Kevin Micalizzi: Welcome to the Quotable podcast. I'm Kevin Micalizzi.
Today we're talking with Gil Allouche. He is the CEO and founder of Metadata.io. We're going to talk about account-based marketing and some of the changes technology's brought. Let's jump into it.
Gil, thank you so much for joining us in the studio today.
Gil Allouche: Yes. Thank you for having me.
Micalizzi: Gil, for our listeners who aren't familiar with you and the work you do, would you share a little bit about yourself?
Allouche: 100%. I'm a software engineer in my background, post-MBA days. I've done marketing in the last 10 years. I started Metadata. It's an AI operator for marketing ops.
It's a marketing technology company here in San Francisco, and I'm the founder, together with 20 other employees growing the business.
Micalizzi: Excellent, and I'm super-excited. I'm joined in the studio today by Carrie Smith. She's a senior manager of product marketing here at Salesforce.
Carrie Smith: Thanks for having me.
Micalizzi: Gil, you've done a lot around account-based marketing, as well as talking about big data overall. Let's not start at the 101 level, because I think there's enough out there, and our listeners are familiar enough with account-based marketing to know what it is.
But I'm curious, from your perspective, kind of where are we at in terms of account-based marketing? Because it's been around for a while now.
Allouche: Yep. That's very, very accurate. Account-based marketing has become very popular recently, because many new companies are there, and they're trying to ride the wave. It's a new category, but account-based marketing has been around for about 11 years.
Some of the influx that you see in all kinds of companies calling themselves account-based marketing is because most B2B companies understood the paradigm of moving into ABM.
In the market now, there are three things that changed that made companies like Metadata and I'm sure things like Salesforce Studio and Einstein a reality, and those are Hadoop, the ability to store a lot of data and very cheaply.
Second one is most machine learning models and libraries are now open source, so anyone can come up with their own machine learning model.
Then, finally, most of the marketing software is operatable by RESTful APIs. Today you can pretty much automate and orchestrate the entire account-based marketing workflow, which in the past, you would have to manually operate many different kinds of tools.
Each and every one of them operates in their own silo.
Micalizzi: Right, so a lot of separation.
Micalizzi: Carrie, from your perspective, because you work much more closely to this than I do, where would you say we're at, from your perspective?
Smith: What we're seeing today is we've realized that products are bought by groups or teams and not by individuals anymore, so I think it's really important to understand who those buyers are, so we can start targeting buyers and growing accounts.
That's why account-based marketing has become so popular over the last few years.
Micalizzi: Right, kind of hunting with spears rather than nets.
Smith: It's fishing with nets.
Micalizzi: Or fishing with nets.
Smith: Yep. Hunting with spears. Sure. Sure.
Micalizzi: Hunting with nets. I suppose you could.
Smith: Do whatever. [Laughs]
Micalizzi: You could do that.
Smith: Nets and spears.
Micalizzi: Gil, I know one of the things you've talked about in webinars I've seen and your team talks and your marketing materials is the importance of your first-party data versus your second-party data in terms of looking at or trying to figure out what your ideal customer profile is and really how you break down and target those accounts you want to go after.
I'm curious, in terms of the first-party data, now that AI and account-based marketing are accessible to the smaller companies that at one point in time would never have been able to afford the technology or really hire the resources to make it happen, what are you finding in terms of that first-party data for the small companies that may not have that wealth of data to really pull from?
Allouche: That's a very common problem. One of the things that we've done is pretty much not rely on the first-party data for anything other than opportunity and commercial information.
When we go to a customer and we try to define for them their ideal customer profile, if we were to rely on the data that is in Salesforce, that probably wouldn't be enough, because, usually, they collect very shallow information from their prospects, so maybe like their name, job title.
Maybe they have the industry and the site of the company, but that's it. When we go to a company and say, "Hey, we're going to help you understand, based on your historical sales information, who is your ideal customer profile and translate that to your TAM, your addressable market," we leverage third-party data from all the major B2B data sources out there, so companies like Data.com and InsideView and Zoominfo and [AgData], and we basically collect any piece of data that is now available publicly and their firmographic data and demographic buyer intent data and technology install base.
All those data sets are now available. They are affordable for a small company. So we can leverage those data sources and pair them together with the first-party data to generate a very wealthy ideal customer profile, and the attributes of that ideal customer profile may end up signal that you would never believe.
I just finished a phone call with a prospect of ours, Survey.com, and they found that the most interesting attribute that has the highest p-value to closed-won opportunity is the skill set in LinkedIn of those particular
Allouche: of the buyer [committee]. Yeah.
We've seen all kinds of crazy signals in the past, and so that's why the model is data-agnostic. It doesn't know what to look for. It just runs a statistical function, and then the attributes come forward.
Micalizzi: Fascinating. Gil, obviously, data can be a challenge for small businesses, and third-party supplements that. I'm curious what other challenges you're seeing, whether it's the small business or a larger enterprise — the challenges you're seeing people encountering in terms of account-based marketing.
Allouche: Yeah. The thing with account-based marketing is, if you Google it up, you'll probably find 100 companies who are doing account-based marketing, but you'll also find many types of companies that aren't really related to one another. You'll find data sources that just sell data that will call themselves account-based marketing, because they can sell you data about accounts, and they know that ABM is a very common term on Google.
Basically, what I'm saying, the confusion in the market is a challenge. The second thing that we see is that companies are now companies that run ads. They run programmatic ads against target accounts.
They are carrying the flag of no more leads. No more lead generation. Although that sounds romantic and sounds cool that you don't have to generate leads anymore, some of the customers that we talk to are not really doing a rip and replace. They're not going to change their entire strategy tomorrow because account-based marketing's cool and going to start — only try to see if their accounts progress in their Salesforce, because now they're doing a different methodology.
One of the things that we see in the market is that vendors should kind of complement whatever the customers are already doing.
If they are doing demand generation, you can narrow down your demand generation now to only your target accounts. If you're doing lead generation, you can still do lead generation, and you still need leads to hand off to your sales counterpart. You just have to be so much more precise in the way you're going after those prospects, and I'll go left or right from your list of target accounts.
Accuracy is kind of the biggest thing that those customers are being challenged with, and they don't really know: "Do I need to completely rip and replace, and can I complement ABM and bring it into my marketing mix, basically?"
Micalizzi: In terms of all those companies that are claiming to be doing account-based marketing or supporting account-based marketing, is there confusion because they're just trying to capitalize on that name, or are they trying to position products as — I'm trying to think of a nice way to say they're a bunch of charlatans and totally taking advantage of a term and aren't really supporting it. Let's not go negative, Kevin.
Allouche: It's the true reality. I have a lot to say about it. It definitely exists. I mean, I just recently talked to a customer for a competitor, and they closed them on a $2,000-per-month subscription on an account-based targeting, like basically running ads against their target accounts, but the budget — it's a super-small startup. They're at seed stage, so they have pretty much nothing. The budget they have to spend on ads with that company is $60 per month, and the money they pay for running those ads is $2,000.
Micalizzi: Oh my gosh.
Allouche: So those deals are out there, and they exist because companies are trying to grow and they are trying to grow aggressively, especially if they're venture funded.
So they would run and keep growing with no rules.
Micalizzi: Right. Gil, you talked about APIs and kind of how that has changed the game here. I'm assuming the bottom line for that is really the fact that all these products can now start to talk to each other more, and you're not just doing things in silos and pulling a list from one system and trying to use it in another, so you're actually tying a lot of that stuff together.
Is it just that these products have matured enough, or are we still kind of at the beginning of that maturation process?
Allouche: The paradigm shift is that now we can store all the data, because it's cheap, and now we can use the data to essentially predict what to do next. Some of the small tools — until now, you had to log in, log out, run manual campaigns, set up the UTM tag, set up a code snippet, and reach delete when it comes through, push it [via a webhook] to your marketing automation or Salesforce — all of those mundane, technical tasks that marketing operations needs to spend most of their day doing, could become — they are still present.
They are still the present, but I would think that two years from now there would be an AI operator running all of those [tags] on behalf of a company, so that the humans, the employees, can focus their time doing the Mad Men kind of stuff, coming up with cool campaigns and programs and taglines and creative and content, and not spend time kind of repeating things that the computer can do.
We see that with some of our — Nutanix is one of our clients, and we scale thousands of experiments for them per month. There is no chance, unless they have a small army, like dozens of people with huge spreadsheets, they would not be able to generate so many different experiments, like "Let's try this collateral with this creative with this audience on this channel with this campaign time with this budget." It's going to take forever to create this huge multivariate experimentation.
But AI solves that problem. It's very easy for a computer to scale up and down from 1,000 to 10, because it's just a few more clusters, a few more computers, and that's it.
Micalizzi: I think there's a lot of confusion around AI. I think, similar to account-based marketing, it's a term that's often applied right now. From your perspective, how are you seeing AI impact account-based marketing?
Allouche: Today, from what I see in the market, AI is being used one way, and that is a classification. Machine learning is a subcategory of AI. Machine learning is essentially being used today mostly [in this] classification form where it goes into clients' data.
It learns what kinds of attributes have led to a closed-won opportunity in their pipeline. It tries to correlate those attributes, and based on those attributes, they build a model. Then every time a new lead comes through, they have the same data taxonomy for that particular lead. They can match it with that model, and they can say, "This lead is likely to materialize into a commercial business or not."
That is lead scoring, and that's called predictive marketing, which is a fancier word to say that. As far as I know, this is how AI is being used today.
At Metadata, we apply AI in a few different ways. When I talk to investors and when I talk to customers, I try to say, "What is the AI that you use?" Because it's different. We use that machine learning classification to do ICP, but we use decision tree.
Micalizzi: I'm sorry. Just for the listeners, by ICP, you mean ideal customer profile?
Allouche: You're correct.
Micalizzi: So taking those attributes and figuring out what that perfect customer looks like.
Micalizzi: Okay. I just want to make sure everyone's following.
Allouche: It's perfect. You're right. I tend to go into acronyms very quickly. Yeah.
Giving something that is a little bit more consumable by a B2B marketer is a way for you to extrapolate the value from AI, but there are many more ways to use AI.
For example, multivariate experimentation. We took a concept that is not new. Financial services companies have been doing this for decades. When they do risk analysis, they take "What happens if Armageddon happens tomorrow and there is a storm and the financial crisis?" and they take all these different factors and then they say, "Okay, this is a good investment. This is a bad investment."
Same way AI can do this for marketing. They can try all kinds of permutations, and they see what happens if X factor happens and Y factor happens and they can start running an experiment and see how the trajectory of that experiment [ends], just like a human being would do it, but instead of doing five experiments, 10 experiments, he does a thousand of them, so the hidden gems — a 20/80 rule is much more impactful.
AI can be really used for so many different use cases. Today it's really rather easy to create those models, even one data scientist and one back-end engineer can do wonders for a small company.
Smith: We're seeing a lot of really cool things happening in the B2B marketing spectrum around AI. I'm curious to know what your thoughts are. Where do you see AI in three years? Where do you see it in five years? And then where do you see it even further down the road? I don't want to say 10 years. That could be just like anything could happen, right? [Laughs]
Micalizzi: I'll be driving my hover car to work, just for the record.
Smith: For sure.
Allouche: They always warn us about the singularity, that as time goes by, [things are] able to change so much faster, so I have no idea what will happen in even five years.
I think, to your question, two or three years from now, I truly think that most of the marketing jobs that exist today, including creative work — product marketing — will still be there, because someone still needs to translate those things, and computers — I don't know if they know how to do it still. But even coming up with creative, coming up with text, today, for example, all the tools are already here. The models need to mature, and they need to be more reliable, like self-driving cars are not really yet, like if you tried to have everyone in a self-driving car, you're going to have some problems.
I think, once the critical mass will start using AI, that's where the training models will be so much more mature, and everyone will be able to start applying those on their day-to-day. Two, three years from now, I don't think marketing ops will do any technical work whatsoever. I think you probably will ask a robot to run your campaign. I think the campaigns would be extremely more personalized, just like you have a complete personalized campaign from a B2C company, like a Coca-Cola or adidas, the same [it] would be for B2B marketing.
You won't ever run an email blast to 100,000 people again. You'll run a 30,000 email blast automatically, based on their time zone, etcetera, to three people each. Those are the kind of things that I think will happen fairly soon. It's just a matter of how quickly companies will adopt.
Smith: Yeah. For sure.
Micalizzi: Moving further out from there, same thing with the self-driving cars. Once that model's down, then it becomes much easier to scale it up and get more folks involved. I kind of want to push for that 10-year mark, Carrie.
Smith: Let me know.
Micalizzi: I want your long-term vision here, Gil.
Allouche: I think you should be talking about a 10-year vision, because I'm kind of afraid to even open my mouth.
Smith: I don't want to touch that with a 10-foot pole.
Micalizzi: Let me ask you, in terms of the approach to account-based marketing now, I mean, our listeners are sales leaders and even sales reps, what should they be doing right now to either support the account-based marketing that's happening or to help their companies take advantage of it?
Allouche: I'm a big proponent of experimentation — experimentation, trial and error, however you want to put it — and automation. I'm a software engineer. Software engineers are super lazy. We don't like to do anything more than, really, not even once, but definitely not twice.
We are doing some betas right now with companies like Conversica that is an AI operator for sales ops, etcetera, where we're trying to see how much of this automation can really be set up in production.
We have a few customers in which we're the AI operator [that] generates all the leads, and another AI operator for a different company follows up on the lead and sets up appointments.
We're trying to see how much this use case is more effective than other channels. With one of our clients, for example, they're a large company. They're public. Every month they do a competition with us. We've been working with them for 11 months now. You would think that at this point they would trust us that we're doing a good job, but every month they test their own internal campaigns versus the Metadata-generated campaigns.
The results are it's about three and a half times the results, but there's still a lot of [uncomfort] because you don't really want to set a robot to run everything for you on autopilot, especially not if you're a public company.
You guys are Salesforce. You know that you have to approve the creative, you have to approve the content, approve the messaging. You don't want to get sued if you said the wrong thing. I think, when companies will get to the place where they are trusting it, that will be a big changer.
For sales [leaders] today, I would say be comfortable trying out new systems. Don't replace. Take 20% of your budget, and the 20% is set for experimentation, and see if that 20% is performing much better than your other 80.
If it does, take another 20 and then try a new thing. Definitely settling down and doing the waterfall approach where you're just playing for the whole year and everything's kind of set in place — I think that's the beginning of the end.
Micalizzi: Right. What mistakes have you seen companies make in approaching account-based marketing, even with all the technology that's out there to support them?
Allouche: There are a bunch of mistakes, but one of the big ones is not adapting. You set up a new technology, you make it happen, and it starts yielding leads or yielding pipeline. Everything else [stays] the same.
For example, one of the companies we worked with — their campaigns are generating [unintelligible] leads. We looked to see how those leads perform, and we found out that they're not following up on many of the leads. We looked to see why. We saw they're like, "Oh, you know, because some of the leads are coming up with Gmail and Yahoo accounts."
We said, "What does it matter? You know account-based marketing. You know for a fact you're targeting the right people at the right companies. It's the whole point. So you can assume that this is the right one." The machinery even enriches that lead anyway and shows you, even if it's a Gmail or a Yahoo, 70% of the time, we could still find out the company and even give you the corporate email. We just don't push it as the main one because they didn't opt in with it.
Once they realize, "Oh, this one's the Bank of America. They signed up two and a half weeks ago, and we didn't even follow up on them" — that's a true story — then they changed.
Then they said, "Okay. So from this channel, now we're going to act differently, and maybe we'll let it pass through the scoring mechanism."
Another company: they used to just put those leads on an autopilot one, like a cadence, like an Outreach.io kind of cadence tool, to just send them kind of templated emails. That also was not very effective, because those leads are hot. They're the right leads. You know exactly everything about them. So it takes a little bit of a different approach. When you run account-based marketing, you do have to at least have one person who is comfortable with testing.
We took one SDR, and that SDR is now more comfortable taking the lead and following up with them, and of course, they became now the star, because they're following up on awesome leads, where, before that, they would throw them into the garbage.
Micalizzi: Is it a fear of changing your approach, or is it similar to, I think, the challenge we see with folks adopting a CRM and not really working to innovate their process? They're literally taking the same paper process or spreadsheet process and trying to use a new tool to approach everything the exact same way.
Is it fear or is it just they lack the skill or the knowledge or foresight to try and modify their approach to really take advantage of the new technologies?
Allouche: Change management is big, always a challenge. We are doing this one-hour consulting for best practice of ABM, and half the time the questions are not about ABM. They're about "How can I convince the executive team that ABM is the right way?"
We will them you don't have to. That's the previous comment of don't do a rip and replace. You don't have to introduce this new thing, and now this new thing is the only thing.
No. It's like a new thing. Let's check it out. If it works for us, take a 10% experimentation, take three months. If it worked, awesome. Didn't work, next thing. I think change management is something that you have to apply when you have new technologies.
Smith: When you have companies come to you and they ask, "How do I get started? What are the first steps I need to do?" what kind of tips are you offering them?
Smith: Aside from "Let's take a deep dive into your data." Obviously, that's one of the main components, but then after that, what's step two, three, four?
Allouche: No. I just recently, we're doing this one-hour consulting, so I have all these examples.
One of the companies was asking us what to do. I try to be super noncommercial when I have those consulting hours, because, obviously, I want to just use Metadata as the best thing in the world, but it's not. Sometimes it's not a good fit, even.
Definitely taking a look at the data is the first thing to go.
Right after that, we tell them to start looking at essentially how do you move from doing this one campaign that you thought is a great idea, because someone who's senior enough in the organization or just whatever won the argument, to only making decisions based on data on a daily basis. So experimenting and trial and error, and trial and error becoming kind of a character in the company, like an attribute of the company, the culture, if you will.
One of the things that I will always recommend to companies is to start with the data, understand what worked, and then be comfortable with failures. Try a campaign.
Assume that 80% of your campaigns are going to be neutral or negative and 20% of them are going to be your dream. You never thought you can achieve something like that. But only with that mindset you'll achieve that. If you want to try one experiment, two experiments, three experiments, and you spend $2,000 and your boss is going to yell at you because it didn't generate enough leads, that's not the right approach. You'll never get to those hidden gems.
Micalizzi: Right. Carrie, in terms of the work you guys do with customers, are there other suggestions that you make in terms of either a starting point or how to kind of get yourself prepped for this?
Smith: Yeah. That's a really good question. I think what we see a lot in terms of account-based marketing, people just want to get started, and they want to understand "What are the first steps we need to do?"
I think we kind of say, "Hey, look at your data. You have data in so many places. You have data in your CRM. You have data in your emails. You have leads and you have contacts, and you kind of need to bring those all together and then start to do some sort of account-matching.
"Then, from there, then you can look at firmographic data, you can look at different segmentation channels, and then you can kind of understand what accounts you need to grow. You look at your white space. Then you can start to kind of double down on your content, and then from there, you can kind of just understand where to grow those accounts and who to target."
Micalizzi: I'm hearing don't dive in headfirst.
Smith: And do a lot of testing.
Smith: Testing's really important.
Micalizzi: I really like your advice, Gil, in terms of taking that 20% — start with that. Make sure it works.
Make sure you're doing it well and moving — kind of this same argument people have with traditional calling versus "We'll sell everything with social sales and social media."
Micalizzi: You kind of need all of this.
Smith: Yeah. You don't want to go in headfirst to ABM. You really want to kind of like scale, because I think it's important to continue what you're doing and kind of grow your ABM over time and just not take a deep-dive approach to it.
Micalizzi: Couldn't agree more. Have we made it easier to scale?
Because I know that's been a challenge for B2B marketers, is you want to have that personal touch, but at the same time, you don't have the staff or the resources to have that personal touch with the number of people you need to, to really grow your business.
Smith: There's a lot of tools out there to help you scale, but I also think those tools can also make it a little bit complicated.
Gil, I'd love your opinion on — there are so many great tools out there for B2B marketers, right? Where do you see that in the future? When we're looking at tools, do you see these great tools being bought out by bigger companies, or do you see tools coming together?
Smith: There's a martech stack out there, right? You look at the Martech 5,000, and there are 5,000 different tools that B2B marketers have access to. Where do you see that coming to a head? I mean, we can't just continue using these 5,000 tools. All of our data's in different silos. We have silo data everywhere. How do you see that coming together in the future and down the road?
Allouche: Yeah. The LUMA landscape with the 5,000, like the famous martech nightmare slide. First of all, I think it became so cool and such a popular slide that I think it's like a self-fulfilling prophecy. Now they're actually looking for new tools, new logos to just push into that slide.
No. I think there is a lot of confusion, and it's a very cumbersome marketing stack that you have to manage today. I think, naturally, many of those companies will just die, because they won't be able to survive.
I think about half of these will probably naturally just dissolve. The other half will consolidate and be acquired by [the] large company, like ToutApp becomes now an SDR feature in Marketo, and BrightFunnel now became the attribution feature in Terminus, and Spiderbook now became another data source for Demandbase. These are medium-size fish eating small fish. There'll be the big fish eating the medium-size fish. So I think, eventually, we'll see a lot of consolidation.
Still, there would always be the reversification, because there will be different companies — a company that cannot ever afford a Demandbase. There are many of those.
SMBs, the small home office, they need something still. They have the different market, but they have similar needs, like maybe they want to show their ad to lawyers or to doctors. They don't have tens of thousands, but they have thousands. Some of those will still be relevant.
One of the approaches we took is to orchestrate those tools automatically. Yes, you have seven tools or 13 tools in your marketing stack. That's cool.
Don't spend 90% of your day trying to operate those tools. Use a platform that you can connect those tools and then squeeze the value that you get from those solutions. Have a computer automate everything that can be automated so that you have much more time figuring out the strategy and the programs versus kind of drown in the marketing ops in day-to-day.
Micalizzi: Right. Gil, for our listeners, let's say I'm at the gym right now, listening to this podcast, getting my workout done.
Soon as I'm finished and I got my phone in front of me, what should I be doing to get myself moving in the right direction for account-based marketing?
Allouche: I would say first things first is to get comfortable with — just like you're playing with stocks: Carve out your budget and your time for experimenting with account-based marketing.
Start with that and then go to your sales counterpart, because every [marketer] has a sales friend who is dreaming of getting some leads from. Go to your sales counterpart and ask them, "What are the dream hundred accounts that, if I help you with any one of those hundred accounts or thousand accounts, I'll be your star?"
Start by forming that relationship. "Okay, now I'm doing something new for you. It's ABM, and it's 100% tailored to your pipeline. So tell me who are you going after?"
I think going and understanding from your sales counterpart exactly who they are looking for because it's going to help you when you do generate some kind of a good outcome. That's going to help you gain more buy-in for doing more experimentation and bigger budget, etcetera.
That's usually the first thing: Cover budget, talk to your salesperson, understand exactly what they want.
Then, before you go to that meeting with your boss or with the CRO or the CMO or maybe the CEO to ask for that bigger budget and start doing some account-based marketing, come with some data. Tell them, "Listen. I already went through our best top 20% of the opportunities, and I already looked at the data that we have, and I put it in Tableau, or I put it in Excel, and I already came up with these personas that we sell to and these technologies of the company that we sell to."
Try to be data driven, even if you've never done it before, even if you've never done data analysis before. It's really not that complicated, especially today with all the tools.
Take one, two, three — I think I already mentioned that before. Put all the datasets into one place. Unify them, because, by themselves, they're not that valuable. Unify a few datasets together. Put them in an analysis tool and start exploration without really knowing what you're looking for.
Those would be the first few things I would go for right now.
For our sales leaders listening, they should pull that list together of their top accounts and start talking to their marketing teams, I'm assuming.
Allouche: Yes. Yes. Certainly.
Micalizzi: Excellent. Gil, I want to ask you a lightning round question. If you could take all the knowledge and experience you have now, go back to the beginning of your career, and give yourself one piece of advice, what would you tell yourself?
Allouche: That's a tough one. Start earlier.
Micalizzi: Start earlier? Okay.
Allouche: Yeah. Or I know.
Start earlier, so let's dream bigger. I grew up — it's a small town named Netanya in Israel. My dream always was the biggest dream I could ever think of, was to be a software engineer lead. That was the top, like the top dream I could go for.
Then I got my first degree and I did the army and then I was a software engineer. I was an engineer manager, and I was 24 years old, but I didn't know anything about business. Then I started thinking, "Okay. Now maybe I can start dreaming bigger." That's how I ended up in Boston, doing my MBA.
But I probably could have started learning those things earlier and not have those limiting thoughts of "This is the top thing I can possibly go for." So that would be the advice I would give myself.
Micalizzi: Right. Your sights weren't set quite as high as they are now.
Allouche: Mm-hmm. Yeah.
Micalizzi: It totally makes sense.
Carrie, how about you? If you could take all of the knowledge and experience you have now, go back to the beginning of your career, and give yourself one piece of advice, what would you tell yourself?
Smith: Can I think about it and get back to you?
I mean, can you ask me in a few minutes?
Smith: I'm trying to think.
Micalizzi: I was like, "When are we going to have you back?"
Smith: No, no, no. I mean in like five minutes. Only because I've done so much. I used to be a schoolteacher, and so marketing is relatively new for me. I've only been in marketing for, I don't know, 10 years, maybe.
The things that I've learned over the last 10 years have also changed, because we didn't have all the tools. When I first started in marketing, it actually was handing over spreadsheets. We didn't have market automation platforms.
When I learned market automation for the first time, I changed my life, and it changed the way that I interacted with sales teams. It changed the way that I interacted with marketing teams. It changed the way that I interact with my VPs and my head of marketing.
I think probably the biggest thing for me is learn as much as you can or as early on, so you can have a point of view and really start to change the way a company works, because there are so many tools out there that can impact a company in a significant way.
If you can learn all those tools, you can really change the way that marketing is done at any company.
Micalizzi: I love it. That is the best quote I think I've heard in a long time. "Marketing automation changed my life."
Smith: Oh, 1,000%.
Micalizzi: That's phenomenal. I love it.
Gil, thank you so much for joining us in the studio.
Allouche: Thank you very much for having me. It was fun.
Micalizzi: And, Carrie, thank you for jumping in and helping out.
Smith: Yeah. Thanks for having me.