Speaker 1: 0:23

Hello everyone, Welcome to another episode of OpsCast brought to you by MarketingOpscom, powered by the MoPros out there. I am your host, Michael Hartman, joined today by Mike Rizzo.

Speaker 2: 0:34

I like the delayed sort of like who is it who's going to be on? Every time you do this, it's great.

Speaker 1: 0:39

Well, the last few have been no one, so yeah, yeah. Yeah.

Speaker 2: 0:44

I tried it. I tried it.

Speaker 1: 0:45

A little surprise. We're now. Let's see. We're recording this early October, which means Mops Palooza is like a month away give or take.

Speaker 2: 0:52

Yeah, four weeks, yeah Woo.

Speaker 1: 0:55

All right.

Speaker 2: 0:56

Yeah, awesome.

Speaker 1: 1:00

All right. Well, joining us today is Adriana Shukla. She is currently director of demand generation at Snowplow. Before joining Snowplow, adriana held general marketing, digital marketing and demand generation leadership roles at multiple different companies. She started her career in web marketing and e-commerce. She focuses on building bridges between technical and non-technical realms to formulate cohesive business and marketing strategies. Adriana, thank you for joining us today.

Speaker 3: 1:27

Thank you so much for having me.

Speaker 1: 1:33

I'm excited to be here. Yeah, okay, good. Yeah, we already uh for our listeners. You know we always have a little chat beforehand. You know we all talked about how different the weather is in our different locations. So, adriana, I think Adriana is the winner, if I'm not mistaken.

Speaker 2: 1:43

Yeah, she was. She's in a more moderate climate.

Speaker 3: 1:47

Yeah, mild climate.

Speaker 1: 1:48

Now let's give it a couple months, and that may change.

Speaker 3: 1:51

Yeah, exactly, yeah, true, let's hope it's a mild winter for us.

Speaker 1: 1:55

Yeah, we'll hope so, all right. Well, this should be a fun, interesting conversation. My assertion, my viewpoint, is that marketers, marketing operations professionals, have created massive amounts of data for their organizations that goes mostly untapped. So to me there's a big miss for most marketing ops teams and professionals. And we can have all the marketing analytics tools in the world, but without the operational backbone to collect, organize, contextualize that data, it's kind of meaningless. So, adriana, you work at a company that kind of does some of this stuff. It's snowplow. What is your take on this right? What role do you think marketing operations teams should have on this world of data and and uh, corralling it?

Speaker 3: 2:40

yeah, sure, yeah, I have a hundred percent agree. We have tons of access okay we can just stop there.

Speaker 1: 2:45

No, I'm just, I like it.

Speaker 3: 2:50

There's your answer. You know I think the real challenge is around the accuracy and making it actionable. You know I, you know, I can say firsthand it's, you know, extremely vital that you have like that solid operational backbone. You know, it's that foundation. Really, without the foundation, you can have all the tools in the world but ultimately the data you're looking at is garbage. You know, we've had all of it happen where you have like Google Analytics, for instance, and we look at a number and it seems like it's ridiculously inflated and you're like why you know what's going on. Or you look at a different platform and you're like and you're like why you know what, what's going on. Or you look at a different platform and you're like this number is off. You just know it. You know, and so it's like, but why? You know and and marketing operation teams were were left to be the ones that just say the why you know what, what's going on here. You know and and so you know we, we as marketing operation professionals, you know, and I have some folks on my team that focus on this we need to focus on, like, what's the source of that data collection? You know, what are you using to collect that data you know, to make sure that you know that first touch point of that, when somebody actually touches the data and then that funnels into something, you know that that point of collection is accurate. And then working with other teams to make sure that all the things that you're collecting are directly tied to those business outcomes.

Speaker 3: 4:15

What is the reason that you're collecting that data? What is the end result that you're looking for? Because, ultimately, everybody wants to just collect everything. It's like I want to collect everything, I want to know everything that everybody's doing at all times, you know, but ultimately, like, you have to figure out what you're trying to solve for Like, what are you? What are you trying to do with that data? And so I find that to be one of the more difficult things for me is, like I always am asking well, why are we collecting that? Like, what are we going to do with that? You know what do we want collecting that? Like, what are we going to do with that? You know what do we want? What's the end result that we want from that? Because ultimately, like, what I find is that we set up all this like collection of data and then ultimately, when we go to action it or go to make it actionable. It's like well, it's not in the format that I want, it's not.

Speaker 3: 5:02

You know it's not actionable, you know it's like oh, this is is great I have all this data but I can't actually separate it out or segment it or those types of things. So it's like what do we, what do I do with this?

Speaker 2: 5:13

well, and it's, and it's for, I think, for our listeners. You know, this is, regardless of where you are in your career uh, you, probably you even in your first year in marketing operations you're going to like rapidly come up against, like, the nerdy nuance of the of what we're talking about here. Right, like Adriana, you're saying, hey, you know, begin with the end in mind, right, is is ultimately your, your point, it sounds like right, and what do you want to do with this at the end of the day? Because, guess what, if you want to do cohort analysis on a plain text field, data point that you didn't set up properly, the standardization of that plain text field is just not going to be easy to analyze, right, and so, by asking the questions up front, what do you want to do? Begin with the end in mind. How are we supposed, how are we meant to try to understand what it is that we're looking to measure here? It actually influences the literal types of fields and data properties that you're creating Multi-choice, single options, booleans for yes, no's and sometimes plain text, right, and so that?

Speaker 2: 6:26

So that it's, it's for the non marketing ops listeners and rev ops listeners. If you're an executive CEO, that doesn't really get into the weeds of this stuff. It's that layer of nerdy detail. Those are the reasons why we're asking the question. It's not. It's not that we're trying to tell you to not do the thing that you want to do. It's that if we don't set this up the right way, you're going to be pissed when we can't generate the report for you, right?

Speaker 1: 6:56

no, it's like I I know, um, a couple of things I want to follow up on, but I think I have. I know I've read from a, from an experience I had. Uh, I was working at a company where we had marketo and salesforce. Salesforce had been around a lot longer and we started monitoring the amount of updates that were happening to data fields on the contract records in Marketo. And also I noticed this huge spike Every day, these large volumes, way more than we actually had records in the database. So things were being updated multiple times every day on two fields that stood out country and state, or you know, country and state or region, whatever.

Speaker 1: 7:37

I started trying to dig into why. Well, what it was is the two systems had different sets of values in the way they were tracking it. So a record would go from Marketo to Salesforce, salesforce would normalize it right in quotes, it sent it back to Marketo, which would then normalize Like it was this like continuous loop that was happening. And I was like how did we end up there? Because I inherited all this stuff, right, and so, like that's the kind of example of things though that can make this stuff hard to wrangle, right?

Speaker 3: 8:07

Yeah, and if this stuff hard to wrangle right, yeah, and if you have teams, you know, working on those different systems, you can also have them contradicting each other. So they go in this infinite loop where you know you're saying one thing for a value and the other team is saying another thing for a value and they're just contradicting and overwriting each other back and forth.

Speaker 1: 8:22

You know that is exactly what was happening. So I want to pick up on one thing you talked about early on, that like the importance of accuracy of data. So I'm going to say something I'd like you to respond. So like I don't believe, especially in the B2B world, that there can be true quote accuracy or like things are quote right, and I think that a lot of that has to do with just the typically lack of controls, to do with just the typically lack of controls like unlike, like, say, financial data, where there's lots of controls, it's very sort of rigid um and there's standards that go across all industries almost globally, but it's not the way it is in b2b marketing and sales data. So I'm not saying there's not a way to find accuracy, but like I, like in general, like I I don't know that there's you should have an expectation, there's a, if there isn't 100 accuracy, you won't reach it. Like you tend to think that's also the case.

Speaker 3: 9:16

or tell me you know we're always trying to get the closest we can to accuracy, right, you know, and I I do agree that you can't get 100. You know, um, you know we obviously want to get to 100 percent. Everybody's like we need. You know, especially executives are like why isn't this 100 percent accurate? You know at all times, and you know the understanding of that is that you know there's human error, that's an aspect of it. There's also technical error, you know where. You know a technology is coming to play and something comes in and it's formatted wrong.

Speaker 3: 9:52

You know it's all about trying to minimize that level of error, right, and and what that percentage of error you're willing to in every business is different. You know it's like what are you willing to say I'm okay with this level of error, and that's something like as a business you kind of ultimately need to decide on, because that's and that's what I mean by that focus and that end result, because it's like, on a revenue KPI, you're not willing to have a large, if any, level of error, whereas on the number of sessions on a webpage you're willing to have a certain amount of error potentially, and so it depends on what the level of data is that you're looking at. Are you looking at? Is it an enterprise-level client or commercial? Are you looking at just a page view or session? Are you looking at the medium or source? Or what are you looking at and what are you trying to get at? That will tell you the level of a room for error that you're willing to work at.

Speaker 3: 10:51

But I think what I always emphasize is that you're only going to get to that level of error that you're willing to live with by documenting out exactly what you are trying to measure. What's your end result? And then measuring, you know, and also putting in exactly what you're trying to track. So you know, mike, you had mentioned like is it a Boolean, is it a text field, is it a?

Speaker 3: 11:16

You know, getting to that level of granularity is what you need to do and it's it's a sucky exercise. Everybody hates like going through that exercise, but I almost see it as like almost a spreadsheet, you know. But Snowplow has like this data product studio right, and it's a way for us to provide that visibility to others and empower others to understand the data. And so it goes into that granularity in giving context of you know, the events that you're tracking. What is it, the why behind it, etc. So that when people are looking at those fields in your CRM or your marketing automation platform or a BI tool, you know what those fields mean, you know where it's getting collected from, you know what you're trying to get from that data, and that's what's most important. And so then, once you have everybody aligned, there's less room for error because everybody is aligned and there's that visibility across different teams and across the organization. It's kind of creating that data driven culture, you know.

Speaker 2: 12:20

Yeah, I love that and it's it's an exciting time to be in in this field in general, I think largely because we've all gone through the hurdle of fumbling through really bad data architecture and we're now getting to a place where we realize, you know, revops as a category is becoming a thing right, you know RevOps as a category is becoming a thing right, and we're trying to align all these different operational partners in the business across sales and support lives in that field, right. And then where you as a business right are saying, hey, you know. Very concretely an example is we have a customer on file, right, and the name of that customer's company record and their physical address is it starts in the CRM, right, it started there because that's how the sale started, right. Once it ends up becoming a customer and finance has it and there's a billing activity that happens inevitably, you will get a corrected set of data because you have to invoice them to the right place, right. And that billing address is now different and it lives in a totally separate you know system.

Speaker 1: 13:55

Well, I wouldn't even use the word corrected Like it's another data point, right, Because it is another right, it is another data point.

Speaker 2: 14:01

right? Yes, that's that's right, Hartman. Right, it's another data point.

Speaker 1: 14:05

I love this. Yes, you're right.

Speaker 2: 14:09

I love this. I keep coming. Yes, you're right, I love this. It is an additional point that typically or historically, has lived outside of the go-to-market stack. And now we're saying, hey, that's a problem, because when we go to reconcile and try to understand who are our best customers, who renews most, you know who adds on more services or products and all of the questions to try to reconcile that information when you don't have it actually coming back in from the finance side of things or standardized, or you're saying, hey, that's where we look for this information Like that. This is. What's exciting is that we now have systems out there to help us do that Right, and we're all aware of the fact that we need to go find the systems to help us do that, which, historically, we were not doing a very good job.

Speaker 3: 14:59

Well, I mean, before it was like everybody was kind of working in their silos looking at all their data separately, you know, and doing their things Right. Data separately, you know, and doing their things right. And and now, because there's larger business initiatives to do like AI recommendation engines or like, do something within the product that you know, like, as somebody becomes a customer, utilizing their past history to, you know, do different things within the product. Now it's becoming more and more important for companies to have that like single source of truth, like that full customer view. You know different things within the product. Now it's becoming more and more important for companies to have that like single source of truth, like that full customer view. You know that 360.

Speaker 3: 15:34

And so it's not just about the beginning funnel for, like marketing stuff, it's not just about the finance data, it's not just about the sales data separately Now. Now it's like looking at the full journey across the whole board. And so that's where you know, a lot of people are turning to like data warehouses or lake houses to consolidate and rectify that and reconcile all of that data. And so when you have all of these different sources and you're bringing them into the warehouse like you can some people use, like CDPs, and they, you know, have a variety of different things going in, but it's kind of this black box per se. And you know, what I like to make sure people understand is that if you're really looking to try to get data you know quality data and you're looking to get that single source of truth, you need to make sure that you have an understanding of where it's coming from, how it's being modeled, how is it being enriched? Getting that context, where is all that information coming from?

Speaker 1: 16:36

And so you need to have that foundation and that platform that can do that, and so yeah, transparency is really critical, right, so you're not spending your time explaining how this stuff comes into there.

Speaker 1: 16:50

Yeah, and to me, I hear a number of people and I probably said it myself where they say like, oh, we can't start doing that kind of reporting because our data is terrible.

Speaker 1: 17:01

And I kind of keep coming back to this, my view, right, that it's never going to be perfect.

Speaker 1: 17:04

So what I've found when I start doing reporting even a little bit going to be perfect so what I've found when I start doing reporting even a little bit is that it uncovers the things that you didn't expect or your assumptions that were incorrect about how a process worked or people worked, and it gives you a chance to go clean that up right, and then, like your data over time gets better by exposing it and making it available to others, if we can move on a little bit. So at Snowplow, you see you guys work with a lot of marketing organizations and kind of see how they approach data and insights and all that. When you think about data that marketers or marketing ops teams could potentially get a hold of because we talked about this, maybe you don't need everything, um, and capture that and do something with it, like what are the, what are the major categories you're thinking about, and then you know what are some of the like challenges that you see with those yeah, sure.

Speaker 3: 17:59

So you know I I do look at it across like a few different categories. I'd say so I. You have, like, your behavioral analytics, which is you looking at the interactions that people are having. You know your touch points, your standard, like clicks and page views, or maybe even video viewership, and like how long you view something, for you know how long, how much scroll depth, if you had on the page those types of things. So like, I encompass that into your behavioral like analytics. And then you have your targeting and segmentation, so like how are you slicing and dicing that data to then target an email or target your advertising? You know, and segment that out, you know. Then, third is probably your personalization. I'd say you know that come. I say that lightly because personalization can be taken in a bunch of different ways. Right and um, you know.

Speaker 1: 18:53

Isn't that just putting someone's first name in an email?

Speaker 3: 18:58

That's what everybody seems to think, at least.

Speaker 1: 19:02

How hard can that be?

Speaker 2: 19:04

It's the most painful thing.

Speaker 3: 19:07

Or you end up getting those emails that still have the token in place. You know where it's the first name, oops.

Speaker 2: 19:14

We call that a moops.

Speaker 3: 19:15

Yeah, um, but yeah, so you know, like you have your, your, your, you know your personalization. And when I say that, I mean like you know when somebody does an action, taking that data and, um, you know, doing something with it to give them a better experience. So you know whether they do something on your website or app or whatever, making sure that the next thing that they do, you're meeting them where they're at in their journey. So you know whether that be an email that is triggered because of an action that they took, or maybe it's it is content that's recommended to them on the website in real time, you know. So that's another aspect. And then you have your optimization. So taking, you know and analyzing all of the data that you have and optimizing your ad spend or your website performance, making sure that it's, you know, more optimized for conversion, you know. Looking at that CRO, you know all of that information. You know. Looking at that CRO, you know all of that information. So the biggest hoops that I see across, those kind of four pillars I guess you'd say is like, from a behavioral analytics perspective, it's you know. What do you track? Can you make sure it's deduplicated so that there's no, like you know crazy amounts of clicks on something, maybe bot traffic, all of that stuff.

Speaker 3: 20:37

From a targeting and segmentation perspective, you know, some of the big, big hurdles I see is making sure that, once again, you've set up all of your, your models or your fields accordingly so that you can segment out that data. So like, take, take a title, for instance, of somebody at companies have their titles in all different ways. I mean, recently I saw like somebody at a different company but the same title. It's like one is, let's say, director of business operations and another one is an SDR, but they do exactly the same function. It's just that one company is giving like a larger title for you know, something that you would typically see like a director, is like an actual director role, but at the company they've named it differently so that they can show like this ownership aspect of things. Or maybe they put in different keywords or ways of segmenting whether it's MarTech, is it marketing technology? Is it MarTech, is it? You know how do they word things. And so being able to figure out how you're going to slice and dice that data and categorize it is really important, and I think that's the biggest thing that people struggle with with that category.

Speaker 3: 21:51

And then personalization.

Speaker 3: 21:52

I'd say you know how to not be creepy but also be helpful. You know, you know like, for instance, when you're you're shopping right and you have a brand that you know maybe you're shopping for a couch and then you see an ad that is advertising a cup holder like cover for your you know arm of your chair, of your couch chair. You know it's like like, oh yeah, I need to deck out my couch with throw pillows or this cup holder that maybe I probably don't need to spend money on but I probably should because I love shopping, or maybe this throw blanket that matches the colors in my room or whatever. And it's like how can you personalize the advertisement or the email that you're getting and things like that? And you know AI can come into play there too, where it's like analyzing what you're looking at, the color schemes of the images that you're looking at, you know what are the sizes of the couches you're looking at. You know those types of things and play that into the recommendations that you're getting to make it even more helpful.

Speaker 2: 22:53

Add into the recommendations that you're getting to make it even more helpful, see, and that stuff really matters, right, like you know on when you, when you say, say, you know things like the sizes of the couches that you're looking looking at, or the style and all that stuff, right that? I got to spend just a brief, a brief stint at a digital asset management company and I learned a lot about taxonomies and, um, the way in which assets like that are. You know the meta data that's put in on on those products, right? Or? Or the images and all that other stuff, um, those standards the process by which you establish a taxonomy and a standardized approach to storing information has been around for quite some time and, to our point in this episode, so far, b2b, for whatever reason, just missed the boat. They missed the boat when it came to setting, like, taxonomies and data standards, and so you know your AI, like to your point, right? Yeah, it can do a great job, but if you didn't have that metadata on file for this couch is X amount of inches long and wide and deep, and blah, blah, blah, blah, it's never going to be able to do that Right, and you have to have those standards in place for these systems to actually do anything of real meaningful value, which is what's kind of like.

Speaker 2: 24:26

Again, I, you know, said earlier it's like exciting to be in this space right now. That's a really exciting thing is that you know marketing operations, revenue operations, whatever title speaking of titles, whatever title you want to give yourself, you are just in this really awesome place Like everybody's like, hey, I want to be seen as a more strategic, you know, team player and all this stuff. Right, I want to be pulled into the conversations. Earlier, it's like what's the phrase? I feel like I should ask the Texan in the room Grab the bull by the horns. I don't know if that's from Texas or somewhere else.

Speaker 1: 25:03

We'll take, we'll claim it.

Speaker 2: 25:04

You'll claim it, but it's like grab that you know by the horns and run with it, right? Because you and this is a talk track that's been coming from Paul Wilson in our community, Like it is not me, it is all of us talking about this you have a huge opportunity to show your company and your teams the art of the possible by saying hey, by laying this really solid foundation in the way we think about our data and our data architecture is going to enable us to do all of this other kind of stuff with AI, Because the AI is only as good as what's being fed into it, Right?

Speaker 3: 25:43

So and there's where that aspect of garbage in garbage out comes from. You know, like you know you're putting in you know garbage, taxonomies and things of that nature. Well, guess what the recommendations are going to be like? Oh, you're buying a couch. Well, let's show you a microwave. And it's like well, that's less than helpful, like I don't see.

Speaker 1: 26:03

Here's what I believe, now that we've all been talking about couches, is that these devices are all listening to us. We're all going to start getting ads for, for couches and chairs that is true actually.

Speaker 2: 26:12

Uh, that is 100 true. I'm for sure going to get some sort of ad for a couch now.

Speaker 3: 26:17

Yes, because yeah maybe I should have talked about, like you know, cars or something fun no, the couch is more suitable.

Speaker 1: 26:27

Like I'll take the couch yeah, I mean I can think of lots of other things that would have been more problematic.

Speaker 3: 26:37

Let's put it that way.

Speaker 1: 26:39

Yeah, for sure.

Speaker 3: 26:41

Well, I'm glad that I could do something that you guys are doing.

Speaker 1: 26:43

For sure. Okay, so you hinted at this a little bit, adriana is the idea of operationalizing data and maybe like data as a product, which, mike, I'm sure you'll like, because you keep talking about how the marketing ops is really kind of like product product management. But, um, you missed a guest actually, I think, recently, where I was like, oh, my mike would have been here, like when I told you so, uh, that's okay I'll say yeah, so so, adrian, like you, you use that phrase like operationalizing data as a product, right?

Speaker 1: 27:19

So what does it like I've been. What does that mean? Right, I think I? I have an idea, but I could be just as likely to be right or wrong.

Speaker 3: 27:27

Yeah, yeah, you know, when I think of data as a product, it's like a long-term, like valuable asset for your company. It's something that requires continuous development, maintenance, refinement, making sure that it's getting the same level of attention time resourcing that you would look at as a product. You know right, because ultimately you're using that data to steer your business and where you're going and making decisions off of that, and so if you're not, it's not a staked in time type of thing. It's something that's always evolving, always adding to it. You know all of those things, so I'm actually kind of curious, mike, to it. You know all of those things. So I'm actually kind of curious, mike. You know data as a product. What are, what's your take on it? You know, like what? You seem a little excited about that as a topic, so I'm kind of curious.

Speaker 2: 28:32

Yeah, I mean. So data for me. So there's, there's two things. What Hartman was talking about a second ago is that I'm a big advocate for this notion that when I did a post a handful of months ago and I said marketing ops isn't marketing and everybody was like, what are you talking about? A lot of it was really around the idea that we do need to create a little bit more of a guardrail around, like let's not just lump it into the broader category of marketing. Let's think about it as what its unique value really is. If you want to be seen as strategic. That's like let's talk about it differently, let's not just talk about it as broad stroke marketing.

Speaker 2: 29:02

And then I have further expanded upon that point to say that really I think you're, in a lot of ways you are a lot like a product manager. Your product is just made up of many products and your job is to figure out how to deploy features and functionality to best enable your customers to buy from you and your team to engage with those customers all at the same time, right? So there's that part of it and I'll come back off of my little pedestal there. But the on the data product side, um, I love the idea of data products, uh, and it's near and dear to my heart because I run a community that's just like filled with great information, right, like we have tons of data coming into our system and I'm constantly looking for ways to enable our community with that information. Yeah, you know so you think about us as like this sort of for lack of a better term the buzzword of like a thought leadership brand, right, so marketingopscom.

Speaker 2: 30:09

If I, just by way of like really concrete example, I tasked somebody recently with hey, our job board is kind of broken right now.

Speaker 2: 30:19

You gotta fix it and then posting jobs like just like we gotta fix that part, but like let's go fetch some job data that you know and like just feed the board because that'll help people find new career opportunities, like that's a win.

Speaker 2: 30:33

And then I went further with that person and I said but as you do this, can we store the information in our own data tables so that I could start to tell a story to the community about which industries are hiring or not, what's the average tenure, what kind of information of value can I point back to our community around the data, around the job market? That is a data product that is its own little tiny niche category within the niche of marketing operations. And so that's what excites me about data products is when you start doing industry research, like we do State of the MoPro and you've got four years running of the same type of questions happening over and over again, you've now like, yeah, the same type of questions happening over and over again. You've now like, yeah, the research is a product, but the data that supports it, that's a data product. Right, and I can repurpose that and create more value back into the market by treating it like a first class product Right.

Speaker 3: 31:32

So, yeah, it's about like compiling it too. You know it's like where, where are you getting that from? And you know, ultimately, like, what are you trying to give to people?

Speaker 3: 31:42

You know, like, what are you trying to do? But yeah, it's like you know, from my perspective, it's like you know you want to try to create like a structured process. And you know, like you said with product management, you know it's like everything is about kind of process. And you know, like you said, with product management, you know it's like everything is about kind of process, like how are we bringing this to market? What is? You know what's going into the product, what is the data that you're going to be using to understand what you should be building and and things of that nature.

Speaker 3: 32:11

You know, like my favorite analogy is that is like shelfware and and there's a lot of tools that are out there that you, you know I call my products. You know it's like because every tool you have to kind of manage as its own own thing and then bring it all together and reconcile it. And there's a lot of shelfware out there because you know a lot of companies, you know they have one marketing ops person or maybe just a few, and there's so many different things that you're managing. And so when you think about shelfware, you know it's like it's sitting there, it's collecting all of this information and but you don't have anybody to operationalize it or to actually like take it and do something with it. Um, and so the reason I like to look at data as a product is because you don't want it to just sit there and rot, you know because this is exactly what I was alluding to the beginning.

Speaker 1: 33:07

Like, this is the missed opportunity. Right, you've got this data that could be valuable and, um, for a variety of reasons. Right, some of it's just lack of time, lack of resources, lack of the like tools and knowledge and expertise to do something with it. Right, all those are play a role in it. But at the end of the day, right, you've got this data that could be valuable to the organization and you're not not getting to use it. This is is why I did a white paper about marketing B2B measurement for the community, and operational data was the first part of it for me, because I think what happens is nobody sees the impact of poor operational data systems and processes until it's downstream and it's too late to do anything about it. So we just had another guest on.

Speaker 1: 33:58

Simply, it was like I want to be able to quickly answer. Ask the question to my ops team right, how many customers do we have? And I was like I cringed because, like, that's like the hard question to answer in almost every place I've been. Because, personally, because there's no definition, right, is it a person? Is it a company? Is it a location of a company, is it a division? Like all those things, all like so, unless you have a definition. But then like, just, it's like, it's not there the way you want it. So the more you can clean up like this.

Speaker 1: 34:31

What I was trying to do is help people sort of build a case for I need to as a marketing ops MarTech, whatever RevOps person I need to be able to. You know, I need to carve out part of my bandwidth to not just doing things like building emails and doing segmentation, but also making sure our data operations are working as well as they could be, or at least continuing to make progress towards getting better. And because it's, I told the boss of mine one time like we really need to work on this. It's not sexy. She's like no, it's sexy. I was like, oh, like. I was like ecstatic right, okay, good, you see it too. But I don't know that most leaders see it that way.

Speaker 3: 35:11

Well, most people see marketing operations as like a tactical thing, and if you're not using marketing operations as a strategic thing, you're going to be in trouble long term. I mean, I putting my putting my marketing operations hat on. You need to like almost be futuristic and like try to plan for all the things that you need to solve for in the future when you're implementing something. Right. So you go and you get tasked with this thing, but they're like, why did you set it up that way? Well, someday you may want this from it, someday you may want this from it.

Speaker 3: 35:42

Someday you may want to do X, y, z, and so we almost have to put this crystal ball hat on to understand what could we possibly be asked in the future to create or do with this thing and setting it up from the beginning so that we have that flexibility and that scalability really, I was like trying inside of our recording studio here for our listeners.

Speaker 2: 36:08

There's these little like media features you can do. I was like trying to find the clapping thing.

Speaker 1: 36:14

I was like yeah, I wanted, I wanted to.

Speaker 2: 36:16

We don't ever use them, but I wanted to do it and it's not working.

Speaker 1: 36:18

So anyway, imagine, sounds like, sounds like I'll bring her here, mike, I don't know imagine cheering and clapping sounds in the background.

Speaker 2: 36:26

For that one, I love it okay.

Speaker 1: 36:28

So I'm going to challenge you a little bit here, adriana, because what you just described I agree with like we should be thinking in the future, but at the same time earlier we talked about the importance of thinking through, like, like mike said, you know, begin with the end in mind, right, and so you know, how do you? Do you have any, you or your team at snowplow? Do you have any guidance or suggestions about how to help our audience who might be going? Oh yeah, we need to. We need to define what data points we need and how to get them, or maybe prioritize, cause we only have limited bandwidth so we can only address part of it. No, like, how do they go about that? Because otherwise I think it does lead to oh, we just need to go get this massive amount of data and just suck it all in somewhere and then it just sits there.

Speaker 3: 37:14

Yeah. So, like I you know I typically take like a tiered approach, I guess you could say and trying to understand exactly like what types of data because you need, like your, you have different levels. Right, you have your like key business objectives. You know what do you need to inform. You know you, your revenue, your, your opportunities, let's say your. You know sales qualified leads, your marketing qualified leads, you know how many prospects entered the database. You know all those things. Those are kind of like your high level, like KPIs, or maybe it's purchased, like for a B2C company, maybe it's the purchases or the return purchases or the returns that happened on a purchase. You know all of those things. So, categorizing as like what's your first priority of data, like from from a top, you know executive level, and then taking it. Then you go more granular, like what caused those MQLs to go into the funnel. You know what make up those MQLs, what made up that purchase, where did they come from? What was the source, the medium, the, you know the action that they took, is it, is there wasn't an event that they, you know, went to? Was there a um, a sale that you had on your website? You know what was it that ultimately got them to convert, and defining that. And so that's from a marketing perspective. But from even a product perspective, you could look at it from a standpoint of you know what actions are they taking within the product in order to get them to repeat purchase or to become a loyalty member or something of that nature, and then going even a level deeper from that and you know it's like, what are the different roles that are doing these things? What are the psychographics behind that? You know, like are they? You know and you can, you can look at kind of their intent and the way that they're coming in and maybe they're more of a.

Speaker 3: 39:13

You know, I had a company that I used to work for. It's like some folks would come in from our advertising, from like a draft Kings or a, you know, like an online app or within their personal life, like, let's say so, like the things that they're doing within their personal life. They'd click on an ad because then and then they go to the business website. But then there's some people that will come in from, like, social media, or there's some people that will come in from a, an article that they're reading. There's different ways that people are consuming this information. So if you can segment out those, those different types of behavior and categorize them around kind of how people are consuming that information, you know that's like a whole nother level.

Speaker 3: 39:56

So it's like if you don't have that top layer information to know overall, how is the business doing, what is? How are you doing in order to grab that information? And is it the right people that are coming in as MQLs? Because you know a lot of marketing and sales teams. They struggle because it's like marketing is just like well, here you go. You know, and for our listeners you know you can't see me, but I'm tossing something over a wall you know it's like well, here you go, here's your stuff.

Speaker 2: 40:25

So true.

Speaker 3: 40:29

You know and and so then it's like the head sales you have to try to, you know, make something of this. But it's like, well, are they truly the right people? And then you're remarketing to these people. So it's like, as you're targeting upfront, you know the, the accurate targeting, you know are you really reaching the people that are going to buy? So it's like you have to kind of chicken and egg, you know. So it's like you have to kind of chicken and egg, you know. So it's like if you're bringing in MQLs and they're not the right person, you know, and then you're retargeting them so that you can try to get more purchases out of them, and it's just hitting a stall. You're never going to get anywhere.

Speaker 1: 40:59

So it's this, you know this aspect of trying to understand the beginning, the horrible cycle of, you know, I just hear like a toilet flushing right, and then I'm going to tell you there's money, right, it's just going away.

Speaker 2: 41:14

Yeah, totally, and I honestly, uh, I think it just further emphasizes the point. So I was on a podcast yesterday and they were like asking me the question like when is it? Is it ever too early to hire a marketing operations person? I was like, well, obviously I'm biased, but generally speaking, no, right, and like this, this illustrates the point. Right, if you, you know, if you are a leader trying to go to market and you want to put emphasis on moving fast, you might hire someone that is more demand gen oriented, that has perhaps a background in marketing operations, but they're going to skew heavily towards the demand inside, which you're going to love because you'll be like, great, we can move fast and try things and try to generate leads and that's going to be great it's highly visible stuff, right stuff that's going in the market, emails as like.

Speaker 2: 42:05

Oh I see like they're doing the things right. But if you hire the marketing operations person or maybe that person skews more on say, hey, we do need to slow down to speed up, because by the end of beginning, with the end of in mind, right by the end of this outcome of this test or this audience that we're trying to get purchases from, or what have you you don't want to end up in a place where marketing is pointing to sales and sales is pointing to marketing and they're both saying that one isn't doing what the other is supposed to be doing, et cetera. Because no one sat down and agreed that this is how we want to define what success looks like and we didn't put any of the data in place to be able to do that. Yep, right, and so slow down to speed up. Folks. Sorry, if you're a demand gen marketer and you wish that you were the first hire, like go be more of an ops person and slow down to speed up right.

Speaker 1: 43:16

yeah, well, it's kind of like how um, maybe this is a counter example, but like I always like I hear, right, I hear regularly about the value of having things like habits or simplifying the outfits you wear things like that because it makes your ability to be people will take the like having habits. They think, oh, it takes creativity out of what you do, but it actually has the opposite effect because you're not thinking about things that are day-to-day decisions, that you don't have to think about anymore. It leaves more capacity to be creative in other realms.

Speaker 2: 43:57

Yeah, I mean, why do you think I'm so creative, hartman? I wear a hat every day, like I have way more time to be creative. Right, exactly, for sure, I'm being facetious people, right, exactly, sure, I'm being facetious people. I'm being facetious, but I do have like a rotation of five shirts that I wear to be fair they're all black.

Speaker 1: 44:18

No, they do change colors, at least.

Speaker 2: 44:22

They change colors, but they are plain colored shirts from Tarjay, so you know easy stuff.

Speaker 1: 44:28

Well, hey, one more, one more thing thing, and then let's we're probably gonna have to wrap up here, but, um, again, kind of going with the experience and exposure you have at snowplow with you know many clients, um, are there any? Are there any? Stories of? Mike will know that I hate the term best, but best practices or examples of where you see people be successful at doing this. Operationalizing data. My God, that took me how many times to get out.

Speaker 2: 44:57

I don't know. Operationalizing that's a hard one.

Speaker 1: 45:04

I speak too fast.

Speaker 3: 45:05

Yeah, speak too fast. Yeah, yeah, there's a few different you know areas that I'd say are good examples. You know, I I think there's two that I like to share the most and you know, don't get me talking on this, I'll talk forever. But it's like you know, optimizing your ad spend and optimizing, like ad tech platforms, with the data that you have. That's one of them, and then the other one is like the personalization aspect of it and kind of that real-time personalization. So I'll talk through like the ad tech area for a second.

Speaker 3: 45:42

Is that you know a lot of companies. They have their Google Ads and like Google Spend, right, and they're tracking you know how many conversions they get. Is that a form submit? Is it a checkout? You know, on a e-commerce site, Um, is it a demo request? On a B2B site, Um, they have those, those typical conversions.

Speaker 3: 46:03

And you know, we all know that we get spam submissions on a variety of things.

Speaker 3: 46:09

We get bots that come to our website and so if you keep it at that high level, you know those high level conversion points, Google is going to optimize off of all of those you know, regardless if they're good or bad, and so I'm a big advocate for connecting, you know, connecting that data into your ad platforms and funneling as much data and conversion points into those ad platforms that you can from top of funnel of, like you know, did they click on a certain section on your pricing page to look at like a particular section, or all the way down funnel to did they schedule a meeting with somebody or did they add more than one thing to their cart, or, very granularly, all of those actions that you would say lead to a successful journey.

Speaker 3: 47:04

So once you have all those data points that are in somebody's journey that you would say are valuable to your company and make sure that they're optimizing the spend because they know exactly who you want to come through. And so the more that you have those data points and not just the arbitrary things that are very, you know, very down funnel, but everything, the more you can put that in, the more it can optimize.

Speaker 1: 47:47

I think your point about like Google doesn't really by itself, differentiate right between those visitors is huge, and so the second one you talked about was oh gosh, now I'm going blank already.

Speaker 3: 48:00

Real-time personalization.

Speaker 1: 48:01

Personalization yeah.

Speaker 3: 48:03

Yeah, so, from like a content recommendations perspective, you have like somebody comes to your website and there's like a lot of different platforms that that try to do this. Is that somebody comes to your website, they, they try to understand who that person is, you know, are they valuable to you? And then they try to serve up. Is a demo, is it a chat with like a certain message? Is it a pop up with a certain sale? Is it a piece of content that they need to read, or like a webinar or something of that nature?

Speaker 3: 48:34

And so, all of that being said, you need to make sure that you have that real-time aspect of things to then get it into somebody's, into a platform's hands, to be able to ingest that and operationalize it. And so there are several like e-commerce, gaming and gambling companies that are doing this type of thing. You look at some of our customers like Burberry or Charlotte Tilbury or those are a lot of like e-commerce platforms, or Strava, for instance, from a fitness perspective. So you look at those companies and they're really trying to do the best they can with their data, you know, operationalize it as well as, you know, optimize their spend so that they're they're becoming more efficient. So yeah.

Speaker 3: 49:22

I'd probably say those are the two most interesting use cases that I love it.

Speaker 1: 49:26

Yeah, that's great, adriana. Thank you so much for this. If folks are interested in learning more about you or what Snowplow is up to and how it might help them, what's the best way for them to do that?

Speaker 3: 49:37

Yeah, so feel free to connect with me on LinkedIn. Happy, happy to chat. And then also go to snowplowio, check us out and feel free to reach out to us. We're always happy to chat and answer any questions you may have.

Speaker 1: 49:49

Fantastic Again, adriana. Thank you so much for sharing this has been a fun conversation. I feel like we could have gone on for another hour. Mike, as always, good to have you here.

Speaker 2: 50:01

Glad to be here, thank you.

Speaker 1: 50:02

I know you've got to run soon so we'll let you go, but, as always, to our, to our listeners out there, thank you for continuing to support us and giving us your input. If you have suggestions for topics or guests, or you want to be a guest, then just reach out to Naomi, Mike or me and we will be happy to talk to you about that. Until next time, everyone. Bye.

Speaker 3: 50:25

Thank you Bye.