Speaker 1: 0:23

Hello everyone, welcome to another episode of OpsCast brought to you by MarketingOpscom, powered by the MoPros out there. I'm your host, michael Hartman, joined today by well, just our guest who we'll get to Joining me today is Deirdre Mahon. Deirdre is currently a product marketing and growth advisor with Superset, advising their portfolio companies. She is a full-stack marketing leader with hands-on experience across growth startups and public companies. Particularly strong at executing against strategy and driving the tactics to exceed goals. She has been instrumental in the creation of new market category segments, including observability, real-time data, movement, cloud consumption, analytics and big data. And, as she said this is not me making this up, she said this she's probably been acquired five times. So, deidre, thank you for joining us today.

Speaker 2: 1:10

Thank you for having me and lovely to be with you, Michael.

Speaker 1: 1:14

Yeah, yeah. Well, we were enjoying a nice chat about rugby before this, so we better get back to business. So, for our listeners, we're going to talk a little bit about a number of different things, but really it's going to be centered around the idea that AI needs human intelligence. We'll get to there in a bit. So, deirdre, during your career, you have worked mostly in marketing and leadership roles at early stage companies, some larger companies, and you've worked closely with ops professionals to stand up that part of the marketing function. So, you know, I think it would be useful for our listeners, before we get into the main topic, to kind of get your perspective as a market leader on what you see. As you know, what is a benchmark kind of marketing operations function compared to others? What do you look for in those roles?

Speaker 2: 2:11

Yes, by that question I'm assuming, mike, you're talking about what is a good starting out marketing operations look like from a human side and otherwise right.

Speaker 1: 2:25

I mean, I think you could take it wherever you want. But yeah, I'm thinking about, I think there's going to be probably two main categories, right. One is starting it from scratch, which I think some people get to do most, don't, uh? And if they do do it, then they're, you know, they're juggling everything, yep um, and then maybe a little bit of overtime. And then others who are probably in sort of a role similar to mine. Very often, even if I was starting something, it was partially in progress, or I inherited something-series A, and you're right, sometimes you know somebody else and they could be a sales or some go-to-market human has, like, at least got HubSpot licensed, but I mean that's just the very sort of minimal viable.

Speaker 2: 3:25

So when I think of a true functioning marketing operations practice, I think first things is the engine. So what is your engine that you're actually going to execute? And the obvious thing is what's your marketing automation system? Most people on the call today are probably either using HubSpot or a close competitor, hubspot. We love them because they've actually helped younger companies get on the engine platform as quickly as possible and not super expensive. So I personally do like HubSpot.

Speaker 2: 4:07

Then the second thing is OK, do we have an agreed upon set of process steps? And you know, if you have a sales team in place or like some small team that's actually actively selling and engaging customers, then that's awesome. So you have a partner that you're going to work on defining those process steps. And one of the most important things to get right out of the gate is what's your model? So are you a sales led? Are you product led? Are you hybrid? Right? So your process steps should be directly mapping to how people engage and buy. And then the last third piece and then I'll touch on the people side of it is. The last piece is do you know what you're actually going to measure and do you have an agreed upon set of KPIs or goals? And how are you going to divide and conquer and co-own and have accountability for those numbers? So that's where, hopefully, you're going to sit down with your sales leader and your person in finance. Based on the model, figure out what those numbers look like okay, I was gonna.

Speaker 1: 5:20

I was gonna ask you if you met goals and metrics for the marketing I've seen, for marketing or for the organization, maybe the revenue engine, if you will. But I think you just by what you just said there, answered it that it's really more for either all marketing or all of the marketing sales functions.

Speaker 2: 5:40

Yeah, Just to touch on your point about you know goals and metrics for the ops team. A lot of times, especially in the early days when you're standing up the engine a lot of times it's like, okay, can you deploy and get this new tool or system or process in place by x?

Speaker 2: 5:59

date train and enable other people on the team to be comfortable and proficient with using it. And also you. You know you immediately as soon as you're buying any tech system, you're immediately on budget topic. So as a team, have we carved out enough budget to do all that system engine and tracking stuff? And sometimes that's like, oh, it's third level of importance, because oftentimes people are thinking like, how are we going to put money into campaigns and programs so we can get the word out and start campaigning and you know outbending? And you forget oh, don't, we need some budget for the tools and the process steps and the onboarding. Need some budget for the tools and the process steps. On the onboarding.

Speaker 2: 6:50

And I will say from a people side, when you're young and you have a very small team and there's maybe two people, doing this who have other jobs as well, by the way, that you often are like a little drowning even though the rest a lot of times, sadly, the rest of the organization is going. You're just building process and engine. When are you going to actually do some marketing Right? So you really have to sort of strike that balance and I highly recommend all the listeners to like invest early in the engine because you don't want to be going back and fixing it later. Get the foundation right and then, from a people standpoint, pull in some specialty contractors that can, you know, be a catalyst and fast forward. Just make sure that you pick the right people. You know you don't want to break your budget, but it does tremendously help if you have a younger, newer team that doesn't know all the moving parts to all the tech, that doesn't know all the moving parts to all the tech.

Speaker 1: 7:48

So just curious, going back to the people, especially as starting up a new function, would you look for someone who's a little more of a generalist and maybe not an expert in, say, a marketing automation platform per se, but has the skills to do it? Or do you look for I need the person who's an expert in just use HubSpot as an example, since you brought it up but the expert in HubSpot who has potential to be able to do the other things and support the marketing teams and be able to be an advisor to those teams.

Speaker 2: 8:24

Yes, good question. My bent is towards the generalist because when you're a small team wearing lots of hats, if you hire only a specialty person that knows only one thing, then you'll end up you know you yourself will end up picking up all the other pieces.

Speaker 2: 8:42

So I want to hire somebody who's a generalist. If that person has some knowledge and experience with HubSpot out the gate and has an appetite to learn and grow, which, if I'm honest, most marketing operations folks that I've worked with in the past. They have ambitious careers, so that's awesome. I have worked with folks that are really comfortable with data and really comfortable with in the weeds. That that's more important. If that individual is comfortable doing that and then you're on a good path and easily show them the growth trajectory out of that.

Speaker 2: 9:21

They're not going to be in the weeds forever, but they have to be comfortable with that. Yeah.

Speaker 1: 9:25

I've rarely had the opportunity to really build a team from the ground up and I think early on I would have said I need somebody who can run the engine, call the marketing automation platform, and be able to fulfill needs like building emails, getting them deployed and that kind of stuff. And then after that I would probably want someone who can take some of that like split it up a little bit, but I think I've gotten to the point where I'm glad you brought up the analytics piece, because I think I've shifted a little bit that I think a second hire would need to. I'd want somebody who really could understand data analytics and help us get insights, because I think there's a lot of potential leverage from that that you don't get if all you're doing is executing on campaigns and not to say those aren't important. But um, at least up until recently and we've had some interesting conversations with guests and vendors and stuff like that but um, I'm just like I just that reporting analytics piece is such a unique.

Speaker 1: 10:28

I don't think there's a deep enough skillset in the pool of talent out there, nor do I think it's a. It can be just built on top of reporting tools, right? I think there's an effort that's required to do it well. So it's interesting that you bring that up to that as well. So that's just because I do think it's a really, really valuable one that's under appreciated by other marketing leaders I've run into yeah, 100 the.

Speaker 2: 10:55

The reality is that marketing has a ton of data, right? Our systems are naturally gathering it all, and that's from like website traffic to conversions and engagement on your HubSpot embedded forms, to then how it downstream converts in the funnel. And first of all you have to know the process and the model, and then you have to start collecting the data and start having some benchmark or baseline, and only then can you figure out where gaps are or stuckness or like, without having exposure to the data and a comfort around how to even read and deduce from the data, then you really can't move forward.

Speaker 2: 11:42

so, and that's number one and number two, like the marketing team has to get really comfortable with that and then you have to be able to sort of translate that to other parts of the business so they understand, because if you drown them with too much information, too much data, that just glays over. Yeah. So you have to pull back and say, okay, here's the most important things sales leader or CEO. So getting really comfortable with data and understanding it is critical in my opinion.

Speaker 1: 12:10

Yeah, well, and I like the second part of translating or the storytelling part of it, exactly Doing the analytics, all right. So you asked me the question back at me about what kind of organization we're talking about. You talked a lot now about the startup world, like do you, in your experience with larger organizations, like, what do you see that's different in those early stage kind of companies and what they're doing from a marketing, marketing app standpoint, and those ones that are, say, medium, larger, maybe even enterprise?

Speaker 2: 12:43

Yeah. So the smaller stage is like you're going to get your MVP right, get the engine built, and then you start to execute and you start to measure. And I always think of it in a one-two punch way you invest, you execute, you measure, and the shrinking of the time between those two things is critical so you can move forward. Two things is critical so you can move forward. When you're further along, hopefully, you know what a good sales cycle looks like and your goal is to get it repeatable and to make sure everybody's on the same page with your understanding of here's what a repeatable looks like. And then you're probably going to be like okay, how do we scale up? So, from a marketing perspective, your operations teams are, besides all the execution that they have to do and focusing in on the things that work and measure.

Speaker 2: 13:38

You're now figuring out how do I do it at scale, because it's always up into the right growth yes um, and so in case, what usually happens is you actually stop being more of a generalist and you start being more of a specialty expert. So there's, like I've seen different ways to skin this, but sometimes you have a dedicated team that is only focused on top of funnel, and then you have another team that's focused on like sort of of funnel, and then you have another team that's focused on like sort of mid funnel.

Speaker 2: 14:11

And then maybe even somebody who's working really closely with product. If you're more of a self-serve POG and you're more sort of in the bottom of the funnel, which is all about usage and adoption and expansion and renewals, right, Very classic POG.

Speaker 1: 14:28

Some customer marketing in some places would be yeah, exactly.

Speaker 2: 14:31

Because you're further along and you have existing base and you have to keep them happy and learn from that and upsell, cross-sell and so on, references and et cetera.

Speaker 2: 14:41

So you end up being like I like this sort of demarcation between am I a generalist or a specialist. So now you're probably more of a specialty and you're focused on a particular part of the business or maybe a particular segment, or you're working with the enterprise sales team directly to help them, versus maybe self-serve side of the house. So ultimately, at the end of the day, you're honing and tuning what's working so that any additional marketing dollars that you're pouring in, that you're being really efficient and spending where you know that there's going to be a growth at those different buying cycle stages. Now you know every week when you do your weekly business review. Now you know what is your expected goals and KPIs and how are you mapping each week to those. So each of those dedicated teams can really get much closer to the problem and make recommendations on what to do to engage, whether it's top, middle or bottom of funnel.

Speaker 1: 17:03

So one of the things I've experienced and I don't know if you've seen this is I think this is a real challenge. As teams get larger, you think, I think intuitively, you're like oh, we can multiply the amount we can get done, but I've found that that multiplier is much smaller than we think it is because there's be there becomes inefficiencies in how things get done, because now you're, instead of you had the generalist who was doing doing the campaign, uh, definition, defining stuff, creating the, the creative, you know, developing the creative, developing the content, um, now that's all done by two, three, four different teams and who are kind of, uh, working in different. You know parts of the process. How have you a I mean, am I the only one who's seen this or you just something you've seen? And b, if you have seen it, how would you think about trying to minimize the impact of that inefficiency that could creep in in those organizations?

Speaker 2: 18:07

Yeah, I mean getting everybody on the same step. One is are we all aligned on the strategy and the plan? Is everybody intimately aware of what our KPIs and goals are? And, as one team, you sit and look at those every week and you decide where do you need to, what do you need to stop doing or what do you need to keep on doing. So alignment is critical, and it's not just alignment among that full marketing team, but also alignment with the sales team as well. And the other thing is and I know we'll probably get into it in this discussion which is what are some of the manual, time-consuming, repetitive tasks that are happening and taking up all of your precious time? That you could actually start using technology and agentic AI-type technology to speed up the cycle. And the part of I think the biggest challenge with marketing is you're. There's so many. Everybody works so closely and collaboratively.

Speaker 2: 19:07

There's so many interdependencies and this has to go on. Then this this is a cycle or a natural workflow to what we do, and I I'm pretty sure, because I've seen it firsthand and worked with companies what can I parallelize and do in concert?

Speaker 1: 19:25

and ai can definitely help there yeah, okay, yeah, I know that was just something that popped in my head because I've, like I I'm sure somewhere, like we've probably got a mix of listeners and viewers who work at smaller organizations and probably, if they've never experienced what it's like in a bigger team, how it can be sometimes frustrating, like why can't we get more done faster? And it's kind of like we get in our own way in a lot of ways.

Speaker 2: 19:54

Yeah, too many meetings to hypothesize what went wrong without having access to the right data set or knowledge. So the one thing I will add on a bigger team, that specialty thing and everybody sort of gets deep dive and narrower is that a lot of times teams HubSpot isn't doing it for us and we actually need to pull data from all these other siloed systems, whether it's our finance or our sales system, and so now we need a data warehouse so they will get Snowflake, Stig, Tableau or Looker on top of that, and then what happens is you will slow down because now you have to hire some data scientist or engineer type person to go figure out how to use that, stand it up and share reports.

Speaker 1: 20:42

So the more proficient you are with sharing information that may be buried in these silos and just being active about that on a consistent basis like it's the same point about bringing everyone together and along for the ride and exposing them to the critical information they need to make decisions, to do their job yeah, I think in my experience, if I were to diagnose most of the scenarios where I've seen uh, inefficient, sort of across team say campaign, just campaign deployment, development deployment a lot of it comes down to A lot of it comes down to reviewing approval processes that are either unclear or overly conservative, I guess is the way I would put it, and sometimes those come together right.

Speaker 1: 21:36

So by unclear I mean like who's actually the one who can approve it versus who can provide input that we may or may not incorporate right Before you deploy. And I think that to me is like, if I could fix one thing in most places is like try to give more trust and then, uh, to the teams to, to, to do the right thing, and when something happens, to get out the door that is maybe not ideal or has something wrong or whatever, is that you then provide grace to those people? It doesn't mean you give them a total pass, right, it needs to be a lesson from that. But I think I think if there were more teams who were comfortable letting their teams move forward quickly, I think a lot of that would go away.

Speaker 2: 22:27

Yeah, a hundred percent. I usually I'm always encouraging team members to, when they have an idea or a suggestion or a brainwave that will help everybody like proactively bring it up. And I think you have to actively allow time and brain space for that, because I think the problem is you're always running so hard and you're always just executing. You execute your report or you try to figure out where you're stuck or problems you rarely devote time to.

Speaker 1: 22:58

let's just have some white space to talk about something and that's when like let the almost let the quietest person in the room speak up, because they probably have lots of ideas that yeah you know so yeah, yeah, I don't have enough time for that I, I agree, um, and in today's world I think sometimes that's looked on like, oh, you're, like you're not getting enough done right. It's like this focus on being busy, which is not always the best thing anyway, I mart, be smart, busy right.

Speaker 1: 23:30

I'm not prepared to talk about brain science tonight, so today, well, let's. You mentioned AI and agentic stuff, so that's really, I think, what we want to spend the rest or much of the rest of the time talking about. But I brought it up when I was introducing you that you you said to me when we spoke earlier that AI will need HI, which you said by human intelligence, and I know you wrote something about this, I don't remember where.

Speaker 2: 24:02

It was a blog in the early part of the year Yep.

Speaker 1: 24:08

So maybe we can put a link to it in the show notes too, but why don't you walk us through what do you mean by that? Ai needs HI, and what is the HI part of it?

Speaker 2: 24:16

anyway, yeah, that's exactly right. So the first of all, like I think everyone on the call today is probably using ChatGPT, right?

Speaker 1: 24:36

And I will admit that I have just like I was. I've been slow to adopt it, but I've just literally. I was just telling somebody earlier today, just in the last.

Speaker 2: 24:43

Two weeks, I'd say. I've almost stopped using Google for search.

Speaker 1: 24:45

I've just started like take long right. Well, it took me a long time compared to others. I'm sure they're like what You're just now getting to it, but yeah, like it's I was talking to to it.

Speaker 2: 24:51

But yeah, um, like it's um. I was talking to a former colleague recently and it's sort of like, um, yeah, it's my go-to, like google search isn't anymore because it gives you so much more and it's if you're stuck on something, it's a great sort of memory memory jogger. Or if you're frozen on writing something, like there's so many different use cases and prompts. So I'm assuming, like everybody has at least tried it on Now a paid chat. Gpt, then, is a little bit more secure and a little bit more advanced in its capabilities, and this is only one LLM that exists in the market today. So the good side of ChatGPT is that it's actually telling us that we can interface with something just using an English prompt and get pretty rich, relevant response and answers. That's awesome. So I was like, oh my God, the instantaneous of it is letting us know that there's a promise. If I really build out some agentic AI-driven workflows, I can actually really get some interesting work done.

Speaker 2: 26:03

The human intelligence part is that it's just data and technology. It's not going to proactively do anything until you actually design it. So so you have to decide what is it I want to get done, and then how am I going to shape it. What are the sort of the beginning and the middle and the end of this thing I want to do? And that's in your control right, your control right.

Speaker 2: 26:34

The other part of it is that you know it will give you some summary result or answer, but you then have to decide what am I going to do with this additional information or content that I've just created in a very fast fashion. So where you want to place that and how you want to distribute or learn from it is entirely your decision. So, first of all, I think AI is our, it's our helper, it's like in our sidecar working with us. It's hopefully going to be told by us humans and learn over time how to do all those repetitive manual things that I don't want to spend time doing. And then, over time it'll get more sort of sophisticated and, by its nature, get sort of comfortable with your use case. Like Claude is very good at that.

Speaker 2: 27:21

And it will learn your voice and your tone when you start producing content with that. It's just an example of one way, but I'm like, I'm all for stop doing these boring, repetitive tasks with our precious time and use our time to do the strategic, smarter work yeah, I feel like, um, then I've had the same conversation with other people I feel like what?

Speaker 1: 27:47

yes, this will. It may completely replace certain jobs, but I believe that in most cases it won't completely replace them. It will change the nature of the jobs because, to your point, say, a bunch of copy or text that has a bunch of email addresses in it, but they're all over, right, they're not in a structured way. I literally just put it into a LLM thing and said I'm going to give you a bunch of text. It has a bunch of email addresses. Give me the email addresses comma separated, and it has worked. Bunch of email addresses. Give me the email addresses comma separated, and it has worked. Beautiful, yeah, that would take probably hours to do and still, I probably still have mistakes yeah, are you like?

Speaker 2: 28:41

oh, what was that thing in the pivot table and the formula that I have to do in the sheet and then yeah, annoying and then you're double checking your work.

Speaker 1: 28:49

Yeah, it's annoying, it's yeah, so, like I've, I'm now like convinced, like this is if it can eliminate that kind of crap. And then, yeah, annoying. And then you're double checking your work. Yeah, it's annoying. Yeah, so, like I'm now like convinced, like this is if it can eliminate that kind of crap from my day to day. And beautiful or where I've used it now has been no-transcript what we should like, how we should organize it. Was it great? No, was it something that was a starting point? Yeah, and it got me. It got me started and that was the hardest part was just getting started.

Speaker 2: 29:34

Totally. Yeah, I love it for that. I'm currently using a copy editing writing tool, AI tool or program. It's called Copy Compass and it's part of a platform that's provided by markov and it does exactly that. I can either upload something that I might have written in google doc or I can give it a prompt and then I can say like, outline a long form article or blog, and then it'll create it for me in a formatted way and then I can start. But I can highlight text and I can say dive deeper, explain more, make this assertive or actually cite and do references and cross-linking.

Speaker 2: 30:24

It'll do spellcheck plagiarism style, does all of the above in one simple platform, that's amazing. One example right. It's amazing one example.

Speaker 1: 30:33

Right, that's amazing.

Speaker 2: 30:34

It's like it just blows me away yeah it's, it's awesome, like I actually enjoy writing myself. So that is back to the point about like this is like getting it faster, produced for you and formatted and laid out and checked. Then you come in as the human and you put your personality into that and you add in, but it means you can do it so much faster.

Speaker 1: 30:58

This reminds me you two. You're totally off subject here, but I'll bring it back. There's a movie that came out years ago called Finding Forrester. Are you familiar with that movie? Yeah, I am, yeah. So this idea of starting with something else that somebody else wrote and and but then making it something of your own is kind of what you're talking about. Yes, because so now?

Speaker 2: 31:18

that's like a little hint for all those people out there listening if you haven't seen finding forrester, go listen, go watch it right, I have to go watch it again now, but the there's a lot of talk and I I think I wrote a blog on this recently a lot of talk and I think I wrote a blog on this recently a lot of talk about like.

Speaker 2: 31:32

people are like oh, traditional SEO is dead and you know Google has constantly changing their algorithms and their preferences are for, like, clustering of topics, not just one keyword. So there's also things you have to keep up with as an SEO type marketer.

Speaker 1: 31:49

So there's also things you have to keep up with as an SEO type marketer.

Speaker 2: 31:52

But the beauty of having a tool like that is it's telling you is this AI produced? Because you know you will get dinged if you're putting out word salads on just trying to optimize for one keyword. So it's actually improving and making your quality better because you can use these agentic tools to tell you is this, too, ai produced or is this plagiarized?

Speaker 2: 32:19

um, that's really interesting there's a couple of other little repetitive tasks. Use cases I'll give because I always like to give practical advice, so like one example would be this so you can like chat with your data which is like unstructured data, like text, or you can chat with your data that's structured. So let's say I output a CSV file from my SEMrush or Ahrefs where I'm doing analysis on keyword research, seo, and you can literally bring the CSV file in start text, chatting with it and asking what keywords have volume of like, a thousand, show me all, produce it in a chart and then you will immediately get insights and answers as to where you should focus. And that's just one little example of letting the AI do the work. And I didn't have to think of like oh my God, I need to go find somebody who does SQL to do some analysis into some warehouse. You can literally just use a little tool that's chatting with your structured data.

Speaker 1: 33:28

That's really interesting. Yeah, I'm like I said, I'm, I am. I dipped my toes in very little bit over the course of the last 12 months and I've really started digging in. I'm looking, I've been. It's been interesting. The other part that I've been using is some of the custom GPTs that are I'm cheap, so I still have the free chat GPT, but there's some great custom apps in there that are sort of focused on certain things that have been. So far, most have been pretty good yes other than the image generating ones.

Speaker 1: 33:57

I those I don't get a little scary a little bit. I think there's work to be done there yeah, no, I agree with you.

Speaker 2: 34:05

Um, I think we're all very impressed with the speed at which you can produce those things. I think it is not very authentic looking, and I actually worked at a company before that was all about visual content licensing and the reality is that real photos and videos exist on the internet. We're all doing it every day on our phones and we're uploading it to social. So it's, it's in the in the world online. So if you can just access that and then pull it into your campaigns. You're better off.

Speaker 1: 34:40

So yeah, yes, yeah, I mean there's. I did one where I was like I needed a, an image for a like a blog post that I was doing for for someone, and I said, yeah, I gave it some ideas and he generated something that was actually. I was like, okay, that works. But then I was looking at closer and I was like it's supposed to be like someone in a kind of a. It was a data, like a cdp related thing, so there's data and charts and stuff on the screen. It's kind of futuristic. But somewhere randomly in the middle was like a flower and I was like why is that there?

Speaker 1: 35:17

the machine decided that yeah I mean, it was, it was the strangest thing, um, but otherwise it was great, uh, but I'm with you, like I've seen some really odd stuff come out of those, those image generators, what. So you've talked about a number of scenarios here. So, in general, where it goes, are there any ai tools that, um, you know, marketing ops, listeners and viewers could be thinking about? This is how I could use ai tools to help me in my day-to-day job that are more ops focused.

Speaker 2: 35:51

Yeah. So I will just say, like there's the categorization of like we just bubble it up for a minute, the categorization of the things you can do with AI. So to orient yourself, like am I actually doing some like research or market research, and it could be more on a macro or it could be on a very micro level. So a micro level might be I'm helping the sales team really understand that these target accounts we're going after and do some enrichment and augmentation of things like industry size, public or not, geos, what's their claim or their benefit or value. So you're sort of like, as an ops person, you're really helping do a lift for that sales team and so those are like a macro level type research. Or you might do something like scrape a competitor's website and put it in a summary doc so I can be really pointy about how I'm going to differentiate Right. So that's a research bucket.

Speaker 2: 36:56

Then we already sort of talked about some of the content capabilities and then, from a demand perspective, like there is workflow steps.

Speaker 2: 37:06

So you kind of touched on it earlier, which is like I have a bunch of email addresses that I got from a show or an event or maybe some joint campaign I did with somebody, and now I need to like parse out what's the domain of the company.

Speaker 2: 37:22

Now I need to parse out what like industry segment. Now I need to parse out where they are, what size, what revenue, all those enrichment things. I need to parse out where they are, what size, what revenue, all those enrichment things. So that is like a very valuable use of like, just like you were saying, oh my God, I'm doing it in Sheets or Google, and that is a speeding up on the execution of a highly targeted campaign, for example. So those kind of workflows, the tool that I'm using right now I touched on earlier, which is it's a company called MarkovMLcom and it, out of the box like you literally self-serve login, out of the box you can launch a specific thing will do exactly that set of tasks. All you have to do is say here's the file with the email addresses, do the tasks and then you get the output and in less than two minutes it's done right.

Speaker 1: 38:19

Wow.

Speaker 2: 38:20

And then I will. I think most of your listeners that are sort of leaning into modern AI-type tools like Clay is the current darling know most people are paying attention to that. Apollo is also a very good tool for like, really getting the right and clean emails for target and especially if you're doing any outbound marketing targeting certain accounts. Um, the name of the game is that. Gone are the days where you do sort of a generic message to this batch. Right, you now have the capabilities to be much more targeted and personalized and not like, oh, let's do like three outbound emails a week or X number of things on your calendar every week week or X number of things on your calendar every week. Now you can be much more sophisticated and get higher conversions and responses.

Speaker 1: 39:18

That's really interesting. Yeah, I can see that being helpful for our listeners as well. So you brought up Clay and you mentioned to me when we were talking before about an example I don't know if you're able to share it where it's saved time doing outreach research. I mean you touched on it there. Can you share a little more about what that was like?

Speaker 2: 39:47

In terms of the time it took to get it off the ground.

Speaker 1: 39:52

Well, I guess there's that too, but I'm just like what? What was it used to do that otherwise would have been done by through human capital right.

Speaker 2: 40:03

Yeah, okay, that's good, good contrast. So I mean we can take the example of the play. So I've worked with my sort of former RevOps and MarketingOps team members in Concert and this is where, like, the human part is coming in as well, right? So we said, oh, we have the existing customers that are happy they're in this industry and segment. How do we go find more of them? So, like I think ZoomInfo and others used to call it, where are my lookalikes? So you have to go figure out where are those ideal customers? So that's kind of painful. Like in the back in the day you would like I'd mess around with Zoom info.

Speaker 1: 40:47

You had to go get D&D and yeah, all that stuff right.

Speaker 2: 40:50

Yeah, a mix of tools, and then you put it into some spreadsheet, and then you have to augment the spreadsheet, and then you have to check it, and then you have to share it with the sales team and then you have to upload it and then you have to go find all the email addresses on the right company and hopefully the tools help you.

Speaker 2: 41:05

So in Clay's example, it is list building for you, but doing it in a very highly targeted way, and it is literally scraping information like it's real time, so it's not like older data that was maybe collected months or years ago. It's literally now telling you this company is doing this stuff and here's the right personas. Play is also going and finding the persona information from multiple sources, not just one source, so they're sort of casting a wider net. So they'll go look at your LinkedIn and probably find information about you in other places if they have a relationship with Apollo, for example, right. And then it tells you all that rich information. And now you can actually be super prescriptive and say create a three-touch sequence and you know top and tail my messaging with these things but tailor it according to the human and the company that they're at, based on what I want to engage them with. And that's where that human part comes in.

Speaker 1: 42:18

So that's true, true, like truly personalized, as opposed to just hello, michael, right, yes, I see you're at such and such company yeah, exactly, and you know that it's very sort of cut pasty, because when you read it it's like nothing to do with me right.

Speaker 2: 42:34

So, um, in the in the before times when you didn have automation, you would ask your BDRs hey, you got to go research each of these target accounts and you have to then tailor your message and you have to go into your outreach or your sales loft or whatever you're using, and make those changes, and you know how long that takes. It's the right thing to do.

Speaker 1: 42:59

It is the right thing to do.

Speaker 2: 43:00

But they don't do it.

Speaker 1: 43:02

No.

Speaker 2: 43:02

Because it's a pain in the butt right.

Speaker 1: 43:05

Sure, yeah, I mean I had a small inbound team at one point in my career and we had templates for large categories of type of follow-up we'd have for incoming leads and but we always made sure that we took the time to tweak them to match what we could tell about like doing a little bit of research, and I think that I've always thought that that little bit of extra effort reduced the volume that we could do but was more effective, and I would take that trade off.

Speaker 2: 43:38

Yes, quality over quantity, yes, so I think that the, the delay tool, is allowing you to strike that balance right, you can get in, you can scale it easily and get the right volume, but your conversion rates are still way higher and you know I have.

Speaker 2: 43:56

It takes some weeks to get this whole thing off the ground because you are shaping it right, it's not. You have to tell it what you want it to do and then you have to like you're not just going to send out these. It produced an email or message and you want to put it in LinkedIn. You've got to validate it. I'm like, I'm good with that, or put in your own little personal touch. So it definitely improves the conversions and I think that's why people are adopting this, because it's working. And you know, if we wanted it, we just didn't have the technology that was enabling us to get there.

Speaker 1: 44:30

Well, actually, I would argue that we did have the technology. We just didn't have the human, like we didn't have the time to take advantage of it the way we would want to.

Speaker 2: 44:37

That's fair, but the technology allows us to speed it up.

Speaker 1: 44:42

Yes, totally agree with that and I like this. I guess this is what we're talking about when I hear about agents, the AI agents and agentic stuff. That's what we're talking about. Right, it's like being able to do multiple steps with a little bit of guidance on what you want the output to be.

Speaker 2: 44:58

And I mean I'm sure you've been there where it's like you realize that the team is spending their precious time on these repetitive things and you're like, let's outsource that. And then you're like, okay, now we got to find a human that's going to do this outsourced, boring work, and then we got to pay them extra and then we got to check their work.

Speaker 1: 45:24

Yeah, yeah. So it's yeah, yeah, for sure. Um, well, let's maybe one more thing, um, and then we can. We can wrap up so one of the things I'm bullish on and have been bullish on about potential for ai to have an impact on is actually somebody we talked about earlier reporting and analytics, because I do believe it has been a very it's like a very human capital intensive thing. Right, there's a lot of effort in addition, just expertise, and so I am bullish on that. Be able to help really drive some great insights over time quickly, whether it's faster, uh, be able to do more um experiments or test more hypotheses, whatever it is right. I think any of those would be beneficial. Are you seeing any of that also out there in your experience with? I know you're doing a lot of consulting these days, so what's your take on that area of AI's impact?

Speaker 2: 46:16

Yeah, the biggest challenge with and I actually had a conversation with a head of an agency today who does all ops and digital marketing and she literally said I am constantly wreck, I'm spending all my day reconciling data, so I call it like it's always stranded and siloed, so it lives inside Salesforce and it lives inside HubSpot, and as much as you're syncing these systems, you still have to make sure it's right. So time and time again I would, at the end of every week or even month or quarter, I would pull data out of both my you know run the business systems and then I put it into a sheet and then I create pivots and add columns, vlookups and all that yep yeah, and then I share it with the ceo and the cfo and whoever else needs to know, and it's a pain in the butt.

Speaker 2: 47:13

And then you, you're, you're in there and you're actually making charts out of it and so on. So AI your question is like how is AI helping? That AI is the I referenced it earlier in our conversation where it's like I've got that lovely CSV file. Like you can turn most of that structured data into CSV. I put it into my AI system. I chat in English, chart me X, y, z or you know how many. How many deals did we close last quarter that were higher than 150K, example? Right, and it will instantly produce the answer put it in a table and pick your chart style and then output and share that.

Speaker 2: 47:57

And the thing, the beautiful thing about having english chat with your structured data is you know you would automatically do that in your meeting anyway. Now you can actually talk to the system to do it. And a question begets another question.

Speaker 2: 48:14

I've been I say this all the time so, as soon as you, one question and you get a nice little nugget and then you're like, well, hang on a minute, how about this? And that's the beauty of the really pulling wisdom out of your data, right? If you can read the data and ask the right questions, then you can really start to be informed and knowledgeable. So I'm all about dive in play with it.

Speaker 1: 48:41

ask more questions, ask more questions that iterative process is how you like, really get the most out of it right. But it takes again, takes time and effort and expertise, and so, again, if we can make that faster but that this is a another aside, but like this is why I whole knowledge that these things one question leads to another question leads to another is why, when I go to some place and I go, we need to get dashboards. No, you don't, you really don't need dashboards. No, start with something small, exactly If that becomes something you know you're going to do regularly, because you usually don't know, fine, make that the beginnings of a dashboard, then do the next one, but don't know, fine, make that the beginnings of a dashboard, then do the next one, but don't start with the idea of we're going to do this comprehensive dashboard.

Speaker 2: 49:24

I think you think I've never seen one of those succeed exactly, and if you present a dashboard which has like 12 things on it that give you a little insight into each part, then you you have to like, if you're talking to your CFO, for example, which the board will actually ask as well, then they're just glazing over because it's too much, yeah, whereas like the nuggets like I love your point about like, just start with something small and I can't tell you how many meetings I've been in and you present what you believe they want to hear.

Speaker 2: 49:58

You go to the meeting with your nice, pretty and you know charts and then, of course, they ask more questions and then you're like well, I have to get back to you on that because I didn't produce that report and it slows everything down yeah and or, or, or maybe worse.

Speaker 1: 50:16

You know you've got a dashboard with multiple reports that are already tough to consume but somebody goes that one's not right, right, yeah, and, and then that that reduces the credibility of all the other ones that may be. Quote right. I mean, first off, saying it's right or wrong is I think it's just a misunderstanding about what you're actually looking at.

Speaker 1: 50:37

But yeah, that's a whole that's a podcast yeah, okay, well now. So now I feel like I have got a lot of catching up to do on all these ai tools and getting some of the mundane stuff out of my life. Um, this has been great. Thank you for for sharing. If, if folks want to connect with you or learn more about what you're doing, or find your pod, your, your blogs or whatever you're doing, what's the best way for them to do that?

Speaker 2: 51:04

They can go on LinkedIn and hopefully Deirdre Mann.

Speaker 1: 51:08

It's a little tricky to spell but we'll make sure we put a link to it somewhere along the way.

Speaker 2: 51:13

Yeah, I do have a website. It's called marketing engine twocom, the number two, because my my belief is that usually younger companies they try it and then they're like, okay, let's do this properly, b2. And then you know just being really modern about your approach. So it's always a challenge. So you can check me out there or find me on LinkedIn and message me directly.

Speaker 1: 51:38

Sounds good. I appreciate it, and you are, because we traded messages today on the marketingopscom Slack, so you're there too, right?

Speaker 2: 51:45

Absolutely.

Speaker 1: 51:46

All right perfect.

Speaker 2: 51:47

Thank you for all you do there, because I get lots of learning from that.

Speaker 1: 51:51

That's great. Well, we appreciate it. Thank you again. Thanks to all of our listeners out there for continuing to support us and sharing your ideas and topics on guests and, as always, if you want to be a guest or have a topic you want to share with us to cover, feel free to reach out to Naomi, Mike or me, and we'd be glad to follow up with that Till next time. Bye, everybody.