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"It shouldn't be consolidated or best-of-breed. I think it could be consolidated and best-of-breed." — Vinicius
Vinicius Rodrigues has spent his career arguing that fragmentation is a legitimate strategy, not a failure to consolidate. He also cut six figures in martech spend through consolidation. As head of marketing technology and operations at Mindbody, now part of Playlist, he manages the global martech stack across North America, EMEA, and APAC, three regions where almost nothing about how customers respond to automation is the same.
Before Mindbody, at Lojas Renner in Brazil, he ran a WhatsApp commerce pilot that generated 30x ROI and identified $20 million in revenue potential, in a market most US martech leaders have never touched. At Mindbody, he built the company's first marketing AI agent from scratch.
This episode is a pressure test of his own framework. If best-of-breed is always contextual, at what point does context become a convenient explanation for whatever decision you already made?
Timestamps
00:01 — Principled fragmentation or consolidation with better branding
04:00 — Agentic AI, the new overhyped buzzword
05:30 — WhatsApp in the US: hopeful but skeptical
12:00 — Unlimited budget, same philosophy
17:36 — Back-testing AI before trusting it with pipeline
19:45 — AI as safety net, not gatekeeper
24:30 — Localize even your research: G2 vs. Trustpilot
29:00 — The WhatsApp fashion stylist, before "agentic" was a word
37:00 — Platform roadmaps favor the biggest market
44:05 — Bilingual as a dual market lens
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What is Making Sense of Martech?
Unfiltered takes on the biggest shifts in marketing technology. We spotlight what matters, who's leading (or lagging), and what's next. In Martech, clarity is power — and we're here to deliver it.
Welcome to the making sense of MarTech, where the rabbit hole goes deeper than the headlines. This
is a show that pressure tests ideas, not just platforms. My guest today built a career arguing
that platform consolidation isn't always the answer. He also got 100 K in martech costs through
consolidation, and earlier this year inherited a stack that became dramatically more complex. So
which is it? Principled fragmentation or pragmatic consolidation with better branding? A little bit
about my guess first. Vinicius Rodriguez is head of marketing, technology and operations at Mind
Body, now part of playlist, the $7.5 billion parent company of Mind Body, obviously, as well as
Booker ClassPass in Egypt. He owns the global martech stack across North America, EMEA and AIPAC.
Three regions where almost nothing about how customers respond to marketing automation is the
same. He knows that firsthand. At a previous agency role in Brazil, he ran a WhatsApp commerce pilot, a
channel. Most US martech leaders have never touched. That generated 30 x ROI and identified 20 million
in revenue potential. Now he's applying that same regional instinct at my buddy, where he
built the company's first marketing AI agent from scratch, and cut six figures in marketing spend
through that consolidation. He didn't theorize about localization. He ran the experiment in his
native Brazil and then took the lesson global. Welcome. Hey, Jacqueline. And, uh, yeah, really excited
to be here. So, as you said before, my body, I spend a couple of years in one of the largest fashion
retailers in Latin America, uh, leading CRM lifecycle marketing, martech strategy. And, you know,
I had the privilege to work on big initiatives across personalization, CDP, AI. So, honestly, I think
what really motivates me is the intersection between technology and marketing strategy. Like,
how can we actually use technology, the right tools data to drive measurable business
outcomes. So excited that you're here. So let's dive in. And first up we've got some rapid fire
questions. What was your first martech tool. It was oracle responses you know in marketing and
automation platform. And of course not considering sales CRM and ad platforms because they had some
experience with those, you know, prior to that. But yeah, I think that was the first one. It's a common
starter esp for folks. Yeah. Okay. Name one channel that overperformed in Brazil
and underperforms in the US or vice versa. Definitely WhatsApp in Brazil
in email in the US. That tracks for sure. Yeah. What is the tech stack you're
working with today? Okay, honestly it's quite a big and I'll say kind of complex martech
stack for a business of our side. But we use Marketo as their marketing automation platform.
Salesforce is our sales CRM, so Marketo is kind of very interconnected to that. We use Bres as our
B2C kind of marketing hub or you know, we use it basically to send our lifecycle comes to our
consumer database. We also use psyllium as our CDP and our tag management system.
And we have some, like I would say, adjacent tools such as chili pepper we use for meeting
booking espera, which is really cool, vendor we use for an AI web chatbot. We use optimizing for
a B testing. So yeah, we do have the central ones like Marketo and some adjacent ones for, you know,
other specific use cases. It's always a wrangling of of tools when you have both the B2B and B2C
side of the business. Yeah, that's the challenge. Yes. So right now I think there's so many
buzzwords that are very overhyped. So I'm curious, what is the term, in your opinion, that every
vendor is pitching right now? That's just shouldn't be the term. You probably agree,
I guess, but it's a generic or even AI because I think, oh yeah, 3 to 4 years ago,
if you were browsing for a new martech platform, you know, you would just go to the website and
find the best CRM, the best, uh, marketing automation platform, or the best CDP. Two years ago,
they all became the AI, CRM, the AI market automation platform, the AI CDP. And then now it's
the authentic CRM, the authentic CDP, the authentic, you know, market automation platform, which is for
me is exciting, but I still think it's a little overrated because it's not fully authentic, right?
At least not to the business expectations yet. Yes. Well, in my opinion, agent tech is just
automation with maybe an extra bell or whistle. Yeah, an easier automation, right? Where you can
just tell AI what you want and then we'll do it for you. But in theory, it's still automation. It's
just trying to make it easier. Exactly. The US versus Brazil. What is one word for the biggest
cultural gap in each market and how they respond to automation? I mean, it has to be one word. I
would say agility slash velocity. Ooh. Okay. All right. WhatsApp as a
CRM channel, is that inevitable in North America within the next five years, or is it permanently a
Latam and APAC thing? I hope it's inevitable because it's a really relevant channel, but
to be honest, I'm not very optimistic about it because it really hasn't taken off yet in the US,
even though, yeah, it's one of the major channels in mostly all the other countries around the
world of the world. So and it's been like that for a year. So not very optimistic, but I would hope
that it does. Yeah, it's kind of mind blowing. How long? If you have any sort of international
friends or family. If you're using it, you've been using it. It's just if you're based in America and
don't speak outwardly, you don't use it. It's very strange. Yeah. And it's a really good channel. I
mean, I get impressed by. I think you guys, like, use SMS a lot, you know, in different ways. But for us,
use, like, WhatsApp is so different. It's it's like much better in some, some way. So yeah, it's gonna
be interesting with RCS and how that re maps everything. And if it does even in other
countries. Yeah that's true. And it's funny because it's owned by meta. Right. So I wonder what
strategy they have for the US since it's been like like that for so many years. Okay. Buy or
build for AI tools. Which do you trust more? Can I say both? Fair buy when you can view and
build where you shouldn't buy? I think that's my philosophy. For example, our AI chatbot is a
good example. We use the vendor called Spera because we just couldn't build something like it
with our current capabilities, right? But on the other hand, we've built custom AI workflows where
we saw it was probably the best option because we would be able to move faster, and it makes made
sense to that point instead of like buying something for that specific use case. That makes
sense. Okay, last but not least, what is your hottest take? I would say it shouldn't be
consolidate or I mean, the question shouldn't be consolidate or best of breed. I think it could be
consolidate and best of breed. So kind of explaining a little bit, I think we should try to
consolidate the foundation. But best of breed, where there's clear incremental value and
differentiation. I think that's probably my my take. I wholeheartedly agree, if only everyone
thought that way. All right, let's get into it. So underneath all of
our conversation is a real tension in your own career. You've built a career arguing the best of
breed. Contextual fragmentation is not a failure state. It's sometimes the answer, but you cut
hundreds of thousands of dollars through consolidation, and that's no longer a
fragmentation win. That's a consolidation. One, to your exact hot take point is context dependent.
Best of breed or a real framework or more sophisticated way of saying you couldn't get the
budget to consolidate, so you made it work. I do believe the context of pain and best of Breed is
a real framework, and I think it should be because, to be honest, consolidating saved us money. And I
think as martech leaders, we should always aim for that, you know, cost savings and also, you know,
management effectiveness for sure. But I still believe that fragmentation when it makes sense is
good because different tools do different things. Well. And, you know, let's be honest, there's no tool
in the market that can consolidate everything that the marketing art needs to do, right? So even
if you consolidate a few pieces, you would still be using separate tools for separate use cases,
because there's no tool can account for all the channels or tactics that your marketing team has.
And again, just to be a little more practical, I think going back to the AI chatbot example with
before signing with Spera, we actually evaluated 13 different chatbot vendors to make sure we were
making the right decision. And one might ask, okay, well, but you use Marketo as your marketing and
automation platform. They do have a web chat bot or well, use Chili Piper for, you know, meeting
booking. And they also launched a AI chatbot. So why didn't you go, you know, with them? I
think honestly, even though like those chatbots are good for our use case, it didn't work well. So
we wanted something that would enable what we ideal scenario. Right. And that's why we thought,
okay, well only Sarah could do that and we signed with them. So that's you know why I think that
when there is incremental value and it makes sense, you know, Best of Breed is a good path
because they outperforming. That's a specific use case that we had in mind. Agreed. But also 13 that
is a big eval in RFP. How long did it take your team to do that? I think they're kind of two
categories of martech tools right. Like one is, let's say, the big martech tools that are kind of
like core and foundational maps CDPs. I would probably not evaluate 13 for those.
And they also take more time because there are a lot they have a lot more requirements. I see the
web chatbot as a JSON one, and that's why I think the evaluation is we could be we could move
faster with it and be a little less tricky. Yeah, it was kind of quick to rule some of those out. So
we started with 13. But after the first round, like we could rule let's say six out because they just
didn't have what some of the basic functionality we were looking at. So it eventually went down to
let's say the main three. And then we decided to okay, maybe let's run a POC with the main one. And
then it worked and we just move forward with them? Understood. Okay, well, we've been talking about
consolidation and best of breed in terms of like reducing budget and making sure we're being smart
about our finances. Would your perspective change if you had an unlimited budget? I honestly, I
think it would stick to the same philosophy. I would consolidate the foundation, especially by
foundation I mean where data orchestration, governance and even analytics live. But I would
intentionally spend on best of breed tools where they could create incremental value or deliver
the specific use case that we have in mind. Um, now, if you allow me, let's complicate a bit
because budget is, I guess only. Yeah, only one part of the question is budget. But there's also like
team resources and business prioritization which which is I think such as important. So for example,
imagine that there's a vendor that claims it could consolidate three of your, you know, main
tools so you could consolidate three into one. I mean, it makes sense in theory, but especially if
it's a big tool to make that consolidation transition would take a lot of effort in time,
like at least a few months, you would probably have to dedicate a team only to that, or at least
like two team members. That would have to be exclusively working on that. And the reality is
that the business doesn't want to stop doing other things for a technical work or for
something that would just improve data governance. You know, they want to launch new tactics, new
channels. So the question is, are we willing to stop or go slower with other growth related
initiatives just to make this better data governance, you know, transition. So yeah,
so it's honestly like a business strategy and business prioritization decision. And sometimes
it's good to kind of to just not have the ideal stack. As long as you were kind of delivering the
results that businesses wanting because one of the martech goals is also, you know, make sure we
enable the the business needs. Right. 100%. Okay. So I want to dig into one of the channels we've kind
of alluded to. So you ran a WhatsApp pilot in Brazil and you had a 30 x ROI. If you
tried to port that exact playbook to the US market tomorrow, what would break first, the
channel, the message or the customers expectations? The channel for sure, because it's
basically WhatsApp is core to how people communicate here in Brazil. So it's kind of people
use it daily like many times a day. It's yes, the main channel we use for everything. That's why
it's so relevant. Right? You have the rich is so big, right? It's just not the case for the US, right?
So even if you use the same strategy, same message, you wouldn't work I guess, because people are not
using it. Right? And also the rich would be limited, right. Because less people using it, the rich would
be limited in there. And so the impact would be. So yeah. Let's move from the philosophy to the actual
plumbing, because you've actually built most of the things that people just talk about. And so
you've built this AI agent yourself. Rather than buying into Salesforce's Agent Force, Adobe's AI
assistant, HubSpot sprees, you name it. Every platform vendor says agent AI, which we've already
kind of talked about, but those all belong in that suite. So what do you think they
get wrong? And at what point does a custom build become a maintenance liability versus a suite
that could just be absorbed? Yeah, that's an interesting topic because honestly, we built the
agent ourselves because there wasn't an option within our stack at the moment. And we really
needed that process enabled, like as soon as possible. And I think that connects to what I was
saying before to allow fragmentation when you have, you know, a good business case for it or you
have to move fast, right? However, I think this is the interesting piece. You realize later that one
of the platforms that the sales ops sales ops teams actually use would enable the same
functionality, and they would also be able to maintain it for a similar cost. So we then decided
to shift that flow to them, because then the mob team wouldn't have to spend time maintaining it,
and the cost was basically the same. So when we had the opportunity to kind of consolidate into
an existing tool, we did it. So but when we couldn't have that option, we kind of just build
it ourselves. And also, I think the landscape has evolved since then, and Marketo has introduced its
own AI assistant, a AI agent features. But that was that only happened like two months ago. If I'm not
wrong and but so when we launched it, we you know those weren't available. Now they are. So if it was
today, I think before trying to set up those workflows ourselves, I would probably try the
features within our stack first. And I would only try to build something else if we kind of prove
that it didn't work, or if we had, like a really good reason to. Well, spoiler alert, I have yet to
hear good feedback about it. Yeah. Although I haven't tried using it as much as
I should, but yeah, haven't really seen game change or anything. Same boat. Okay. How are you
measuring quality of leads? And how do you know that the model isn't just getting better at
filtering filtering leads that are hard to convert rather than leads that are actually
unqualified? Because those are not the same thing as much as sales would like to pretend they are.
Yeah, we have different layers of qualification, right? We use the kind of traditional sales
qualification criteria like marketing qualified leads, sales accepted leads, sale qualified leads
and etc. basically, what we wanted to have was a better marketing qualified lead because, um, I
think one of the challenges in the martech landscape here is we are a B2B, to C
company. We sell the mind body platform to business customers, but we also have the Mind Body
app for consumers that want to book classes. And although we have distinct websites for H,
there's still a lot of consumers that go to our B2B website and end up filling out forms saying
they want a demo, which they clearly don't write. They just want to sell something related to
booking, or actually just want to book a class and don't know how to do it. One of the challenges is
how can we fit there all those consumer leads, at least to kind of route them to the appropriate
place and not to the sales reps, right? So that's where the agent really helped us to kind of
figure out, okay, well, these are likely consumers and these are likely businesses because. So it is
easy if your prospects actually all have business email domains, which is not always our case
because we do serve very small businesses or solopreneurs that use Gmail addresses Hotmail
addresses. So it's a little more challenging. So it's a perfect job for AI or otherwise, you would
have to have a rap kind of, you know, looking at each specific record to figure that out. So are
you relying solely on the agenda AI for this filtering, or do you also have a human in the loop?
Because to your point, if you have a boutique studio, they might actually want to talk to sales.
Yeah. Good point. So before actually launching it we back tested it. So I think I kind of
liked the idea. So basically we launched the model but we didn't take any actions in the beginning.
So we launched the model and then just let the model kind of run and categorize those leads for
certain. But for a few months and only after we had enough data, we then back tested the data with
the actual Rap disposition for those same leads. And then we were able to compare like, well, who AI
is actually saying it is a low quality lead against what raps actually disposition as low
quality leads. Once we saw that those predictions were really accurate, only then we decided to take
actions. So that kind of helped us to make sure they were being good predictions and were doing
what they intended to do. I'm going to push you again. Do you re-order this every so often, or is
it a continual conversation, or is it kind of a set and forget. Yeah we do re audit it and we'll
definitely keep doing it since it's recent. But also those low quality leads like we're not
gonna just block them or filter them entirely. We kind of like we just work on this, but we have
like a separate prioritization queue for them. So we're not going to remove them entirely from the
flow. For those that are in that separate pool, we can some of those might still be relevant leads
and we don't want to miss them. As for the consumer side of things like if, well, what if they
were consumers and if they have questions? And now we were not going to be answering because we're
not going to be connecting them to sales. We do have some processes in place to if we do believe
that there were consumers in have like real questions, we'll route them to the customer
support team and to the appropriate team. We just don't want to take the sales team bandwidth to
handle things that are not within their scope for sure. I think it's one of the hardest parts of B2B,
B2C or B2C to be, depending on where you started. When I was at Grammarly in-house and building
this out, we would have students who were actually the original audience of Grammarly. It was
originally just built for students, and they would unintentionally or accidentally submit
for contacting sales when really they just wanted to discount or they meant to sign up. Yeah, and it
was a daily, daily struggle of trying to make sure we didn't speak to
them on the B2B side of the house and instead suppress them, and then made sure that they made
themselves available to the B2C lifecycle team, uh, in nurturing them in their
own journey. Exactly. Yeah, it makes sense. It's similar to what we're doing here. And also, I mean,
the kind of filtering consumer is just one of the pieces of the agent that we build. But we also
have some sort of ICP qualification within the agent as well. That kind of help us to with
prioritization, right. To prioritize the better leads. But we're not going to take any filtering
actions for those for sure. A couple of different layers that will help us both hopefully
prioritize the better leads, but also be more efficient by not focusing on leads that are not
true leads like your consumers, just with support questions for example. Exactly. I think the beauty
of these, this use case with agent AI is it's everything we've been doing just with
some elevated features. It's lead scoring, lead grading, ICP fit. It's all of those things in
one. And then the extra filtering of okay, lets air traffic control to the correct team
as per their profile and request. So that's where I think the agent piece helps, but still under the
umbrella of league qualification. Exactly. And to your point, earlier, it's kind of like the
foundation was already set. Now it's the additional accouterments or the additional
benefit. And then is your agent outside of your primary martech stack. And
in terms of like your Salesforce instance, your Marketo instance, and then it filters back in or
gets pushed back into Marketo or what is the mechanism? Yeah, it's pushed back. So we started
by pushing them back into Marketo. Now they're pushing them back in directly into Salesforce. So
yeah it's basically it's a it's a process kind of like outside of the main stack. But yeah the data
is kind of managed and pushed back to the to the main platforms. So yeah, kind of everything happens.
You can see everything there within the stack is just like, yeah, the AI process is happening around
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All right. Switching gears slightly, the you reported additional conversions from your meta
server side work. And the additional reported is doing a lot of work there. So. Net new conversions
or conversions that were already happening but weren't being attributed. Those are completely
different business implications. And I want to know which you're claiming. This is related to
setting up copy or server side conversion integrations with meta Not only like we've done
that with many other channels as well, because when I joined, unfortunately we only had client
side tracking, or at least for most of the events, and now we actually have probably more than 15
events being tracked, both client side and server side, and also offline events. So this is related
to us like working on the server side connection to meta and copy. And yeah, to your question kind
of like a mix of both right. So two things. It's related to conversions that weren't being
attributed and conversions that meta didn't have visibility that were happening because we not
only are now sending conversions that are attributed to meta campaigns, we're actually
seeing all conversions, all CRM conversions that happen, you know, in our business to meta. So it can
basically seize everything that's happening, like get all the conversions, then reads that not only
the ones that are attributed to those specific campaigns. So it's not really incremental
conversions that actually happen. It's I would say it's incremental track conversions. However, I will
say that when Smarta tracks more conversions and can attribute more conversions, it does help to
confirm more, right? It kind of enhances the outgo in the feedback loop so it can improve and
optimize, you know, the conversion tactics. So when you're deciding on a channel strategy for a new
region, what are you looking to to drive the call. So as a data you already have local
team input or purely trial and error? I would say it's a combination of all of them. So usually
starts with research. You know, make sure you understand what actually works for that region,
which includes team input, you know, and feedback from locos. And from there I would say it's try
and nurture. Right. Like you have to start with at least at research to narrow down to the specific
things you want to test. And then honestly, you're only going to know if it really works once you
test it. And yeah, like a good example is for example, we know that G2 the software review like
website is really strong in the US and across other regions as well, but primarily strong in the
US for the software B2B landscape. But Trustpilot is stronger in Europe, especially
UK and, you know, countries around the UK. So this is a good example where by research and team
local input, you kind of understand, okay, maybe we have to have not only like a software review
tactic for G2 in the US, but, you know, a different one for Trustpilot in Europe. So we can make sure
we have a, let's say, good software reviews in different regions. I had no idea. That's a good one
to know. Yeah. And that that came from sales reps feedback. So it's good. Okay. Yeah that's great. I
mean I'm not a fan of G2 personally, only for the fact that it's very incentivized. But is
Trustpilot incentivized in the same capacity or is it kind of set up differently? They work a
little bit differently, especially in the way that they work on incentives. So it's a different setup
and different tactics. But I think the the strategy behind it is the same. Also, I will say
though, that G2 reviews in Trustpilot reviews and basically any sort of software reviews are now
influencing SEO and Geo a lot. So they're becoming increasingly important now in this age.
Very, very true. Okay. You've alluded to it in different ways, but I'm curious. You launched
an agent AI chatbot on WhatsApp before that was really even something available to the market in
2023. What all did that entail? How did you foresee the vision for this? Tell me
everything. This was in my prior role. We didn't really have a great business case to start
testing it. We kind of started with stakeholders saying, well, there is this American startup we're
partnering up, partnering up with because the company actually invested in them. And we believe
that we should use what they're developing. But we didn't really have clear KPIs for Business Case.
And me as the martech leader there. They said okay, well, let's figure out the business case. You know,
one of the main KPIs was kind of revenue. But anyways, I think the goal there was. Okay, well, we
do send WhatsApp messages. It's a really relevant channel. And just, you know, as a baseline here, like
at least on a fashion retail landscape in Brazil, the open rates got up to almost 80%
click rate to about like 30%. So yeah, it's a very engaging channel. But we thought, okay, what if we
have an AI agent inside of the WhatsApp. So you can start with a campaign or you can just have it
as a, let's say like more like a receptive channel as well. But what if we have AI there that can
kind of chat with customers and the the overall goal is can we kind of have a personal stylist in
there? So like we could actually say, well, tell me about what are the type of, you know, your kind of
what outfits or what lifestyle you like. And I'm happy to suggest looks for you. So you kind of
started with that idea, uh, again, early days of ChatGPT and, you know, lambs being pushed to
marketing tools. But it was really cool, actually. We saw that customers really started to engage,
and they started to say, well, I'm looking for let's say, um, you know, jeans or a black t shirt.
And then the I would kind of figure out like, okay, well, this is your lifestyle and you're like a
couple options that you might like, you know, would actually add the images, the links, like five
different images and links of different, you know, combinations of clothing and add that to the
website conversation. So that was again, that was really cool because was in the early days, it was
kind of like a genetic, even though that term didn't even exist, was basically just generative
AI llms. And yeah, it was honestly like a fashion stylist recommending things. So it was not
only answering questions through LM, it was more like recommending things by understanding what
you want and also bringing the products from our e-commerce so that customer could actually see
the image and then click and make the purchase. So yeah, that was kind of like the the whole idea of
that use case. On top of that, with open tax questions and answers. You were also able to
gather a lot of behavioral and interest information from your customers that we didn't
have a way to do otherwise. So, for example, if you started saying, well, I actually prefer a red t
shirt or I'm looking for a red dress or things like that, that was like really relevant data for
us to gather that we hardly could get by just your website browsing activity or your engagement
with your CM campaigns. So one of the goals was buy the things that the customers are saying. We
will probably have a much better refined segmentation as well. That's awesome. And I think
truly is one of the best use cases because it makes so much sense. Yeah. But then of course, there
is that inflection point where, okay, now it actually needs to be hand off to a human. That's
definitely important, especially if those are like support related things that AI can solve. So we we
had to think about that hand off layer as well. For sure, there's nothing more infuriating when
you've got a complex situation and you're like, I need to talk to a human. Yeah, yeah. AI just says, oh,
sorry, I canceled that. You need to connect with the human, but then it doesn't connect you with
the human, just as they need to do, and doesn't give you a number or a link to connect to them. So.
And also the context doesn't travel. And it's just like, yeah. Oh, this is so frustrating. This
shouldn't be so hard for any consumer, especially those of us who know the inner workings of how
this backend works. You have the data. So Marketo is for the B2B side of the
business. Bres is for the consumer side of the business. You've got two different platforms, two
likely different data models. As a result, two different sets of integrations all inside one
company and by your own framework that is the vertical appropriate fragmentation. But from a
data standpoint, do you know what's working across that split? Are you running two visibility regimes
and calling it a strategy? Or how are you managing this so that both sides of the business can see
what's going on, or at least know that they're not interfering with the other? Yeah, that's a good
push. So we kept them separate on purpose, because we just saw that trying to
merge everything into one system, like the customer and consumer database just
risked a bigger data mess than running two clean, separate platforms. So that was the decision that
we took, especially because the relationship between the different, like the data hierarchy for
B2B is it's much different than the data hierarchy for B2C. And also, I inherited that setup
from before I joined. And and it's working well. So we just decided that well, trying to make a big
data consolidation change was not within the top priorities for the past cycles. That kind of
relates to what I was saying before that. Like our martech strategy needs to be connected to the
business strategy, right? So for us, it wouldn't be worth it to spend such effort to consolidate that
if it's working. And we had like other better priorities. Agreed. I think a lot of people are
just looking at the next shiny thing like, oh, they'll do it better. Like, if it's not broke, don't
fix it. Yeah. Unless there's like a really huge cost saving, which most of the
times it's not the case. I mean, there's usually like a good cost saving opportunity in
consolidating, but sometimes it's lower than the cost of having to work through all that while
going lower and stopping other initiatives. Exactly. People don't factor in always there.
Sometimes they forget how much of a cost, both resource wise but budgetary wise, migrating
and or implementing costs on top of day to day. Not to mention net new
initiatives like you can. For the most part, keep the business running as usual, but that doesn't
exactly help the bottom line. Typically, the agent you've set up runs on OpenAI through
Naden and Marketo, and it's built and tuned in English language environment.
Does that same set up perform as well when your qualifying leads in Portuguese or a different
language, or is there a real quality gap in LLM performance outside of English that most US
based martech leaders have literally never thought about? To be honest, I haven't really seen
a real quality gap there. I think now the LMS handle other languages pretty well at this
point, and the output still comes back in English for us. So honestly, it's been working well for us
as is. I'll say, though, that you do need to create a more complex LLM prompt to
account for specific region scenarios. You know, for example, Arabic letter form fills
for eastern, Middle Eastern. It should be fine. But like if you start to get Arabic form fills for,
let's say the UK, it might not be as a good quality lead that's coming there just because, you
know, our sales team in the UK won't be as good as the Middle Eastern team to support those type of
leads. So you do have to take like to kind of give the prompt some additional context, local context
to, to be able to, to run properly. But it's not really like a language issue. I think it's more
like some context that if the Elm just read like the plain language, it would probably not get. And
that's a nuance that you also have to learn as your team is growing or changes and people move
to different roles. It's a tricky one that I don't know if any lead routing
part of the business can actually sort out correctly without some human intervention in
those corner cases, maybe at some point in the future. Now I want to take a step back. We've been
talking through some incredible strategies and implementations you've conducted, and because our
industry data is telling us something different than the story, typically than we're living with,
and platform vendors keep selling consolidation. And they're almost always us built
us tested. And when you're running a global platform optimized for American channel
norms, but it's deployed in places like Brazil or Southeast Asia or the Middle East. Who is being
left behind, forgotten, and what are you actually losing? I would say that now the major platforms
already have the capabilities that are needed across different regions. So for example, WhatsApp
connection, I think most of the major platforms already have that integration. That's sad. I still
believe that the platform's roadmap is built for its biggest market, and anything outside of that
norm becomes a local teams problem to solve. Right. So I mean, going back to the Nan agent,
it's something that we built because the main platform that we had didn't have a feature that
would enable that. And yeah, and I think that's the case for specific business needs as well. I mean,
the big platforms will hardly have all the features that local teams need because, you know,
each business is so different. There's different business sizes, you know, different company sizes,
etc. and I think they'll always have something that the local team will have to kind of figure
out. And that's where I think ingenuity comes in, which is probably, for me, one of the best skills
that martech leaders in marketing operators should now have. Right. Instead of just sticking to
that same processing platform, they have to kind of be able to figure out how to do things that
that current platform currently did not support because it's not part of their main market. Right?
So agreed. And now I want to double click on that. How do you filter out candidates that will be
on your team that you're hiring for, to ensure that they have that level of not just ingenuity,
creativity and problem solving, but also part of a growth mindset and not fixed to what the
platforms dictate. I think it also depends on the role, right? I like to kind of have different roles
where one is more general and can take off general marketing ops projects. And of course, we
might need one that's more specific. For example, the marketing automation manager, like it needs to
know that marketing automation platform very well. That's sad. I think even for that, let's say
marketing and automation manager, I kind of always like to look at do you have a broader marketing
knowledge? Do you know other tools besides Marketo, HubSpot, Salesforce? Are you willing to learn, you
know, like CDPs. Are you willing to learn how ads channels work? So I think it's really important
that marketing operators now kind of start to have a broader knowledge of the whole marketing
ecosystem. So that's definitely one of the things that I try to evaluate. I see that a lot of
martech leaders say that, well, AI will probably replace the like, very operational tasks,
and I think that can be true. Right. So it's probably one more case for us kind of being
trying to be more general and learn like how to set up different things in different ways so we
can be like always like a valuable asset for any marketing team. Agreed. My favorite way of
finding out if someone has that kind of thought process and way of thinking is an interview
process saying, hey, if you don't know how to do something, what do you do? It's very fascinating
when some candidates say immediately like, oh, well, I'll ask my manager. And I'm like, that is the last
step because the ideal answer is Google it or search on your favorite AI
platform. Search on your internal wiki. And if you come up short in every single one of those, then
ask your manager of like, hey, I've looked all these different places. This seems like very
unique to us and not documented. Could you explain so that I know how to further explore do this
project? I think the best answer to that would be I'll figure that out, or I'll try to figure out if
I can't. Then I'll come back to you with the options that I looked at, and you help me to
assess it. I like that mindset. And we actually honestly, I think 1 or 2 years ago we've been
through situations like this where like the business needed to connect. Like, well, we also use
Pendo for in-app in product communications, but it's not really martech on. It's kind of owned by
the product engineering team. But however, it's a really relevant tool for us. And we did. We
needed to kind of set up some integrations between Pendo and Marketo. And they have they had
like different data architectures. But we figured out we just thought, okay, let's research. And we
saw that, well, through a Zapier integration and some like JavaScript work that AI helped us to do
because I don't know JavaScript. But with AI, I was able to create the code we were able to kind of
solve. And we actually launched a really cool product launch. Yeah, that's a good example of well,
I don't know, but I'll try to figure that out. I'll see if I can connect the pieces. If I don't, I'll
come back and maybe we can figure that out together or something like that. Yeah. No, that's a
great use case. And also Pendo is very good for product analytics. So I think you could do some in
product comms and things like that. But like for product analytics I think it would still be best
suitable. But yeah I really wanted to do some braze in product comms and testing would actually
give marketing more flexibility and agility, and would also allow us to do like some cool
personalized campaigns. But we kind of depended on product to set up the Grays integration because
it involves like product coding and things like that. And again, it came down to not being like the
biggest business priority for the product team. And we kind of okay, well, we'll accept it until
the time comes. So as martech leaders, we should be able to kind of adapt to that. Agreed. But also not
forget that, hey, you know, we can do this, or at least we can add in an initiative so that there
are options for marketing campaigns, you know? So yeah, make sure you show that present that roadmap
to stakeholders, flag the blockers. So we're like at least everyone is aligned in agree on the
limitations in the decision. So like it's not like a martech fault is like a business decision.
Agreed. So we've been talking about just essentially building the connective tissue to
something that doesn't exist and having to not so much make makeshift solutions with
duct tape, but you have to creatively solve these things until the market catches up, which is
hopefully going to happen at some point. Yeah. And what does this mean for either a new person on
your team or the next person who touches it when you're one departure away from
losing the institutional knowledge, holding it up? Do you have documentation for all of this? Do you
make it self-explanatory within the code itself? Is there a shared project folder? You name it. What
are the ways you're able to make sure this institutional knowledge of how the systems are
set up stays within the company, no matter what? This is a challenge and I think this
is good documentation. So here we use notion. We have like a marketing Ops in Marketing technology
documentation page where any new thing that we do, we try to document there, including also like all
the existing stuff that we have. Of course you kind of always inherit some legacy work and
things like that. And those are like also worth documenting, but it takes time to do that. But I
would say that anything new that we launch, it kind of needs to be documented. It kind of needs
to be part of the delivery, right? So whenever the person leaves or, you know, team changes will at
least people will see how that was set up. And also now with, you know, cloud, cloud SKUs and
things like that, I think you can really leverage, like cloud to document some of these things and
have some kind of if you're using them for like specific connections or skills, like the skills
itself, have the documentation or will actually show the steps. And it's always helpful to kind
of document that and share it with the team members so everyone has access to it. Agreed. I
think documentation always gets thrown to the wayside. And I'm like, now it's easier than ever
before. Yeah, to have it. Even though I was totally that nerd that I was writing the documentation
with Glee kind of going to not just the larger industry, but really for you specifically, how do
you think being multicultural and bilingual has actually helped you in your role, especially
working with international markets. I mean, besides letting me work directly with my Brazilian team
in Portuguese and with, you know, my US colleagues in English, it also allows me to be inserted into
two different big markets. So for example, I can have different perspectives on where the industry
is headed in two big and great market markets for tech. You know, the Brazilian market, the US
market. Yeah, I think that's a positive thing. And also it allows me to connect to a broader range
of peers in industry leaders and, you know, to learn best practices. Okay. Vinicius, you
make a compelling case that the smartest martech strategy is not always the most elegant one, but
it's the one that really fits the market, the vertical and the moment you're operating in.
Whether that holds up under playlist, Next Chapter will be one of the most interesting sequels or
more interesting maybe in the story. And so thank you for coming on into the hot seat. But before
I let you go. Who is someone we should have on the podcast? And it's always in parentheses because
it's hard to find, but preferably a senior female leader. I have two names in mind. One is
Kali Maya. So she was my one of my managers in, you know, one of my prior
roles. And she has like really good martech knowledge, but primarily as like a business
lifecycle strategy. So I think she'll be a good candidate. But also I would recommend Callaham do
I think that's her last name? Uh, which just took over the martech team for ClassPass? Uh, but she
comes from a great SEO background, so I think she will be a good candidate to you for a podcast as
well. Well, I would love an introduction and thank you so much for coming on. Where can folks
find you? Follow along and hear your insights. Yeah, people can find me on LinkedIn, like Vinicius
Rodriguez. Uh, as you can see the name here, I tend to share some more tech things there, although, you
know, not as frequently, but we can get in touch. Okay. Well, thank you so much.
Before we wrap, here's what I want people to actually walk away with. Not the headline, but the
underneath of it. His AI lead qualification agent ran silently for months, with predictions back
tested against actual rep dispositions before it was ever allowed to act. Which means trusting
an agent with real decisions requires the very unglamorous validation period most teams are too
impatient to build in. The model earns authority, and isn't granted on day one, Marketo
and brace are kept deliberately separate and not consolidated because B2B and B2C data hierarchies
are fundamentally different, and merging them would trade a small integration headache for a
much bigger data mess, which means sometimes the fragmented setup is actually the disciplined
choice, and consolidation for its own sake is the shortcut that costs you later. Also, software
review platforms aren't even global. G2 dominates in the US, while Trustpilot is actually stronger
in the UK and Europe, which means even the research and vendor evaluation process itself has
to be localized to an extent, not just the channels and messaging a company ships to
customers. Thanks for tuning in to the making sense of martech. See you next time. A special
thank you to Christine Murtaugh, who edited this episode. In an extra special thank you to Jenna
Carter for believing this passion project needs business. Stay curious.