3287: Data-Driven Marketing: How Converge Uses Technology to Drive Growth

[00:00:04] Why do so many marketing teams still feel like they're flying blind when it comes to data? And why in an era of cloud computing, AI and real-time analytics are we still seeing bottlenecks? Bottlenecks that prevent timely insights? Well today I'm going to be joined by the VP of Engineering at Converge Performance Marketing Agency. And my guest is going to bring a pragmatic and forward-thinking perspective to a challenge that many businesses face.

[00:00:34] But few solve very well. And that is how to break down the persistent data silos between marketing and engineering. And instead of waiting days or even weeks for an analytics report to come through, my guest believes that marketing teams should have direct guided access to the data that they need when they need it. And anyone that works in a large organization will know that that sounds much simpler than it actually is.

[00:01:01] So today we'll talk about how tools like BigQuery and other low-code platforms are helping to enable this shift. The cultural and operational barriers that sadly still stand in the way. And what happens when you empower marketers with real-time insights without needing them to write SQL or chase down developers, etc. The difference that that can make.

[00:01:24] Ultimately, today's conversation is going to be about autonomy, collaboration, and changing the role of engineering in today's data-driven marketing environment. So, how do you move from dependency to data fluency? And what kind of performance uplift could this unlock for marketing teams across the board? Let's find out as I officially introduce you to today's guest. So, a massive warm welcome to the show.

[00:01:51] Can you tell everyone listening a little about who you are and what you do? Sure. Thank you, Neil. It's a pleasure to be here. My name is Jose Soto. A little about me, I'm a technologist at heart. I love exploring new technologies and finding creative ways to use them to solve problems and really just make life better for me and the people around me. Outside of work, I'm big on the outdoors. My family and I regularly go overlanding. We love camping and exploring new places.

[00:02:21] But more than anything, it's being a father that's my life's greatest joy. It really helps shapes the way I lead and how I approach mentorship and growth both at home and in the workplace. I'm the vice president of engineering at Converge Marketing. We're a performance marketing company that helps brands grow through the power of creativity, data, and technology. And I've spent over 20 years in the tech industry building and scaling platforms across sectors like health tech, insurance, blockchain, and branding.

[00:02:50] So, Neil, what I love the most is that I get to do the work that I'm genuinely passionate about, which is solving real-world problems, mentoring teams, and building these systems that drive meaningful results. It gives me the space to show up as my full self and lead with authenticity. Well, it's a pleasure to have you join me on the podcast. And not only everything that you just mentioned, there may be 5,000 miles between us today, but we both share a love and passion for soccer. And you're a Liverpool fan too, right? Yes, I am.

[00:03:19] So, do you get all the games in the US? Because the time difference must be quite tricky. Do you have to get up early, etc.? We do, yes. We have to get up very early some days. Sometimes we're at the pub at 4 a.m. just so that we can watch the game streaming live with a group of people. Other times we just watch at home using something like Fubo TV on our apps. So, we do get access to these games out here. Man, I love it. I love how you said that we have to get up at 4 a.m. and the bars have to open up. I'd love to see you get there.

[00:03:48] I'd love to see me get that one past my wife. But if it works, go for it. I love it. It does work. And it's a fantastic experience. And the environment is well worth getting up early. Brilliant. And back to the technology for a moment. For people hearing about your company for the very first time, tell me a little bit more about Converge Performance Marketing, your role there. And how does your engineering team intersect with marketing? Because I used to be in IT in another lifetime.

[00:04:18] And the worlds of IT and marketing, they seem to conflict with each other a lot. Sometimes they have different needs, et cetera. One was the guardians of the network. Others need to get stuff out there and promote things quickly. How has that relationship changed? And tell me more about your company. Absolutely. So Converge Marketing is a growth partner for brands. We like to say, forget your agency of record. We're your agency of action. And what that really means is we just don't make recommendations.

[00:04:48] We build, execute, and scale for our brands. We combine data, technology, and creative to help brands grow in really measurable and meaningful ways. So our work spans the full customer journey, everything from demand generation to brand awareness and conversion and retention. And what really sets us apart is how integrated we are. You know, like you were saying, IT and marketing sometimes conflict.

[00:05:14] But in this case, media, analytics, creative, and technology are all working hand in hand. And it helps us stay focused on the business outcomes, not just these vanity metrics. In my role as the VP of engineering, I lead the team that builds the tech powering all of that. So we handle everything from the backend systems that process and connect data to the front end experiences, these landing pages that help brands connect with their customers.

[00:05:42] But more than that, engineering isn't just a support function here. We're really embedded with the media team and the creative team and the analytics team. We develop things like custom tools that help streamline ad operations, dynamic landing pages that adapt in real time, and even data pipelines that give stakeholders immediate insight into what's working. And it's not just for the clients either, because we also build internal tools to help improve how our teams work.

[00:06:12] Automation, efficiency, and usability are really baked into everything that we do. And ultimately, that delivers more value for the brands we serve. And at the end of the day, my job is to ensure that the technology is reliable, scalable, and driving this real growth, both for our clients and for Converge itself. And before you join me on the podcast today, I was reading how you've described a common bottleneck in performance marketing, where engineers, who are the gatekeepers of data,

[00:06:40] and how does that slow down marketing teams in practice? As I said a few moments ago, there's always been a conflict between these two departments. They have grown closer together. But tell me more about this particular problem. That's exactly right. And in a lot of organizations, engineering becomes the bottleneck because data access is tightly controlled. And there could be multiple reasons for that, either security, complexity, or maybe just a lack of tooling.

[00:07:08] But marketing and data teams need to move fast. We're constantly launching campaigns, testing audiences, iterating on our creative. But they constantly hit a wall when they need something, like performance insights, data ingest updates, or maybe even something as simple as a list of leads. And in practice, this means that marketing is submitting tickets, waiting on landing pages to be built,

[00:07:34] or maybe operating off of improperly ingested data that then needs to be cleansed. It really creates friction, and it slows down our experimentation, and frankly, it leads to missed opportunities. And it also puts pressure on our engineering teams to context switch and provide support in a reactive manner, as opposed to focusing on more scalable solutions. At Converge, we've worked really hard to flip that dynamic. So instead of being the gatekeepers,

[00:08:03] our engineers have built self-service systems that empower marketers to get what they need safely and quickly. We put a lot of energy into clean data pipelines, intuitive interfaces, and guardrails that really let non-technical users explore our insights, launch campaigns, even run QA testing without breaking anything. So it's about shifting this mentality from the, you have to ask us, to this mentality of, we've built the tools so that you can do it yourself.

[00:08:33] And there is so much hype at the moment around AI, but of course, AI is only as good and only as useful as the data that you're able to feed into those machine learning models. And one phrase I'm hearing more and more about at the moment is data democratization. So what does true data democratization look like in a marketing organization? And how are you helping to make that a reality at Converge? Great question. Yes.

[00:08:59] True data democratization means putting the right data into the hands of the right people at the right time without needing that technical translator in the middle. In a marketing organization, that means people like media buyers, creatives, strategists, and analysts can all explore the insights, spot trends, and make decisions based on real-time performance without waiting on engineering to run queries or pull reports.

[00:09:29] And at Converge, we're making that a reality in a few ways. First, we built a unique platform from the data up. So after years of experience, we spotted common data patterns that were being used internally across multiple teams, as well as data patterns that our clients were using. And this is all siloed data patterns. So we were able to then wrangle those data schemas, implement standardization by using these proper data models and relationships.

[00:09:57] And that helped us create a unified system that is much more scalable. I can't even begin to tell you how happy that makes our data and analytics teams, because having a standard that works for our first-party data, as well as our ingested third-party data, makes for a much more manageable workflow for that team. And the benefits don't just stop with our data and analytics team either, because we're now able to provide a common interface for product teams, media teams,

[00:10:25] and digital delivery teams to quickly launch campaigns, run quality assurance, and deliver assets on their own. And these were all tasks that had previously required lots of engineering intervention and slower turnaround times. We also spend a lot of time on education and trust. So it's not just about the access, it's about the usability. We partner closely with our marketing teams to understand what it is that they need.

[00:10:51] And we build out the interfaces that speak their language and not ours. Another way that we've made data democratization a reality at Converge is by embracing things like no-code or low-code platforms that provide value that is beyond the means of an internal engineering team. Because our goal is not to reinvent the wheel. If there are tools out there that can benefit our internal teams, we will absolutely evaluate them and see how they can fit into our systems.

[00:11:19] But ultimately, democratization isn't about just opening the floodgates. It's about building smart systems that give people the power to act, both confidently and independently, in the service of growth. And from my experience, keeping those data analytics teams happy is no easy feat, but you've been able to do that. So kudos to you there. And another thing I was reading is you've embraced tools like BigQuery and low-code, no-code platforms and so many different tools there.

[00:11:47] So how do you empower non-technical users to work with data confidently? Because it is a bit of a dark art. It can feel very complex to people that are not from a tech background. So how are you doing that? Absolutely. I mean, tools like BigQuery and other low-code, no-code platforms have really been game changers for us. BigQuery gives us the scale and speed to centralize massive amounts of data from first-party systems, media channels,

[00:12:16] CRM systems, web analytics, a vast array of different sources. But what makes it truly powerful is how we've layered accessibility on top of it. So instead of making internal teams learn SQL or wait on engineering, we can connect BigQuery to tools like Looker Studio for custom-built dashboards that let non-technical users interact with the data visually. And it also allows them to build their own dashboards if they choose.

[00:12:44] They can explore things like performance marketing, build reports, filter campaigns by audience, or even share the reports, all without writing any line of code. We've also embraced tools like KNIME. This is an open-source data analytics platform that enables our users to create visual workflows for data processing, visual workflows for data modeling, and sometimes just visualizations themselves without the need for extensive programming knowledge. And before KNIME,

[00:13:14] if there were ever a need to modify third-party data ingest or modify client disposition ingest, pretty much anything that has to do with ETL, extract transform load, or data cleansing, it would have to go through engineering. And we'd have to either write the code to support that request or modify database stored procedures to do so, which is not a small task. Engineering was the bottleneck at that point.

[00:13:41] Now, we can update a visual workflow in KNIME with almost no need to touch an IDE. So it's all about reducing the friction. When you give people interfaces that speak their language and abstract away the technical complexity that they're not even interested in, then you not only move faster, but you also create this culture where everyone is more curious and more data-driven and empowered to take action on their own. So just to bring to life some of the things that we're talking about today,

[00:14:11] what are some of the early results or even benefits that you've seen from giving marketers direct access to data? And have you noticed any cultural or operational shifts? What are you seeing here? Definitely. Giving marketers direct access to data has led to some very clear wins early on. And the most immediate benefit was speed. Campaign optimizations that used to take weeks, generating specifications for API interactions, lead forwarding logics, updating geofencing,

[00:14:41] even ingesting disposition data, all of this can now be done in days. And most of that time is just inherent with the systems that we often have to interface with because we have to deal with partners and clients during the quality assurance process. But it is a vast difference from our older system. But beyond speed, we've seen a deeper cultural shift where marketers are now asking better questions. They're digging into the performance trends, spotting opportunities,

[00:15:10] and challenging these assumptions that we used to make because the data's right there and they're confident in using it. It's created a more collaborative environment where decisions are made based on what's actually happening and not just this gut feeling or anecdotal feedback. But operationally, it's reduced the load on engineering as well. So instead of triaging ad hoc data requests, our team is now focused on building smarter systems and scaling capabilities. So now instead of acting like a support desk,

[00:15:40] engineering is seen more as a strategic partner, which is exactly the kind of relationship that we want to have. And data silos are still a big problem and departments are notoriously protective of who can and who cannot access their data. So from your engineering perspective, how do you maintain proper data governance and integrity while opening up access to broader teams? It must be a problem that you encounter a lot. I know it always has been. I suspect it always will be a challenge of sorts,

[00:16:10] especially when AI needs access to the data. How are you dealing with this problem? And that's the key challenge. And it's where a lot of organizations will get it wrong, where if you just open up the floodgates without a plan, you risk creating more chaos. Things like inconsistent definitions, duplicate records, disparate spreadsheets across multiple users and decisions being made on bad data because of all of this. So from an engineering perspective,

[00:16:39] our approach to data governance really starts with structure. And we have a unique challenge in that we're not fully in control of how different partners manage their data, nor are we fully in control of how different clients manage their data. And we need to interface in both directions. And even more so, layers of complexity exist on top of the data structures themselves. For example, one client may consider a potential lead outside of their geofence in one situation, but not in another.

[00:17:08] And it works differently for a different client. And this is just one small example. These are things that we all have to, we have to take all of this into consideration when building out our data structures. So that's the challenge. But you asked about how our approach looks. Yeah. So first we centralize and standardize our first party data. Then we standardize the translation between our own data and that of the partners in our clients. So that means we have our own internal

[00:17:37] Rosetta Stone for data mapping for things like clean data pipelines. And this provides that single source of truth that we then store in a system like BigQuery. We invest upfront in data modeling so everyone is speaking the same language, whether they're in marketing, whether they're in sales, or whether they're in analytics. Then we have to apply the role-based access control. This is key. And build interfaces tailored to different needs. A media buyer might see campaign performance

[00:18:07] and audience insights, while an executive dashboard may show higher level KPIs. So we're not just giving access and keys to everyone for everything. We're giving them access to what's useful in a safe and structured way. And finally, we bake in observability, tracking how data is being used, flagging anomalies, and making sure that there's always this feedback loop between our users and our data team. Governance isn't a one-time setup. It's a living system that evolves

[00:18:37] as our business grows. So the goal is to empower, not overwhelm. And it's to make trust in the data, the default and not the exception. And if we were to look further ahead, I'm curious, how do you see AI and machine learning further transforming these data-driven decision-making, this area that everyone's trying to get right at the moment, especially in marketing teams? How do you see all this evolving?

[00:19:05] And what role do you see yourselves playing in this too? Oh, yes. AI and machine learning are already reshaping how marketing teams make these decisions. And we're really just getting started. So up until now, a lot of data-driven marketing has been about hindsight. Things like pulling reports, analyzing what's happened, and reacting to that data. But AI helps shift the dynamic towards foresight and automation.

[00:19:33] I see three major transformations coming. First is in predictive analytics. Things like forecasting customer lifetime value, churn risk, or the likelihood of a lead even converting. These models allow marketers to prioritize efforts and allocate budgets with more precision instead of just relying on broad averages or past behavior alone. Second is creative and messaging optimization. Machine learning can now analyze which headlines,

[00:20:03] images, or formats perform best with different segments. It's wild. And it can even generate new creative variations at scale. So this kind of real-time adaptability is going to raise the bar for performance marketing. And third, I think AI is going to start driving a lot more autonomous systems. We are already implementing early versions of features in our campaign platform that will adjust bids, pacing, or even creative on the fly based on performance signals.

[00:20:33] So for marketers, that means less time on manual tasks and more time on strategy and storytelling. From the engineering perspective, our job is to build the infrastructure to support all of that from clean, timely data to this transparent feedback loops. So AI isn't just a layer that we bolt on. It has to be integrated thoughtfully with trust and business context built in. And earlier in our conversation today, you were talking about how you empower

[00:21:03] non-technical users to work with data confidently. And I think there's a real pressure on everyone to be in a state of continuous learning. So I'd love to throw this question over to you now. How or where do you self-educate? How do you keep up to speed with this pace of technological change that we're all grappling with right now? There's definitely pressure these days to always be learning. And I think it's a good thing, but it's easy to forget that not all learning comes from a course or a book. I've spent

[00:21:32] countless hours with self-learning videos and reading. And while this is all valuable, very valuable, I find that the real education comes from experience. Just being in the thick of it, building, failing, adjusting, and trying again. That's where the real growth happens. I also think one of the most overlooked learning resources is in your own team. Most of us are surrounded by people with a ton of expertise and it's just sitting in their heads. So I try to stay curious,

[00:22:02] ask questions, and create that space for the kind of conversations that bring out these new ideas. So just listening and learning from the people around you can be incredibly powerful. And at Converge, we've made that kind of learning part of our culture. We hold regular workshops and town halls specifically designed to break down these knowledge silos. So whether it's an engineer walking through a new system that they built or a strategist sharing campaign insights, the sessions really help these teams

[00:22:32] understand each other's work. It helps us ask the right questions and build stronger cross-functional collaboration. It's a simple way to turn everyday work into shared growth and it's a ton of fun. And honestly, the best way that I've found to learn is to teach. If you ever have the opportunity to do so, I highly recommend it. Explaining something forces you to truly understand it. And so I try to stay open and share what I know and help others grow because every team,

[00:23:02] every time I do that, I grow a little as well. Fantastic advice there and a perfect moment to end our conversation. But before I do let you go, for anyone interested in exploring anything that we talked about today in a little bit more detail there and contact you or your team or just find more information and keep up to speed with the work you're doing, where would you like to point them? Sure. The best place to connect with me directly is on LinkedIn at Jose Can Help. And in the spirit of branding, you can find me

[00:23:31] at Jose Can Help on most platforms. It's my own personal brand. But if you'd ever like to learn more about the work we're doing at Converge, head over to convergemarketing.com where we regularly share insights and case studies and you can take a closer look at how we can help brands grow through data, creativity, and technology. So I'm always open and happy to connect, exchange ideas, or just talk shop. So please don't hesitate to reach out. And any Liverpool fans out there, they welcome to contact you too?

[00:24:01] Yes, only Liverpool fans. Absolutely love it. And we covered so much today from the data bottleneck, how traditionally engineers have handled data requests causing delays and hindering marketing agility and also the benefits of data democratization on the flip side of this, increased agility, reduced engineering overhead, improved decision-making, and enhanced collaboration. I'd love to hear from people and what they thought of the conversation today and how they're using AI and machine learning

[00:24:31] to further democratize data analysis and enable so many more sophisticated insights. Lots to talk about here, lots to take away, but thank you for sharing your story with me today. Thank you, Neil. I think as we've heard today, the future of marketing isn't just creative, it's computational. And it starts by rethinking the handoffs that slow everything down. So when marketing teams no longer need to rely on engineering for every single query, and when they're equipped with the right tools

[00:25:00] and the right guardwails, and when governance is in place without creating more red tape, that's when something powerful happens. Insights speed up, decisions improve, and engineering gets to focus on building, not just babysitting dashboards. So whether you call it data democratization or just good old-fashioned common sense, it's clear that organizations thriving today are going to be the ones that are creating a shared language between technical

[00:25:29] and the non-technical teams. But what do you think? Are we finally at the point where marketing can be data enabled as it is customer focused? And if you're on the other side of the fence, if you're in IT or in engineering, does this approach sound like a relief to you or more of a risk? Join the conversation, share your thoughts, we'll keep exploring where tech meets real-world impacts. You can message me, techblogwriteroutlook.com, LinkedIn,

[00:25:59] X, Instagram, just at Neil C. Hughes. But that's it for today, so I will speak with you all again very soon. It's time for me to go now. Bye for now. The time for now is now the person who has We'll see you next time Thank you.