doing monitoring of systems and interactions, or both, or maybe something else completely different?

Speaker 2: 27:25

Using both. I mean we have some systems in place for more on the, I would say, like monitoring some of the system metrics and some others more like around the database health and database points specifically. So we use mostly the reporting from multiple sources for that purpose and I think, as you said, I think there's the possibility of, I would say, assessing the quality of each data source is very important to use reporting in that regard. So like incrementally understanding where, if the data that we're using makes sense, so obviously crafting some new, I would say like a data glossary overall for the team to use. So everyone is like aligned of the specific definitions and when you introduce some new data points, always making sure that you can triangulate also those data points across multiple sources to make sure that that is relevant and the results make sense for your business and for your use case overall.

Speaker 1: 28:55

But I think it's super important. Yeah, I think that's super important. You end up spending time not arguing about whether or not it's right or wrong, but whether or not the definition is right. And like the definition can be changed right Over time. Right, you don't want to do that as all that often, but it becomes one about like this is the way we defined it. You know, we could argue about whether or not that definition is. Is the one that we all agree to, or maybe we need a secondary one? Right that, whatever that may be? And it gets you out of this like is it quote, right or not right? Do you have that?

Speaker 2: 29:31

Have you had experience with that kind of stuff as well? Yeah, no, I think I mean that's definitely super important. I think that's something that's for our own metrics. It's a work in progress across different teams. So, like making sure that we align on those definitions, making sure that we can. Those are like you know and we know how long some of those definitions are valid for as well. Can it be like for like a fiscal year, can be even for like for last time. But just, I think everyone needs to find an agreement. Until that data is no longer, this can no longer be considered like fresh or like. It needs to find an agreement until that data is no longer is no longer be considered like fresh or like can needs to be updated. But yeah, I think it's a work in progress all the time, for sure.

Speaker 1: 30:13

Yeah, another point of saying like don't wait until you think everything is right. You can start without having all that, because it can be, it can be changed over time. So you yeah, I think you hinted at this, but I think most people listening again would say they know that they would agree data quality is important. At the same time, they either don't have a team like yours or they don't have the bandwidth to spend on sort of the behind-the-scenes data quality but you do so, it's great. The scenes data quality but you do so, it's great. So if they could learn from your experience a little bit, how have you structured your data quality, data governance? I don't even want to say team, but the way you've implemented it there. Do you have a more centralized team that manages all this stuff? Do you have one that's decentralized, where there's only small people at the center, or is it some hybrid of that? It's a great question.

Speaker 2: 31:07

I think we try to follow more like a hybrid approach whenever we can. I think in general. So I think it's trying to get the best of both worlds in general, so like striking some balance between the, the standardizing things at the central level, and like giving autonomy to like different teams to, like you know, have some agency to to make changes for their own needs, Right. So, and that also can be, can depend, can also change depending on the phase of the implementation. So we tend to be a bit more, I think, like centralized during like the initial deployment phase and then we we allow over time to become a bit, you know, to just to decentralize the model a little bit, so like it ends up generally in a in a hybrid a little bit. So it ends up generally in a hybrid mode most of the time so.

Speaker 2: 32:08

I think we have done multiple implementations in that sense, both for using Salesforce integrations and things like automation platform and such. So I think we tend to see that as giving some of the best results so far.

Speaker 1: 32:26

Do you have I don't even know if that's right, but say, a committee that helps you with prioritizing those efforts and maybe helping to decide on that? That are maybe not directly reporting to you but are stakeholders from other departments?

Speaker 2: 32:45

yeah, yeah, we, we, uh well, still in his infancy, but we, we, we started off like uh, data governance, like executive committee starting, like in the last few months, so like it's something. The idea here is like really to to not only like prioritizing initiatives but then deciding on helping to just reduce some of the technology overlap that we might have across different platforms, but also making some of these decisions where it makes sense to have more centralization versus less centralizations, and just being able to adopt that. So I think this is something that we also offer to have like more centralization versus less centralizations, and like and just being able to to adopt that. So I think this is something that we also offer to our customers, this kind of like guidance. But it's something that we started implementing ourselves most recently and so far, I think, yeah, so far it's been, it's been definitely been helpful. It just um, it needs, obviously, you know it integrates with your own cadence and in terms of overall processes and all of that, and I think it's beneficial to all port partners for sure.

Speaker 1: 33:52

Gotcha, sorry, this just popped into my head, so this is coming at you out of left field. Who do you report into? What's your reporting structure?

Speaker 2: 34:03

Yeah, yeah, yeah. So I think I used to actually be part of marketing operations until last year, so I think now my team is actually reporting into business and systems technology, which rolls up into revenue operations and ultimately to the CFO under so on in the finance.

Speaker 1: 34:27

Okay, oh, so that's interesting, that the revenue operations rolls up to CFO that's. I actually like to hear that.

Speaker 2: 34:37

Seems to be a trend, yeah.

Speaker 1: 34:39

Yeah, I don't know. I don't know if it's a trend, but I think it's a more appropriate place than either a head of sales or head of marketing or even a CRO, if you have that, but that could be a whole. In fact, that has been a whole topic for one of these episodes before. So, taking a step back right, as I said, I don't think most people have this kind of team in place to really focus on data quality. How was that idea pitched? Were you part of pitching the idea that we could do this and then getting the resources allocated both allocating budget dollars, technology and people to make this a priority? How did that happen?

Speaker 2: 35:27

Yeah, I think this is, I think I really like overall, I think my former manager like definition of data quality, I would say as a team sport, meaning that like I think since I don't think any single individual can has enough like knowledge across multiple domains to to assess the data quality overall. So like it's just simply simply impossible. I'm thinking about like things like the sales team, like where there are cases where they have a lot of like inner knowledge about the functioning of certain accounts, who has, like you know, buying power across the business units, for example, who are the main stakeholders and all of that. So I think it's really hard for one single person to drive the conversation. So I think it's very important to keep a really close feedback loop with your sales team in B2 ticketing system for collecting feedback about our data problems. So things like missing data, things like inaccurate data, cross-records, account hierarchies, duplicate records, all of that.

Speaker 2: 37:02

So I think that data collection part has been really like the building block to build the case to get more resources around data quality, I think, both in terms of like people and both in terms of tools.

Speaker 2: 37:13

So I think that's how we started the conversation right in terms of allocating new budget around that right in terms of allocating new budget around that. So I think and you can, and it's really I think despite the most obvious impact of these initiatives you can see also the immediate ripple effect with things like, you know, territory planning, like lead routing, and all of that, because obviously you know you need to plan for those across with your data quality, because technically, it would be nice to have data changes always happening in real time, but that can also affect greatly some of these things like account assignments or like lead routing, as I mentioned. So there needs to be a process that really includes them in the picture as well. But yeah, so I think in general, I think we've seen really good results with this system and just we were able to kind of just assign both like more like headcount and more like new technologies dedicated to solving these issues. So I think it's I, yeah, I think I can definitely advocate for that.

Speaker 1: 38:29

Yeah yeah, so you hinted at this maybe, but you know you say the results have been good. How? This is something I would struggle with, and I think a lot of our listeners would too, is how do you, how do you, measure the impact of this kind of work?

Speaker 2: 38:44

data quality, day governance yeah absolutely, I think what one that I mean there's multiple ways to do. I would say like, first of all, like being able to build like kind of like always on, like some like self-serve, like dashboards that are focused on specific channels that everyone can access to see the performance for these data quality initiatives like what is the lift in terms of field rate for specific account fields, or things like I would say, what is the fear rate for how many new contacts have been created for specific executives, and so forth, and making sure that overall, we are able to always update the field with whatever they need. So we have things like sales and marketing cafes, where people informally talk about marketing metrics, or sending out email newsletter on a weekly or monthly basis, where we always update some of these metrics and let the team know. So it needs to be very deliberate and upfront in terms of making sure that we track those results and let the team know about them as well.

Speaker 1: 40:09

Gotcha. So I think we'll maybe get into one, maybe two more questions here and then we'll have to wrap up, but we're recording this in October kind of late-ish October 2024. October kind of late-ish October 2024. So planning for 2025 is either happening or soon to be happening for many people. Thinking about both your marketing ops and your data governance roles. Right, how do you? I think people have struggled with this, as I have. How do you measure? Do you have any recommendations for how people should measure marketing operations at the organizations?

Speaker 2: 40:47

Yeah, no, I think that's a great question.

Speaker 2: 40:49

I think my, I think everything should really I mean ideally should really start from using, like you know, from an OKR perspective, so like understanding, like ideally you should have some company level OKRs that start from the top down and just propagates across the team, so, like any team unit, I would say unit and team OKRs should be aligned with the overall company ones and the broken down into milestones in that sense, and then in that, in that, in that framework, then all the I would say all the general pillars of marketing ops would also self-align with those kr's in general, which are also very they I mean kr's by definition, that they are very quantifiable.

Speaker 2: 41:40

So I, I think you can add into those things, like you know, I don't know a number of MQL and QAs generated for each you know, from our campaigns for, like specifically for the campaign ops team, things like you know, the database metric, health for the DataOps team, things like more like accuracy of forecasting, for example, from the analytics team, and kind of like blend them all together into like an aggregate metric that rolls up to the RevOps team overall.

Speaker 2: 42:21

So I think it's kind of like I, I like to see them all like kind of like aligned together in that sense. But, and I think, especially at the beginning of a new fiscal year, it makes sense to take your time and just kind of like plan out these KRs for the whole year, along with all the milestones, and it's something that maybe might be a bit of a not a perfect process. You might have to like revise some of those metrics on the fly, like when during the year, but I think it's, it's well worth the effort to um to make sure that you are aligned with your overall company goals and overall, just you know like it makes for like more, like overall, like integrated, like a collaboration across the teams.

Speaker 1: 43:07

Right, I think what I take away from that is several things that I would agree with. One is making sure you understand that, how what you're doing aligns with company goals, right, how that ladders up directly, indirectly, right? I think keeping that in mind is important. The second being you mentioned things that are measurable. Right, I think keeping that in his mind is important. The second being, you mentioned things that are measurable, and I think that's a really that sometimes gets missed, right? I think people feel like that.

Speaker 1: 43:33

It's a little squishy, hard to measure what we do. I think there are ways to do it, and you mentioned milestones, and milestones towards certain goals that are maybe not as quantifiable, but at least you can show progress, right, I think there's ways to do that. The other one that I would add, and this is more for me having, you know, led teams and coach people. I think it's easy to start coming up with, you know, eight, nine, 10 major goals or metrics that you want to measure yourself against, and I think that's a mistake, right? I mean, if you could focus on the three, four, maybe five that are most likely to have an impact on the overall company goals, right? I think that's an important piece too. Do you have a similar philosophy?

Speaker 2: 44:18

Yeah, absolutely. I mean there's endless possibilities of like think getting lost in some. I would say, you know, maybe optimization that maybe makes sense for your team, but then they're not aligned with what the company is doing, maybe even detrimental on the long run. So you have to be extremely careful in the planning phase to make sure that those are aligned.

Speaker 1: 44:45

Awesome, great. So, stefano, we've covered a lot of ground, but is there anything that we didn't touch on that you want to make sure our audience heard from or could learn from you.

Speaker 2: 44:58

No, I think this is great, I think I really enjoyed the discussion so far and then I think, yeah, I'm definitely happy to provide more insights if someone wants to contact me Perfect.

Speaker 1: 45:11

Well, that leads me into my very last question for you, which is if folks want to connect with you, or learn from you, or hear more about how you think about this stuff, what's the best way for them to do that?

Speaker 2: 45:29

Yeah, I think using linkedin. I think my linkedin handle is just uh, first and last name stefano matzali. I think that that would be.

Speaker 1: 45:32

That would be perfect, all right there. So there's how you pronounce it.

Speaker 2: 45:34

Say no I think you did great um, well, great stefano, this was.

Speaker 1: 45:40

This was a lot of fun. I I know I walked away from this learning some things. I think I even wrote a white paper for the marketing app stock community this past year about how to measure B2B marketing effectiveness, and I had a whole portion of it on what I call operational data and the importance of it and having a plan on how to make that better, and so I think we touched on a number of things there, and I think that's all good. Thank you, stefano. Thanks for joining us, thanks for all of our listeners out there to continue to support us as always. If you have suggestions for topics or guests or want to be a guest, reach out to me, mike Rizzo or Naomi Liu, and we would be happy to talk to you about that in more detail. Until next time, bye everybody.