What’s up everyone, welcome to our first episode of 2025 – today we have the pleasure of sitting down with Austin Hay, Co-Founder and Co-CEO at Clarify and Martech Teacher at Reforge.

Summary: Something extraordinary is brewing in the world of martech. In the near future, Austin thinks AI agents will turn into an omniscient digital butler, anticipating your needs with uncanny precision while vanishing into the background of your workday. But the real revolution unfolds in the seemingly mundane machinery of marketing operations, where innovative companies are transforming their spaghetti mess of data pipes and platforms into something approaching digital poetry. The fundamental building blocks of our systems aren't disappearing, they're gaining superpowers. Hear it from one of our industry's most thoughtful builders.

About Austin

• Austin started his career at Accenture but he left the Fortune 500 world to join a startup called Branch where he became the 4th employee
• Austin then created his own boutique mobile growth engineering consultancy. He grew the practice to 1.5M with big names like Walmart, Jet, Airbnb, Foursquare and more.
• His consulting practice was aqui-hired by mParticle – a leading CDP solution where he would eventually become VP of Growth 
• He later joined Runway as VP of Business Operations
• He also started building The Marketing Technology Academy – an online learning center for martech which he would eventually sell to Reforge and become the Instructor for the new Martech course
• He was also Head of Martech at Ramp, a fintech startup
• Last year, Austin strapped on his jetpack and became a product founder at Clarify conquering SF and Hubspot and building the first flexible, intelligent CRM that people actually enjoy using. 

AI Agents and the Hidden Promise of Ambient Computing

Let's face it, manually feeding context to AI feels a bit like teaching a fish to ride a bicycle. Current AI systems, brilliant as they may be at crunching numbers and crafting responses, still stumble around our digital workspaces like a tourist without a map. Sure, they can write a decent blog post or solve complex equations, but they're essentially working with one hand tied behind their virtual back.

Now, imagine your AI assistant as more of a digital detective, quietly observing and understanding everything happening on your screen. No more copying and pasting chunks of text or explaining what's in your Notion workspace for the hundredth time. Picture having a conversation with your computer while it maintains an almost supernatural awareness of your digital environment, from those buried Slack threads to that spreadsheet you've been avoiding. Recent demonstrations, like Kieran Flanagan's adventure with Gemini's screen reader, hint at this future, even if current versions move with all the grace of a sleepy sloth.

The real magic kicks in when we start thinking about operating system-level integration. Platform-specific AI agents are like horses wearing blinders; they can only see what's directly in front of them. But desktop applications from companies like GPT and Anthropic are pushing toward something far more interesting: AI that can understand your entire digital world, not just a tiny slice of it. It's the difference between having a personal assistant who can only help you in the kitchen versus one who can manage your entire house.

Here's where things get particularly juicy: this isn't some far-off sci-fi fantasy. We're looking at a five-year horizon where the clunky, permission-asking AI of today evolves into something far more sophisticated. The transformation won't happen overnight (sorry, instant gratification seekers), but when it does, we're talking about a 10x boost in productivity that makes current productivity hacks look like using a butter knife to cut down a forest.

Key takeaway: While today's AI assistants feel like overeager interns requiring constant supervision, the next five years will usher in truly ambient AI that seamlessly integrates with our operating systems. The future isn't about teaching AI to understand us; it's about AI that already knows what we need, when we need it, across our entire digital landscape.

The Limitations of AI Agent Marketplaces

The AI marketplace concept raises important questions about automation's role in our daily work. While downloading specialized AI agents for every task might sound appealing, reality suggests a different path forward. Current marketplace models mirror the Chrome extension ecosystem, where tools often remain peripheral rather than becoming essential to core workflows.

Austin frames the central debate in venture capital circles clearly: will we depend on AI agents that require explicit commands, or will we embrace ambient intelligence that works proactively in the background? Looking at the CRM space, Austin points out a crucial consideration that many futurists overlook. You can't simply discard two decades of sales methodology and expect professionals to embrace a completely alien interface. Instead, sellers need familiar elements: contacts, companies, opportunities, and tasks, all presented in recognizable formats that align with established workflows.

The intersection of traditional software and AI becomes particularly interesting when Austin discusses CDP platforms. Users expect certain fundamentals, like accessing persona views and tracking customer behavior. The innovation opportunity lies not in replacing these elements but in enhancing them through intelligent automation. Austin suggests that the key difference emerges in how these agents operate: will users actively assign tasks, or will agents run continuously in the background, performing expected functions without explicit direction?

While some platforms champion what Austin calls the "jack of all trades" approach with Notion-style customizable workflows, he makes a compelling case for specialized, industry-specific solutions. This focused approach, where AI agents operate autonomously within well-defined parameters, might prove more valuable than a marketplace full of generic tools. Austin emphasizes that the more specialized you are in understanding user needs, the more effective your agentic experience can be, particularly when it runs seamlessly in the background without requiring constant configuration.

The reality likely lies somewhere in between these two extremes. Certain straightforward tasks, like data enrichment for new records or basic categorization, seem well-suited for autonomous AI agents. However, more complex decisions involving customer lifecycle management, timing of promotional offers, or predictive modeling for next-best-action recommendations require deeper integration with historical data and sophisticated propensity models. The key to success may not be choosing between marketplace agents or integrated solutions, but rather understanding which approach best suits specific use cases and organizational needs.

Key takeaway: Success in AI automation won't come from marketplace-driven point solutions but through deeply integrated, industry-specific AI that enhances existing workflows while maintaining familiar interfaces. Austin's insights suggest focusing on building AI that complements rather than replaces established business processes, creating tools that feel natural rather than revolutionary.

The Core Primitives of Martech and the Path to Self-Designing APIs

Ever tried explaining Bitcoin to your grandparents? That's roughly how it feels watching companies try to skip straight to AI automation without understanding their data foundations. Austin breaks down the concept of primitives in martech, those fun...

What’s up everyone, today is our last episode of the year and if you paid attention to the intro, I’m excited to officially welcome Darrell Alfonso as the newest co-host of the podcast!

Summary: The Humans of Martech enters an exciting new chapter with Darrell Alfonso joining as co-host, bringing fresh energy and insights to the show. As a long-time listener and new dad, Darrell offers relatable stories of juggling work, family, and community while sharing bold predictions like the shift to warehouse-native architectures in martech, which promise to streamline data operations for enterprises. With AI poised to handle executional tasks, Darrell emphasizes the evolving role of marketers as strategic thinkers guiding AI with emotional intelligence and ethical oversight. As the podcast heads into 2025, it remains committed to delivering actionable insights, thought-provoking predictions, and a fresh perspective for the martech community.

Welcoming a New Co-Host and Celebrating Baby Milestones

Darrell’s journey to becoming a co-host on the podcast came full circle, blending mentorship, passion, and personal milestones. He shared how one of his mentees suggested the idea, sparking an opportunity he immediately embraced. As an early listener of the show, Darrell highlighted his admiration for its unfiltered and geeky deep dives, calling it his favorite podcast—a sentiment that fueled his excitement for the road ahead.

On a personal note, Darrell and his wife recently welcomed their baby boy, just eight weeks ago. Parenthood, he admitted, has been a whirlwind of sleepless nights and steep learning curves. As ambitious and organized as he and his wife are, they’ve quickly discovered that babies don’t operate on predictable timelines. Moments of progress—like better sleep—often take a step back as developmental leaps shake up routines. While the lack of rest is taxing, Darrell’s outlook reflects a blend of exhaustion and gratitude.

Balancing professional life with a newborn is no small feat. Darrell recounted a whirlwind day of delivering a keynote, driving home, and immediately diving into baby duties. He joked about the unpredictability of these moments while acknowledging the personal growth they inspire. Virtual support groups like Maven have also helped him navigate the early stages of parenthood, offering both guidance and camaraderie with other new parents.

For all the challenges that come with parenthood, I always like to emphasize gratitude. Reflecting on the struggles my family faced in our journey to parenthood (and how many other couples have it much harder), we need to emphasize the importance of cherishing even the tough parts. The joy and fulfillment of finally welcoming our child outweigh the sleepless nights and ever-changing routines.

Key takeaway: Parenthood is a mix of exhaustion, growth, and gratitude. Embracing the ups and downs, leaning on community support, and focusing on the meaningful moments can help navigate this transformative stage of life.

Marketing Tools Without Databases

Okay… enough baby talk haha.

Darrell predicts that in 5 years, most marketing tools will no longer rely on databases. At first glance, this concept might seem shocking—after all, marketing automation platforms, CRMs, and CDPs are fundamentally built on relational databases. But Darrell suggests this assumption is rooted in tradition, not necessity, and outlines a shift toward a warehouse-native or zero-copy data architecture that could redefine how tools operate.

To illustrate this point, he draws a simple analogy. Consider apps like Yelp or Google Places. When you share a restaurant with a friend, the app doesn’t create a duplicate of your contacts database; it accesses the data on-demand. Contrast this with the typical marketing stack, where almost every tool replicates contact data, creating endless updates, sync errors, and manual fixes. Darrell estimates that more than 80% of a team’s data work revolves around ensuring consistency across these copied datasets—a cumbersome and inefficient process.

The inefficiency extends beyond wasted effort. Darrell shares examples of bi-directional sync loops that occur when two systems endlessly update each other, introducing a frustrating complexity to even the simplest workflows. These scenarios highlight how deeply ingrained data copying is within current systems and how much time is spent combating its limitations.

Shifting to a zero-copy model, Darrell argues, could eliminate these inefficiencies. A warehouse-native approach would enable tools to work directly from a centralized data warehouse, bypassing the need for constant synchronization. This not only streamlines operations but also reduces the risk of errors. It’s a radical departure from the status quo but one he believes is inevitable as teams demand greater agility and accuracy in their tools.

Key takeaway: The future of marketing tools lies in a warehouse-native approach, eliminating the inefficiencies of duplicated data. By moving beyond traditional databases, teams can reduce errors, streamline processes, and focus their energy on strategic initiatives rather than endless data synchronization.

Preparing for a Warehouse Native Future

I think the shift toward a warehouse-native approach for marketing tools feels inevitable, but its timeline remains uncertain. While this approach won’t entirely replace APIs, it will change how tools interact. Instead of passing data back and forth through integrations, tools will increasingly work directly from a centralized data warehouse, eliminating inefficiencies tied to duplication and synchronization.

This prediction, often misunderstood as futuristic, is already shaping current tools. Vendors like MessageGears and Castle.io are leading the charge, offering solutions that bypass traditional database structures and avoid charging based on record counts. Despite their innovations, the challenge lies in industry adoption. Many teams are accustomed to older models, making this transition as much about change management as it is about technology.

A critical insight from my past research and conversations with experts on the podcast highlights the importance of internal readiness. Tools can only perform as well as the data they rely on. High-quality, structured data is the foundation for warehouse-native success. Teams must focus on improving internal processes now, rather than waiting for the perfect tool to arrive. This means investing in data hygiene, organization, and strategy to prepare for the opportunities that a warehouse-native architecture will bring.

However, the path forward isn’t without challenges. Many companies are still immature in their data strategies, making widespread adoption a longer process than anticipated. Whether this shift takes five years or more, the direction is clear: vendors and teams must align their operations with the possibilities of a warehouse-first world.

Key takeaway: Warehouse-native tools represent a significant step forward in reducing inefficiencies and modernizing operations. Teams can prepare for this shift by prioritizing high-quality, well-structured data. The strength of these tools lies in how they interact with clean, organized data, making internal readiness the best first step for embracing this future.

Understanding When Warehouse Native Tools Matter

Darrell explains that the value of warehouse-native tools becomes clear especially when dealing with large volumes of data. For small and mid-sized companies, the classic setup—a CRM, marketing automation platform, and a few connected tools—works perfectly fine. He notes that for organizations with 500 employees or fewer, traditional data architectures remain suff...

What’s up everyone, today we have the pleasure of sitting down with Kacie Jenkins, SVP Marketing at Sendoso.

Summary: Marketing isn’t about cramming creativity into a spreadsheet, and Kacie’s journey proves it. She took on last-touch attribution, broke free from narrow metrics, and built a system that told the whole story, one where sales and marketing actually worked together. It wasn’t flashy; it was months of unsexy foundational work that led to record-breaking results. Kacie’s advice is to stop obsessing over proving your worth with perfect data. Focus on collaboration, long-term strategies, and building something so good it proves itself.

About Kacie

• Kacie started her career as a recording artist for 6 years where she recorded and released 2 top 30’s singles on country radio
• She transitioned to FANDOM as Marketing Manager where she helped build and scale entertainment and gaming communities
• She then shifted to consumer tech and worked at Roku where she helped take their streaming stick to market
• She later joined Fastly when they were still a tiny startup and was eventually promoted to VP of Marketing while helping them scale to $200M in ARR and a massive IPO
• She moved on to a few other VP of marketing stints at Ace Hotel and then Sourcegraph
• Today Kacie is Senior Vice President of Marketing at Sendoso, the top gifting and direct mail platform for revenue teams

Why Marketing Needs to Break Free from Last Touch Attribution

Kacie has strong opinions about last touch attribution and its role in marketing, calling it both misguided and overused. She recounts a memorable example where a company’s finance team mandated that every marketing touchpoint be unique, forbidding multiple efforts for a single account. The result was a fragmented strategy, with marketing forced to isolate efforts rather than integrate them—a scenario she describes as fundamentally broken. This, she says, reflects a wider misunderstanding of marketing’s role in driving success.

In her experience, marketing is often held to an unrealistic standard that no other department faces. “No one questions whether a sales team should exist,” Kacie points out, yet marketers are repeatedly asked to prove their value in isolation. This obsession with single-point attribution—whether first or last touch—reduces complex buyer journeys to simplistic, unrealistic models. She likens it to sports, where success is measured by the contributions of the entire team, not just the final goal or play. In marketing, the same principle applies: campaigns succeed when brand, product, sales, and customer experience work cohesively.

Kacie highlights how marketers often agree to flawed measurement practices under intense job pressure. Many leaders, she notes, demand immediate, trackable results and dismiss longer-term investments like brand building. When these short-sighted strategies fail, the blame lands on the marketing team, perpetuating a destructive cycle. This became especially apparent during the pandemic, when companies slashed budgets for brand and integrated marketing, only to see their performance suffer months later.

At its core, the problem stems from a demand to quantify marketing in ways that are convenient rather than meaningful. Kacie insists that attribution models like last touch can provide insights but have been misused to force marketing into a demand capture role that undervalues its broader impact. Effective marketing, she argues, cannot succeed in a vacuum—it depends on the health and alignment of the entire organization.

Key takeaway: Attribution models like last touch offer insights but become problematic when used in isolation. Marketing thrives on collaboration across teams, long-term investments, and integrated strategies. Simplistic measurement frameworks undermine this by reducing success to isolated metrics, which fail to capture the bigger picture. Focus on fostering collaboration and investing in holistic strategies rather than chasing immediate, trackable wins.

What’s the Best Way to Prove What Drives Revenue in Marketing?

Kacie’s candid take on the challenges of attribution didn’t stop there. She explains that board members and leadership often seek simple answers, asking, “What drove the most revenue?” This, she notes, is rarely a question with a singular answer, and it certainly doesn’t lie solely in the last touchpoint.

Her approach combines every available data point, UTMs, self-reported attribution, and multi-touch models, to create a comprehensive picture. This isn’t about assigning credit to one channel or tactic but understanding the collective influence of all touchpoints. For instance, at Sendoso, Kacie leveraged this holistic perspective to reinvigorate outbound sales. By investing in trust-building, strong branding, and thoughtful partnerships, the team shifted outbound calls from cold to warm, creating an environment where sales and marketing aligned seamlessly. The results were tangible, but proving causality required a deeper story, not just a simple report.

She recalls challenging her finance team’s reliance on last-touch data. When presenting a report that suggested “more direct traffic” as the solution, she asked bluntly, “What does that even mean?” This moment underscored how reductive metrics fail to capture the true impact of marketing efforts. By shifting the focus to sales-qualified opportunities and long-term patterns, she built trust with stakeholders and steered conversations toward what truly drives growth.

Kacie emphasizes that this broader view isn’t fast or easy, and it requires fighting against short-term thinking. Marketers must advocate for strategies that don’t immediately show up in last-touch reports but are essential for sustainable growth. She also draws from B2C insights, where buying decisions often happen long before measurable touchpoints, reminding us that customers’ journeys rarely follow a predictable path.

Key takeaway: Attribution isn’t about isolating success to one channel or tactic. By combining multiple data sources and focusing on long-term causality, marketers can tell a more accurate story. This approach builds trust with leadership, aligns teams, and justifies investments that might not show immediate ROI but are crucial for sustainable success.

How to Convince Leadership to Rethink Measurement

Kacie explains that driving change in marketing attribution and measurement requires aligning cross-functional teams and proving value over time. When she joined Sendoso, the disconnect between sales and marketing created distrust, and outdated metrics like MQLs dominated conversations. To address this, she set clear expectations with leadership: changes would be foundational, require significant investment, and take time to show results. This up-front agreement ensured her efforts had initial backing, though challenges arose as the process unfolded.

A crucial part of the transformation was bridging the gap between marketing and finance. Kacie worked with a finance partner who embraced curiosity, seeking to understand marketing’s perspective by educating himself through webinars and discussions. This mutual respect and collaboration were essential for aligning goals and building trust. She demonstrated that her approach wasn’t about gaming numbers or securing credit but about laying a foundation for sustainable growth.

Despite initial alignment, skepticism crept in as foundational work—cleaning up Salesforce fields, rethinking sales stages, and redefining metrics—took longer to yield visible results. Kacie emphasizes the importance of perseverance during this phase. Companies often lack the patience for foundational changes, cutting le...

What’s up everyone, today we have the pleasure of sitting down with Stephen Stouffer, Director of Automation Solutions at Tray.ai and the first ever returning guest. We had Stephen on earlier in the year in episode 112 where we unpacked the practical wonders of combining AI tools with iPaaS solutions. 

Summary: AI can transform your marketing without overwhelming you. Start with one use case. Watch the results, and go from there. You don’t need to master data science to add AI value, but you need to be willing to experiment, keep what works, and let the tech do the heavy lifting. 

Customer Journey Mapping Essentials

Customer journey mapping, as Stephen puts it, is best approached as a clear, structured framework. For marketers, this often starts by examining the visitor's first few seconds on a website. Stephen’s “three, five, seven rule” is a useful guide: three seconds to capture attention, five to build engagement, and seven to prompt action. Reviewing homepage or landing page performance through this lens keeps the focus on essentials. Are calls-to-action (CTAs) clear and accessible? Does the page guide users toward the intended outcome effectively?

Stephen further notes the importance of every element “above the fold.” Content here needs to be concise, visually appealing, and should naturally lead users to the next step. A well-placed CTA, such as a prominent button, encourages forward motion, while a hidden or confusing one can derail the journey. Each interaction should be straightforward and intuitive.

Beyond landing pages, Stephen highlights the journey before a visitor even arrives. Campaign managers, for instance, should ensure that ad copy and visuals align with the landing page, creating a smooth transition from ad to action. Consistency here reduces friction and keeps the experience cohesive.

For advanced mapping, Stephen recommends storyboarding different customer personas and their digital pathways. By tuning each stage to fit these profiles, marketers can craft a journey that feels relevant and trustworthy, engaging each segment from the very first interaction.

Key takeaway: Use the "three, five, seven rule" to evaluate each customer touchpoint on your homepage or landing pages. This approach helps ensure your content captures attention, fosters engagement, and prompts action—all within a few seconds.

AI’s Role in Automating Personalized Emails

Stephen recently demonstrated how AI automates personalized emails with just a first name, last name, and email. AI uses data from sources like LinkedIn, company details, and job history to craft messages that feel genuinely tailored to each recipient, far beyond typical generic responses.

This level of automation doesn't just boost engagement; it saves significant time. Instead of setting up complex variable fields or spending 15-20 minutes per email on manual research, AI handles it all in seconds. Stephen notes that marketing teams can skip intricate field configurations, while sales teams gain back valuable time to focus on high-impact tasks.

AI also serves as a replacement for traditional enrichment tools, pulling in dynamic contact details without third-party data providers. For sales, it means delivering relevant, personalized content effortlessly. AI does the heavy lifting, creating an email that feels custom-built for the recipient—no manual assembly required.

Key takeaway: AI enables efficient, data-rich personalization for customer outreach, saving marketing and sales teams time and resources while boosting the quality of each touchpoint.

Automating Personalized Outreach with AI Agents

AI agents are redefining how teams approach personalized outreach, offering new ways to automate highly customized interactions. Stephen explains how Tray.ai leverages a powerful combination of APIs—OpenAI, Google, LinkedIn, and more—to build out complex automation processes directly within its platform. Each AI agent is designed to use the best tool for the task at hand. Given the right context and instructions, these agents can gather relevant data from press releases, Crunchbase, LinkedIn profiles, blog posts, and other sources to craft an email that feels genuinely tailored.

Imagine a marketing email generated entirely by an AI agent. With the recipient’s email, role, and other contextual clues, the AI might produce a message like, “Hey, congratulations on your recent speaking slot at AntiCon in London. Hope you had a safe journey back!” This level of personalization would usually require about 15 minutes of research by a BDR or ISR. Now, it can be fully automated, freeing up sales and marketing teams to focus on strategy and high-priority tasks rather than time-consuming data gathering and crafting.

Stephen points out that the true power of AI agents comes from implementing them in real, tangible ways. For instance, rather than abstract promises of efficiency, Tray.ai demonstrates AI’s impact with practical use cases like this automated email personalization, which resonates more directly with the people using it. By creating a functional demo that allows teams to see this technology in action, Tray.ai bridges the gap between AI's potential and its practical application.

For anyone curious to test it out, Stephen offers a live demo of the personalized email automation. This hands-on approach helps users understand the realistic possibilities AI agents bring to customer engagement and outreach, transforming the process from concept to actionable, impactful workflows.

Key takeaway: AI agents streamline personalized outreach, combining data sources and automation tools to generate highly customized emails without manual research. By automating these tasks, teams can focus on high-impact activities while still delivering meaningful, individualized interactions.

Challenges in Implementing AI-Driven Customer Journey Mapping

Implementing AI-driven customer journey mapping and personalization comes with its share of challenges. Stephen highlights three primary obstacles teams face: complexity, connectivity and compliance.

1. The technology’s complexity. Even technical professionals sometimes struggle to understand the nuts and bolts of AI integrations, making it difficult for organizations to determine how to effectively deploy these tools. The ambiguity around building and customizing AI solutions internally often becomes a barrier to adoption.
2. The challenge of data connectivity. For AI agents to deliver relevant outputs, they need access to comprehensive data across systems. Whether it’s a CRM, sales records, or product usage data, these inputs provide the context for AI to make useful recommendations. While crafting a prompt might sound straightforward, gathering and linking all the necessary data to inform that prompt is anything but simple. Stephen explains that AI can only be as effective as the information it’s fed, making seamless data integration a top priority for effective personalization.
3. Perhaps most daunting, the challenge is compliance. When feeding sensitive data into large language models, teams must navigate a maze of security requirements like SOC 2, HIPAA, and GDPR compliance. Many organizations hesitate to dive into AI because of the fear of regulatory risks. Legal teams often step in, concerned abo...

What’s up everyone, today we have the pleasure of sitting down with Nataly Kelly, CMO at Zappi.

Summary: Global expansion is a wild process that connects brands to the unique vibe of each market, it’s not just creating a website or translating content. Every market brings its own needs, from how audiences navigate sites to what resonates visually and emotionally. Moving into international territories means showing up prepared, with a localization strategy that’s flexible and has a ton of local insight. Marketing Ops and RevOps both play a key role in localization as a strategic partner, organizing data and decision-making to fuel growth across departments.

About Nataly

• Nataly started her career as an interpreter at AT&T and later co-founded a research and consulting company which was acqui-hired by her biggest customer where she would serve as Director of Product Development
• She later held Chief Research Officer and VP of Market Development titles at a market research firm and a translation and localization company
• Nataly then made the mega move to HubSpot as VP of Marketing where she would spend nearly 8 years – involved in all aspects of full-funnel marketing globally, including International Ops and Localization
• She then moved to Rebrandly as Chief Growth Officer leading sales, marketing and product 
• Nataly’s also an author, she’s published 3 books and 1 coming out next year, she has a Newsletter called ‘Making Global Work’
• Today, Nataly’s moved into her 4th SaaS marketing leadership role as CMO at Zappi–the leading consumer insights platform 

Why LinkedIn Works for Building a Newsletter

Nataly decided on LinkedIn for her newsletter with one primary goal: reaching more people, fast. In marketing, there's always talk of “owning your audience,” but for Nataly, the built-in reach LinkedIn offers outweighed the usual risks. Sure, LinkedIn could shift its algorithm or start favoring video, but Nataly isn’t fazed. She believes adaptability is more valuable than control. “If LinkedIn ever moves entirely to video, I might reconsider,” she says. “But for now, it’s a writer’s platform, and I’m a writer.”

What really sold her, though, is LinkedIn’s “triple play” effect. Each time she publishes a newsletter, her audience doesn’t just see it once—they get three reminders. The content appears in their feed, triggers a platform notification, and even lands in their email inbox. This multi-touchpoint delivery isn’t just convenient; it significantly boosts her visibility. In a crowded digital space, those three nudges are powerful. And the best part? It doesn’t take any extra work on her end. For Nataly, this setup is gold: “If I can reach my audience in three different ways without doing three times the work, I’m in.”

On top of that, LinkedIn’s algorithm has started indexing her posts for keywords, so they pop up in search results long after she hits “publish.” Nataly likes this longevity. She’s seen her posts gather momentum over time, which reassures her that LinkedIn isn’t likely to abandon text-based content anytime soon. This layered exposure works in her favor, especially since she’s already built a solid following on LinkedIn. Her audience is naturally expanding, without any additional ad spend or email list management.

This approach ties back to a guiding principle Nataly picked up at HubSpot: follow the growth. When a channel shows traction, commit fully and ride the momentum. LinkedIn’s growth trajectory fits perfectly with her goals, allowing her to spend her time effectively—engaging with followers, creating relevant content, and letting the platform do the heavy lifting. “I see LinkedIn growing, and I’m here for the ride,” she says.

While email newsletters and other platforms might come into play in the future, right now, LinkedIn is her sweet spot. It’s a low-maintenance option that lets her connect with her community directly, on the platform where she’s already active. She’s writing for the sake of sharing knowledge, and LinkedIn offers a direct, hassle-free way to reach a broad audience without splitting her focus across multiple channels.

Key takeaway: For marketers aiming to maximize reach, LinkedIn’s multi-touchpoint setup and organic audience growth make it an ideal platform. When traction is the goal, LinkedIn’s notification, email, and feed distribution offer valuable, low-effort exposure—perfect for those who want to focus on content, not channel management.

Understanding the Nuances of Going Global

Nataly makes a clear distinction between "going global" and "going local," a distinction that goes beyond simply putting content online for everyone to see. Launching a website, or even setting up a LinkedIn profile, can technically connect a person to a global audience. But creating an intentional, local connection demands a specific approach, one that carefully considers language, cultural context, and user experience. For Nataly, globalization isn’t just about reaching people across borders—it’s about meeting those audiences where they are, with language and content that resonate.

Her insights stem from years of experience, including her work at HubSpot, where she developed a practical framework to explain these concepts to teams across the company. She found that simplifying these ideas into one-word definitions helped cut through the confusion. For example, “internationalization” is about adapting the technical side, like making code accessible to different languages and regions. This step ensures the foundational structure can support localized content, but it’s just the beginning.

Translation, Nataly explains, isn’t about directly swapping words. True translation involves adapting the message itself. For one audience, a particular phrase might evoke excitement; for another, it might fall flat or even offend. Nataly emphasizes that effective translation reaches beyond literal words to convey a message that feels native to each audience, maintaining intent, tone, and cultural relevance.

Localization goes further, adapting the entire user experience for specific markets. It's not just about making text comprehensible but ensuring every interaction—from navigation to design—feels intuitive for users in diverse regions. For instance, a website optimized for American users may assume all visitors speak English, but this model doesn’t apply universally. In countries like Canada, India, or across the EU, multi-language realities complicate navigation. This level of adaptation requires deep cultural and technical knowledge to avoid common pitfalls and create a seamless experience.

Globalization, however, is the ultimate adaptation, demanding a complete rethinking of the framework itself. Nataly notes that one of the biggest challenges is getting teams to shift from a single-market mindset to a truly global perspective. A platform initially designed for one language or culture may struggle when stretched to fit a multilingual or multicultural user base. Globalization requires a build-it-right-from-the-start approach, anticipating diverse user needs and ensuring the platform can expand without limitations.

Key takeaway: Successful globalization is about more than just reaching an international audience; it requires intentionally adapting every layer—from code to experience—to create content that resonates locally while remaining accessible globally.

Strategic Timing for Going Global

When HubSpot considered expanding internationally, it wasn’t about leaping into new markets; it was about waiting until the timing and resources aligned. Nataly recalls that CEO Brian Halligan was deliberate, even drafting a Harvard Business Review piece outlining Hu...

What’s up everyone, today we have the pleasure of sitting down with Jim Williams, CMO at Uptempo.

Summary: Forget version control spreadsheets and stale budgets, Jim’s take on marketing planning is about putting purpose behind every dollar. Instead of throwing darts at a board, focus on creating a blueprint that connects goals to actual business impact. For him, goals shouldn’t be handed down from the top like a royal decree but hammered out together with practitioners so they’re ambitious… but you know, grounded in reality. Marketing Ops pros are the unsung heroes, bringing sanity to the madness with data and KPIs that keep every piece aligned. Plus, AI’s set to take over the boring bits—updating data, tracking budgets, making sure no dollar gets lost—leaving marketers free to do what they do best: make real magic happen.

About Jim

• Jim started his career in PR and Product Marketing before spending 7 years at Eloqua as Sr Dir of Product Marketing and helping the company rise from 15M ARR to 92M and IPO. 
• He later moved on to Influitive – the popular advocate marketing platform – as VP of Marketing where he helped grow the company from pre revenue to 12M in ARR
• He then moved over to the DNS world as Snr VP of Marketing at BlueCat where he led all facets of marketing
• He then became CMO at BrandMaker which has since rebranded to Uptempo, the leading enterprise marketing operations software that helps marketers plan better, spend smarter and execute with confidence.

What Is a Marketing Plan

Jim dispels the idea that marketing planning should be like “throwing darts at a dartboard.” A marketing plan isn’t a guessing game; it’s a strategic framework for how teams tackle the future. One of the most common mistakes Jim sees? Dusting off last year’s plan and rebranding it for the new year. This tactic, he argues, is the quickest way to stay stuck. In a world that demands fresh thinking, relying on past strategies doesn’t cut it.

The old-school concept of a “pivot” has taken on a new life in marketing. It’s no longer about just one big strategy shift but about building in constant adaptability. Jim suggests that, unlike traditional yearly plans, today’s marketing requires continuous recalibration. The best teams aren’t just agile once—they’re agile all the time. That flexibility to assess, pivot, and refine isn’t a luxury; it’s the core of modern marketing planning.

Another common pitfall Jim highlights is the habit of dividing up the budget before solidifying a game plan. For too many teams, budget allocation is seen as the end goal rather than just a piece of the puzzle. Getting the numbers in place is just step one, not the entire strategy. A plan isn’t simply a breakdown of costs; it’s the strategic “why” and “how” behind each dollar spent. Without defining the intended outcomes, budgets lose meaning.

Jim makes an essential distinction: budgets support the mission, but plans set the course. The budget tells you what’s possible financially, but the plan clarifies what needs to be achieved. This separation between resources and goals keeps marketing teams focused, providing a framework to measure success rather than just track expenses. With a clear strategy in place, budgets go from static numbers to dynamic assets driving real outcomes.

Key takeaway: A budget is just a set of numbers; a marketing plan is the vision behind those numbers. By keeping intent at the forefront, teams can transform budget allocations into impactful actions, staying adaptable and ready for whatever’s next.

Building a Marketing Plan That Aligns Top-Down and Bottom-Up Goals

Jim dives into the complexities of planning in a large organization, pointing out that it’s not a matter of simply setting goals at an offsite retreat. At the enterprise level, planning is a detailed, phased, six to nine-month process. Yet, he notes that surprisingly few accessible resources break down this method. For many marketers, planning seems shrouded in mystery—a skill they’re expected to learn on the job, often after they’ve already taken on leadership responsibilities.

Jim explains that marketing planning often starts with annual, top-down forecasts. This approach provides broad company objectives, which interlock with a bottoms-up plan in later stages. Rather than seeing top-down and bottom-up planning as opposing methods, Jim views them as stages in a coordinated approach. At Optempo, they’ve formalized this method in a seven-step “blueprint for marketing planning” to guide teams through each phase. This blueprint begins with setting overarching company objectives—determining whether the focus is on market expansion, product launches, margin improvements, or even mergers and acquisitions. Until these objectives are set, marketing teams can’t start defining specific growth tactics.

Once top-level objectives are clear, Jim explains that the marketing team distills them into a focused “plan on a page,” a roadmap outlining how marketing will support each objective. This document serves as a communication tool, clarifying what marketing intends to achieve and aligning these goals with company-wide expectations. According to Jim, defining these specific objectives—whether they involve selling to new buyers, entering fresh markets, or optimizing existing processes—is foundational for cohesive planning.

Jim also breaks down the budget allocation process, which directly follows the plan on a page. This is where marketing teams work with finance to divide funds, categorizing costs into programmatic and non-programmatic expenses, as well as campaign-based and non-campaign-based spending. By grouping expenses into clear, high-level “buckets,” Jim explains, teams ensure their budgets align with strategic priorities and company-wide financial targets.

Key takeaway: A successful marketing plan balances top-down objectives with bottom-up execution. Begin with high-level company goals, then translate them into actionable steps and align budget allocations accordingly. This approach ensures that both strategy and resources are directed toward achieving meaningful impact.

Why Marketing Goals Need to Be a Two-Way Conversation

Jim counters the misconception that company goals are simply handed down from a closed-door board meeting, with marketers then left scrambling to hit those targets. He clarifies that in most forward-thinking companies, the setting of financial objectives isn’t a secretive, top-down affair. Instead, it’s a dialogue involving senior leadership across all departments—including marketing. When the ownership of a business, be it public shareholders or private investors, establishes financial ambitions, these aren’t randomly assigned numbers; they’re set with input from an executive team that includes the CMO or head of marketing.

Jim explains that technology companies, for example, often focus on maximizing valuation. The board or ownership group typically benchmarks these goals using standards like the “Rule of 40”—a common framework in SaaS that blends growth rate and profitability. But these objectives are usually part of a larger, multi-year vision, not just a single-year target. Once these broad metrics are set, the board works backward to define the current year’s objectives. From there, it’s up to the executive team, including marketing leadership, to devise the most effective strategies to meet these targets.

Jim emphasizes that marketing isn’t just a passive recipient of goals. Marketing leadership works closely with other executives to determine how marketing can help hit specific benchmarks. It’s at this stage that the conversation turns practical. For instance, if a company needs a particular level of market penetr...

What’s up everyone, today we have the pleasure of sitting down with Barbara Galiza, Growth and Marketing Analytics Consultant.

Summary: Attribution is a bit like navigating Amsterdam’s canals: mesmerizing but full of hidden turns that don’t always make sense. You don’t need to chart every twist—just focus on finding the direction that moves you forward. Instead of obsessing over every click, use attribution like a compass, not a GPS. Multi-touch attribution (MTA) gives you some of the story, but often misses those quiet yet powerful nudges that drive real decisions. Layering in rule-based or incrementality testing can fill the gaps, giving a clearer picture of what’s driving your wins. For startups, it’s even simpler: stick to what’s working and forget complex attribution—qualitative feedback is often the best guide in the early days. Data doesn’t need to be perfect, just practical, and sometimes trusting that a strategy is working is enough to keep pushing it.

About Barbara

• Barbara was an early employee at Her (YC), the biggest platform for LGBTQ women where she would eventually become Head of Growth
• She was also Head of Growth at different startups like Pariti and Homerun
• She worked at an agency where she led data and analytics for Microsoft EMEA
• Barbara then went out on her own as a GTM and Analytics consultant for various companies like Gitpod, WeTransfer, Sidekick and dbt Labs
• She has a newsletter on marketing data: 021 newsletter.
• She produces content for data brands (dbt, Mixpanel, Amplitude) like case studies and webinars

Building Data Literacy Through SQL

Data literacy is essential for modern marketers, but it doesn't have to be intimidating. Barbara’s advice is simple: learn SQL. While marketers today are surrounded by user-friendly tools and drag-and-drop interfaces, those who want to truly grasp their data should get comfortable with SQL. It’s not about becoming a data engineer but about understanding how the numbers you rely on every day are built. SQL helps you see how data connects, how it’s organized, and how you can group it to make sense of what’s happening in your campaigns.

What’s great is that you don’t need to dive into formal classes or certifications. Start where you are. Most companies are sitting on a goldmine of structured marketing data, whether it’s Google Analytics data in BigQuery or Amplitude events stored in a data warehouse. The next time you’re building a report, try using SQL for a small part of the process. It’s a skill that compounds over time. Once you get familiar with the basics, you’ll start to see data in a different way, and you’ll be able to spot insights faster.

Barbara also points out a crucial, often overlooked skill: understanding why your tools give credit to certain campaigns. Why does one Facebook ad outperform others in your reports? Why does Google Analytics attribute more conversions to certain sources? Getting to the bottom of these questions puts you in a much stronger position as a marketer. If you can explain how attribution models work and why certain data points appear, you're already ahead of most.

At the end of the day, it’s about making smarter decisions. Barbara believes that marketers who can confidently say, “I know why these numbers look the way they do,” are in the top 10% of data-driven marketers. It’s not just about collecting data; it’s about making sense of it and using it to steer your strategies.

Key takeaway: Learning SQL gives marketers the power to truly understand their data. Starting small, even with basic queries, can unlock a deeper understanding of how marketing data is structured and why campaigns perform the way they do. The key is to build practical skills that help you make more informed decisions.

Rethinking Attribution and Understanding Its Role in Measurement

Barbara brings clarity to two commonly conflated concepts: attribution and measurement. While many marketers default to thinking of attribution as purely click-based or multi-touch attribution (MTA), Barbara challenges this view. She argues that attribution goes beyond just tracking clicks and touches throughout a customer’s journey. It’s about understanding the overall impact of marketing efforts—whether through incrementality tests, media mix modeling (MMM), or holdout groups. Attribution is meant to explain how marketing drives results, but it’s not the only tool for assessing campaign success.

MTA, particularly click-based models, excels at measuring bottom-funnel actions like search marketing, where high-intent users click on an ad and then convert. This method works well for campaigns that rely on clicks to move the needle. However, Barbara notes that it has its limitations, especially when it comes to non-click-based channels like video or display. MTA often over-credits search campaigns because that’s where the conversion is tracked, but it misses the broader influence of awareness-building efforts. In essence, MTA can tell you what happened after the click, but not what inspired it in the first place—be it a podcast mention or an engaging piece of content seen days before.

On a broader level, Barbara explains that attribution is not the same as measurement. Attribution focuses specifically on tying marketing efforts to business results, such as leads or revenue. Measurement, on the other hand, casts a wider net. It includes performance across various metrics, not just conversions. For instance, measuring how well different messaging resonates with audiences is crucial, but it doesn’t always directly lead to immediate sales. Measurement can inform future strategies by offering insights into engagement, customer preferences, and channel effectiveness.

As Barbara sees it, attribution is a subset of measurement. It’s a tool for understanding what drives business outcomes, but it shouldn’t be the only tool marketers rely on. For example, MTA has its place but should be used alongside other models like MMM to paint a fuller picture. Measurement, meanwhile, helps marketers assess the effectiveness of everything from messaging to customer touchpoints, beyond just the end goal of conversion.

Key takeaway: Attribution is one piece of the measurement puzzle, focusing on business outcomes, while measurement encompasses a broader range of insights. Marketers should use a mix of attribution models to understand their campaigns and apply measurement tools to gain a holistic view of performance.

Limitations of Multi-Touch Attribution in Credit Distribution

Multi-touch attribution (MTA) is often seen as a way to distribute credit across different customer touchpoints, but Barbara questions its effectiveness in this role. She argues that MTA is inherently limited because it only attributes credit to interactions that involve a click. This creates a skewed view of the customer journey, where only click-driven strategies—like search ads—are recognized, leaving other key touchpoints, like connected TV (CTV) or social media, out of the equation. The result is a narrow perspective that doesn't capture the full influence of various channels.

Barbara points out that for marketers to make better decisions, MTA needs more than just click data. One alternative she suggests is pairing MTA with rule-based attribution models, where data from "How did you hear about us?" surveys are integrated into the analysis. This way, marketers can capture insights from channels that don’t typically generate clicks but still play a crucial role in driving awareness or consideration. By adding this type of first-party data, businesses get a broader understanding of what’s really influencing their customers.

Some data agencies are also experimenting with es...

What’s up everyone, today we have the pleasure of sitting down with Steven Aldrich, Co-CEO and Co-Founder at Ragnarok NYC.

Summary: Like the aftermath of Ragnarök according to Norse mythology, the martech world is emerging stronger, more focused, and ripe with potential. Rather than being overwhelmed by the chaos, marketers should use this time to rethink how to evaluate technology choices through the lens of business value. Prioritize platforms that drive real-world impact and avoid getting lured by features that blaze brightly for a moment, only to be swallowed by the tide of irrelevance.

About Steven

• Steven’s first job out of business school was a customs broker in Colombia, before his Visa ran out and he was forced to return to the US 
• He started his marketing career as a Marketing and Comms Associate at a market research firm where he discovered the wonders of HTML, email development and Adobe dreamweaver
• While continuing his full time in-house career working in email and CRM roles for different industries, Steven and his co-founder Spencer launched Ragnarok, first as a side hustle where they spent their evenings moonlighting as marketing technology consultants
• In 2017, both co-founders decided to take the leap and go all in on their agency
• Today Ragnarok is a 50+ person full service martech agency that’s helped well known brands like zapier, dropbox, asana, adobe and many more!

The Evolution of Martech and the Impact of Consolidation

When asked about the future of martech, Steven immediately highlighted the ongoing consolidation in the industry. He pointed to acquisitions like Twilio snapping up Segment and Salesforce expanding its Customer Data Platform (CDP) offerings as clear signals. According to Steven, these moves indicate that we’re in the midst of a reshuffling phase—one that will shape how martech platforms are built and used over the next decade.

However, it’s not just about merging and acquisitions. Steven sees the next wave of growth stemming from generative AI. This technology, while still in its infancy for many organizations, will soon be as fundamental as marketing automation tools were a decade ago. Platforms are experimenting with Gen AI features like automated content creation, but they’re still scratching the surface. “Right now, a marketer isn’t likely to sit down and have their AI tool write an entire creative brief,” Steven noted. “But once the tech reaches a level where it’s drafting briefs and campaign strategies, it’ll fundamentally change what marketers do day-to-day.”

He also predicts that the next few years will separate the genuine innovators from the rest. Startups focusing on AI-powered automation and advanced integrations will emerge as key players. Those that fail to embrace this trend will struggle to maintain relevance. Steven pointed to companies like Castle.io as an example—a newer entrant that has managed to make a name for itself by rethinking traditional automation and going all-in on a warehouse-first approach.

Looking ahead, Steven envisions a future where marketers become more like strategic curators rather than operators. Instead of creating every campaign element manually, marketers will outline goals and high-level structures, and let the tools figure out the rest. “Think of a platform where you set your conversion goals, outline your audience, and the tool builds the journey for you,” he explained. Some companies are testing these capabilities internally, but we’re still far from a world where it’s the norm. To reach that stage, platforms need to overcome significant technical challenges and gain marketer trust.

Ultimately, Steven believes that by the ten-year mark, the martech industry will look entirely different. The focus will shift away from basic integrations and automation to more complex AI-driven orchestration. Platforms will evolve into decision-making engines, allowing marketers to focus on strategy, creativity, and innovation, leaving the grunt work to the machines.

Key takeaway: The martech industry is undergoing a consolidation phase as it readies itself for the next wave of innovation: generative AI. Startups that embrace AI-driven automation will emerge stronger, while legacy platforms must integrate these new capabilities or risk becoming obsolete. In the next decade, marketers will transition from hands-on campaign execution to strategic oversight, as tools handle more of the complex work autonomously.

Blending Automation with Human Spark for Smarter Martech Strategies

When it comes to AI and automation in martech, there’s a spectrum of opinions. On one end, some marketers insist that only a human can truly understand and engage their audience. On the other end, there’s a growing camp eager to hand over the repetitive tasks to machines and focus on strategy. Steven pointed out that the real value lies in finding a balance between the two extremes, especially for industries with strict compliance requirements like FinTech and health tech.

Steven used abandoned cart programs as a foundational example of automation’s role in marketing. Not long ago, these campaigns were inconsistent and cumbersome. Companies like Klaviyo and Shopify stepped in, making abandoned cart emails table stakes for eCommerce. Now, if you abandon your cart, you can almost predict when you’ll receive that follow-up email offering a discount or reminder. “It’s just expected,” Steven explained. He believes this kind of automated functionality has become the baseline for what customers and marketers alike view as the norm.

But not every industry can afford to automate at that level. With sectors like finance or healthcare, there’s a need for humans to review and validate messages for compliance. “A legal person is at the end of every review,” Steven said. “It’s frustrating and time-consuming, but the cost of sending the wrong message at the wrong time is just too high.” He sees these industries gradually adopting AI where they can—incremental optimization, message testing—but keeping a human in the loop for quality assurance.

The evolution of martech, in Steven’s view, will be about advancing beyond these early stages. He predicts that the future will bring a seamless integration where humans set high-level goals, and AI takes care of execution. The role of the marketer shifts from managing individual campaigns to curating experiences and setting strategic parameters. Some platforms are already testing these capabilities, but they’re far from ready for mainstream adoption. “Imagine a future where marketers simply set their audience, goals, and content, and the tool builds the entire journey for them,” Steven envisioned. This approach would redefine what it means to be a marketing operator, giving professionals more time to think strategically rather than tactically.

Ultimately, Steven sees the evolution of martech as an interplay between speed and quality. Some companies will succeed by automating faster, launching multiple initiatives, and iterating based on outcomes. Others will opt for a more deliberate approach, spending more time crafting the perfect message. “There isn’t one clear winner,” Steven concluded. “It’s about choosing the right tool for the job and understanding what’s at stake when the human element is minimized.”

Key takeaway: Martech’s future lies in balancing automation with human oversight. While some industries can embrace full-scale automation, others need humans in the loop to maintain compliance and quality. Marketers must choose tools that fit their strategic goals—whether that’s rapid iteration or precision crafting.

The Value of Data Science in Martech Optimization

When asked about the role of data science in marketing operations, Steven was quick to point out the di...

What’s up everyone, today we have the pleasure of sitting down with Danny Lambert, Director of Marketing Operations at dbt Labs.

Summary: Marketers often feel like they're battling a dragon when it comes to integrating data. We’re overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams. Danny walks us through his journey of cautiously entering the data world and the role dbt can play for marketing teams. By learning just enough SQL, knowing what tools you need to get started with and leaning on dbt’s tools, you can start small and gradually build a warehouse-first martech stack. The reward is more control over your data, flexibility to deploy personalized campaigns independently, and a competitive edge that no pre-packaged solution can match.

About Daniel

• Danny started his career at an event solutions company where he wore several different marketing hats including getting his first taste of marketing automation  
• He then worked in marketing ops at IZEA, at marketplace that connects brands with influencers before having a short stint at McGaw.io one of the leading martech and analytics agencies
• He then moved over to healtech at CareCloud where he led Demandgen and ABM
• He then transitioned to Rev.com the popular transcription company where he started in marketing ops, then demand gen before being promoted to Director of Integrated Marketing
• And today Dan is Director of Marketing Operations at dbt Labs, the creators of the most popular software for data transformation used by data engineers at more than 20k companies

Navigating the Disconnect Between Marketers and Data Teams

Many marketers struggle to engage with data teams because they feel worlds apart. Danny points out that it’s a lot like the early days of marketing’s relationship with product teams. Before product-led growth (PLG) became a buzzword, marketers and product teams operated in separate silos. It took a concerted effort to break that wall, and the same shift is needed with data. Marketers often find the mechanics of data engineering and warehousing intimidating, and for good reason—they weren’t trained for it. But it doesn’t have to be that way.

Danny recounts his time at CareCloud, where he was exposed to the concept of a data warehouse. The idea was gaining traction, and he attended a Snowflake event to grasp the essentials. After an hour of slides and schemas, he walked out just as confused as when he walked in. The issue wasn’t the information; it was the delivery. Marketers need to see things in action. Theoretical talks don’t cut it—practical, straightforward tutorials that walk you through the steps are what marketers crave. Installing tools like dbt and seeing data move can make it all click. It’s the difference between hearing about a new tool and actually feeling it work in your hands.

There’s also a major gap in educational resources that cater to marketers. As Danny highlights, marketing professionals who want to embrace data often get lost in the flood of courses and jargon-heavy materials. It’s a jungle out there—marketers want concise, actionable guidance, not a deep dive into tech theory. Without the right content, many opt to stay in their lane, using tools and methods they already know. It feels safer, especially when they’re under pressure to perform quickly.

Danny points out that this pressure to ramp up fast can discourage experimentation with a warehouse-first approach. New roles often come with tight timelines, and there’s a tendency to lean on old habits. Shifting to something like data warehousing means slowing down, learning the ropes, and building enough belief in the new approach to back it up internally. But if you’ve spent years doing things differently, it’s hard to develop the conviction needed to push for change. Confidence comes from exposure and understanding, but without that, the warehouse-first idea feels too foreign to champion.

Key takeaway: Marketers often shy away from data teams because they lack practical, accessible education and feel pressured to stick with familiar methods. Building confidence through hands-on learning and real-world examples is crucial for integrating data and marketing in a meaningful way.

Overcoming Barriers to Data Literacy in Marketing

Many marketers hesitate to engage deeply with data, often because they don’t see it as central to their roles. Danny explains that for most, data feels like a secondary tool—something meant to assist rather than dominate their day-to-day work. The challenge is that the pathway to becoming data-savvy isn’t straightforward. Even among those who’ve made the leap, each person’s journey looks different. Some take online courses, like those on Codecademy, learning SQL from scratch. Others find mentors who guide them through the maze of data management, or they happen to work in environments where they can lean on a data specialist nearby. But there’s no universal roadmap, which makes the process feel daunting.

Danny believes that the lack of a clear, predictable path to mastering data is one of the biggest hurdles marketers face. With so many options available—some technical, others more hands-on—marketers often struggle to identify which approach will actually get them the skills they need. For those with limited time, this uncertainty can be a dealbreaker. Without knowing if the investment will pay off, it’s easier to focus on other areas of marketing that feel more familiar and essential. Danny points out that while resources like Udemy are improving the situation, marketers still need a straightforward, reliable way to become proficient in data.

Another critical factor is the perceived opportunity cost. Marketers are often juggling multiple responsibilities, from staying up-to-date with industry trends to managing campaigns. For many, the idea of dedicating time to learning data—an area they may feel they have minimal expertise in—feels like too large a barrier. Why spend time learning about data warehousing when there are immediate, pressing marketing concepts to master? This fear of committing time and energy to an unfamiliar, complex area keeps many from taking the first step.

Danny emphasizes that while the accessibility of learning tools is improving, there’s still a significant gap. Even for those who want to upskill, the fear of the unknown and the lack of a guided pathway can make it feel like an insurmountable challenge. Until marketers can see a clear, accessible way to develop these skills, many will remain hesitant to dive into data, choosing to stick to familiar ground instead.

Key takeaway: Marketers often shy away from learning data skills due to a lack of accessible, consistent learning paths and the fear of time investment without guaranteed outcomes. Creating structured, easy-to-follow resources is crucial to making data literacy a viable option for busy professionals.

Unlocking the Full Potential of Data with dbt

Danny describes the transformation dbt brings to the data landscape, making it accessible not just to engineers but also to marketing ops and other non-engineering teams. In the past, accessing and manipulating data was a highly specialized skill, often requiring a marketer to rely heavily on a single engineer. As Danny puts it, you needed to build a relationship with this “one person in a closet somewhere” to get any insight or change implemented. This old approach made data access exclusive, slow, and frustrating for teams trying to move fast.

With dbt, Danny explains, the dynamics shift dramatically. It creates different roles and permission levels for everyone interacting with data, enabling a self-service model for marketers and operat...

What’s up everyone, today we have the pleasure of sitting down with Lourenço Mello, Product Marketing Lead at Snowflake.

Summary: Lourenço drops us straight into the gravity well of martech, where Snowflake’s latest report pulls in the tools that really matter, letting the fluff float away. It’s all about data gravity, bringing the applications to the data instead of wasting energy shuttling data around. This shift is redefining what’s possible, streamlining operations, and giving marketers a new superpower to harness the forces of AI and analytics. With composability blurring boundaries and AI breaking down silos, the takeaway is crystal clear: master data quality and you’ll have the gravitational pull to outpace the competition.

About Lourenço

• Lourenço started his career at an enterprise telecom company based in Portugal where he dabbled in competitive analysis, pricing and biz dev
• He later completed his MBA at UCLA and then spent 5 years at Microsoft as a Senior PMM focused on Azure and their data business
• Today, Lourenço is Product Marketing Lead for the Solutions team at Snowflake

Understanding the Marketing Data Stack Report Methodology

Lourenço’s perspective on Snowflake’s Marketing Data Stack Report centers around a fundamental commitment to objective analysis. Rather than focusing on internal partnerships or pushing favored solutions, Snowflake’s report leverages comprehensive telemetry data to identify which tools are truly gaining traction among its 8,000+ customers. This approach enables them to deliver a more impartial view of the martech landscape.

The methodology starts by categorizing the landscape according to current trends and customer adoption. Snowflake first identifies the relevant categories that its customers are using for marketing use cases, based on a snapshot of the industry. Lourenço emphasized that the analysis isn’t limited to tools with direct business relationships or joint ventures but looks holistically at the adoption metrics across the board. This objectivity sets the report apart, as it can spotlight tools that Snowflake hasn’t actively partnered with—yet are clearly valuable to their customers.

Two primary metrics guide the analysis: breadth of adoption and depth of adoption. Breadth measures how many customers are using a particular tool or solution, offering an initial view of popularity. However, without understanding how deeply those tools are being utilized, breadth alone can be misleading. Lourenço highlighted that a platform may have thousands of users but very minimal actual engagement. Thus, the second metric—depth of adoption—assesses how sophisticated the usage is within each customer’s implementation, revealing the true stickiness and impact of the tool.

By indexing both breadth and depth of adoption, Snowflake is able to create a ranked list of tools and platforms within each category. This process ensures that the final report is rooted in genuine customer behavior and preference, rather than internal biases. As Lourenço puts it, “the cool thing about this and really what's been so fun to be a part of is really the objectivity of the analysis.” The report not only highlights tools that are already well-integrated but also uncovers opportunities to build relationships with platforms that customers have independently gravitated towards.

This level of transparency ultimately fosters stronger collaboration between Snowflake and its partners. By showing where their customers are seeing success, the report opens the door for potential go-to-market initiatives that were previously unexplored. In a martech landscape often clouded by promotional bias, this approach offers a rare glimpse into which technologies are truly making a difference.

Key takeaway: The core strength of Snowflake’s Marketing Data Stack Report lies in its objectivity. By focusing on customer adoption metrics and removing subjective biases, the report provides a clearer view of the tools that are genuinely resonating with the market. This methodology enables Snowflake to support its customers with data-driven insights, and it paves the way for more meaningful partnerships with emerging leaders in the field.

Key Shifts Defining Martech and AdTech Today

When asked about the notable shifts between 2023 and 2024, Lourenço from Snowflake made it clear—what were once considered trends are now fundamental changes that have reshaped marketing. Last year’s report pointed to themes like the convergence of AdTech and martech, data privacy, generative AI, and the pursuit of a single source of truth. This year, these aren’t just trends—they’re seismic shifts that have permanently altered how the industry operates.

Instead of being temporary developments, Lourenço emphasized that these themes are “not going away,” likening them to the foundation of the industry itself. The report identifies three key forces driving transformation: data privacy, data gravity, and generative AI. These forces influence everything from how companies measure performance to how they monetize and manage first-party data. One of the more interesting dynamics highlighted this year is the emergence of commerce media, where industries traditionally characterized by thin margins—like retail and travel—are leveraging their vast pools of first-party data to unlock new revenue streams and drive higher profitability.

Data gravity, in particular, is a crucial concept. It describes how data is increasingly becoming the central point for both martech and AdTech activities. As Lourenço points out, brands are now using the same data source for real-time bidding on the AdTech side and for personalized experiences on the martech side. This convergence is made possible by advancements in data infrastructure, such as Snowflake’s native app framework. By allowing applications to run where the data resides, brands eliminate the need to move data back and forth, reducing latency and improving privacy. An example Lourenço shared involved identity resolution, where an eight-day process to reconcile identity data is now achievable in mere hours, sometimes even minutes, thanks to this infrastructure shift.

Another powerful change mentioned was how companies are transforming their roles—from being purely ad buyers to becoming ad sellers. This shift, Lourenço explains, is a direct consequence of organizations capitalizing on the value of their first-party data, looking to move up the value chain by creating new revenue channels through data monetization. Meanwhile, customers are increasingly interested in Marketing Mix Modeling (MMM) and other approaches to understand and optimize their media investment in light of these shifts.

The takeaway is clear: these are not passing trends but fundamental changes in how the industry functions. Snowflake’s position at the intersection of martech and AdTech provides a unique vantage point to observe these developments, and Lourenço’s insights offer a glimpse into the future of data-driven marketing.

Key takeaway: The convergence of data privacy, data gravity, and generative AI are not just fleeting trends—they’re transformative forces that are redefining the marketing landscape. Brands that align their strategy with these shifts can unlock new revenue streams, capitalize on efficiency gains, and strengthen data security, ensuring they stay ahead of the curve.

The Concept of Data Gravity in Modern Data Architecture

When Lourenço introduced the idea of data gravity, it wasn’t just about centralizing data; it was about rethinking how applications interact with it. The term itself evokes a sense of drawing everything—data, applications, and processes—toward a unified center. But in a broader sense, Lourenço emphasized that it’...