Humans of Martech Podcast Por Phil Gamache arte de portada

Humans of Martech

Humans of Martech

De: Phil Gamache
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Future-proofing the humans behind the tech. Follow Phil Gamache and Darrell Alfonso on their mission to help future-proof the humans behind the tech and have successful careers in the constantly expanding universe of martech.©2025 Humans of Martech Inc. Economía Exito Profesional Marketing Marketing y Ventas
Episodios
  • 201: Scott Brinker: If he reset his career today, where would he focus?

    201: Scott Brinker: If he reset his career today, where would he focus?
    Jan 6 2026
    What’s up everyone, today we have the honor of sitting down with the legendary Scott Brinker, a rare repeat guest, the Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself.(00:00) - Intro (01:12) - In This Episode (05:09) - Scott Brinker’s Guidance For Marketers Rethinking Their Career Path (11:27) - If You Started Over in Martech, What Would You Learn First (16:47) - People Side (21:13) - Life Long Learning (26:20) - Habits to Stay Ahead (32:14) - Why Deep Specialization Protects Marketers From AI Confusion (37:27) - Why Technical Skills Decide the Future of Your Marketing Career (41:00) - Why Change Leadership Matters More Than Technical AI Skills (47:11) - How MCP Gives Marketers a Path Out of Integration Hell (52:49) - Why Heterogeneous Stacks are the Default for Modern Marketing Teams (54:51) - How To Build A Martech Messaging BS Detector (59:37) - Why Your Energy Grows Faster When You Invest in Other PeopleSummary: Scott Brinker shares exactly where he would focus if he reset his career today, and his answer cuts through the noise. He’d build one deep specialty to judge AI’s confident mistakes, grow cross-functional range to bridge marketing and engineering, and lean into technical skills like SQL and APIs to turn ideas into working systems. He’d treat curiosity as a steady rhythm instead of a rigid routine, learn how influence actually moves inside companies, and guide teams through change with simple, human clarity. His take on composability, MCP, and vendor noise rounds out a clear roadmap for any marketer trying to stay sharp in a chaotic industry.About ScottScott has spent his career merging the world of marketing and technology and somehow making it look effortless. He co-founded ion interactive back when “interactive content” felt like a daring experiment, then opened the Chief Marketing Technologist blog in 2008 to spark a conversation the industry didn’t know it needed. He sketched the very first Martech Landscape when the ecosystem fit on a single page with about 150 vendors, and later brought the MarTech conference to life in 2014, where he still shapes the program. Most recently, he guided HubSpot’s platform ecosystem, helping the company stay connected to a martech universe that’s grown to more than 15,000 tools. Today, Scott continues to helm chiefmartec.com, the well the entire industry keeps returning to for clarity, curiosity, and direction.Scott Brinker’s Guidance For Marketers Rethinking Their Career PathMid career marketers keep asking themselves whether they should stick with the field or throw everything out and start fresh. Scott relates to that feeling, and he talks about it with a kind of grounded humor. He describes his own wandering thoughts about running a vineyard, feeling the soil under his shoes and imagining the quiet. Then he remembers the old saying about wineries, which is that the only guaranteed outcome is a smaller bank account. His story captures the emotional drift that comes with burnout. People are not always craving a new field. They are often craving a new relationship with their work.Scott moves quickly to the part that matters. He directs his attention to AI because it is reshaping the field faster than many teams can absorb. He explains that someone could spend every hour of the week experimenting and still only catch a fraction of what is happening. He sees that chaos as a signal. Overload creates opportunity, and the people who step toward it gain an advantage. He urges mid career operators to lean into the friction and build new muscle. He even calls out how many people will resist change and cling to familiar workflows. He views that resistance as a gift for the ones willing to explore.“People who lean into the change really have the opportunity to differentiate themselves and discover things.”Scott brings back a story from a napkin sketch. He drew two curves, one for the explosive pace of technological advancement and one for the slower rhythm of organizational change. The curves explain the tension everyone feels. Teams operate on slower timelines. Tools operate on faster ones. The gap between those curves is wide, and professionals who learn to navigate that space turn themselves into catalysts inside their companies. He sees mid career marketers as prime candidates for this role because they have enough lived experience to understand where teams stall and enough hunger to explore new territory.Scott encourages people to channel their curiosity into specific work. He suggests treating AI exploration like a practice and not like a trend. A steady rhythm of experiments helps someone grow their internal influence. Better experiments produce useful artifacts. These artifacts often become internal proof points that accelerate change. He believes the next wave of opportunity belongs to people who document what they try, translate what they learn, and help their companies adapt at a ...
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    1 h y 4 m
  • 200: Matthew Castino: How Canva measures marketing

    200: Matthew Castino: How Canva measures marketing
    Dec 16 2025
    What’s up everyone, today we have the pleasure of sitting down with Matthew Castino, Marketing Measurement Science Lead @ Canva.(00:00) - Intro (01:10) - In This Episode (03:50) - Canva’s Prioritization System for Marketing Experiments (11:26) - What Happened When Canva Turned Off Branded Search (18:48) - Structuring Global Measurement Teams for Local Decision Making (24:32) - How Canva Integrates Marketing Measurement Into Company Forecasting (31:58) - Using MMM Scenario Tools To Align Finance And Marketing (37:05) - Why Multi Touch Attribution Still Matters at Canva (42:42) - How Canva Builds Feedback Loops Between MMM and Experiments (46:44) - Canva’s AI Workflow Automation for Geo Experiments (51:31) - Why Strong Coworker Relationships Improve Career SatisfactionSummary: Canva operates at a scale where every marketing decision carries huge weight, and Matt leads the measurement function that keeps those decisions grounded in science. He leans on experiments to challenge assumptions that models inflate. As the company grew, he reshaped measurement so centralized models stayed steady while embedded data scientists guided decisions locally, and he built one forecasting engine that finance and marketing can trust together. He keeps multi touch attribution in play because user behavior exposes patterns MMM misses, and he treats disagreements between methods as signals worth examining. AI removes the bottlenecks around geo tests, data questions, and creative tagging, giving his team space to focus on evidence instead of logistics. About MatthewMatthew Castino blends psychology, statistics, and marketing intuition in a way that feels almost unfair. With a PhD in Psychology and a career spent building measurement systems that actually work, he’s now the Marketing Measurement Science Lead at Canva, where he turns sprawling datasets and ambitious growth questions into evidence that teams can trust.His path winds through academia, health research, and the high-tempo world of sports trading. At UNSW, Matt taught psychology and statistics while contributing to research at CHETRE. At Tabcorp, he moved through roles in customer profiling, risk systems, and US/domestic sports trading; spaces where every model, every assumption, and every decision meets real consequences fast. Those years sharpened his sense for what signal looks like in a messy environment.Matt lives in Australia and remains endlessly curious about how people think, how markets behave, and why measurement keeps getting harder, and more fun.Canva’s Prioritization System for Marketing ExperimentsCanva’s marketing experiments run in conditions that rarely resemble the clean, product controlled environment that most tech companies love to romanticize. Matthew works in markets filled with messy signals, country level quirks, channel specific behaviors, and creative that behaves differently depending on the audience. Canva built a world class experimentation platform for product, but none of that machinery helps when teams need to run geo tests or channel experiments across markets that function on completely different rhythms. Marketing had to build its own tooling, and Matthew treats that reality with a mix of respect and practicality.His team relies on a prioritization system grounded in two concrete variables.SpendUncertaintyLarge budgets demand measurement rigor because wasted dollars compound across millions of impressions. Matthew cares about placing the most reliable experiments behind the markets and channels with the biggest financial commitments. He pairs that with a very sober evaluation of uncertainty. His team pulls signals from MMM models, platform lift tests, creative engagement, and confidence intervals. They pay special attention to MMM intervals that expand beyond comfortable ranges, especially when historical spend has not varied enough for the model to learn. He reads weak creative engagement as a warning sign because poor engagement usually drags efficiency down even before the attribution questions show up.“We try to figure out where the most money is spent in the most uncertain way.”The next challenge sits in the structure of the team. Matthew ran experimentation globally from a centralized group for years, and that model made sense when the company footprint was narrower. Canva now operates in regions where creative norms differ sharply, and local teams want more authority to respond to market dynamics in real time. Matthew sees that centralization slows everything once the company reaches global scale. He pushes for embedded data scientists who sit inside each region, work directly with marketers, and build market specific experimentation roadmaps that reflect local context. That way experimentation becomes a partner to strategy instead of a bottleneck.Matthew avoids building a tower of approvals because heavy process often suffocates marketing momentum. He prefers a model where teams follow shared principles, ...
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    56 m
  • 199: Anna Aubuchon: Moving BI workloads into LLMs and using AI to build what you used to buy

    199: Anna Aubuchon: Moving BI workloads into LLMs and using AI to build what you used to buy
    Dec 9 2025
    What’s up everyone, today we have the pleasure of sitting down with Anna Aubuchon, VP of Operations at Civic Technologies.(00:00) - Intro (01:15) - In This Episode (04:15) - How AI Flipped the Build Versus Buy Decision (07:13) - Redrawing What “Complex” Means (12:20) - Why In House AI Provides Better Economics And Control (15:33) - How to Treat AI as an Insourcing Engine (21:02) - Moving BI Workloads Out of Dashboards and Into LLMs (31:37) - Guardrails That Keep AI Querying Accurate (38:18) - Using Role Based AI Guardrails Across MCP Servers (44:43) - Ops People are Creators of Systems Rather Than Maintainers of Them (48:12) - Why Natural Language AI Lowers the Barrier for First-Time Builders (52:31) - Technical Literacy Requirements for Next Generation Operators (56:46) - Why Creative Practice Strengthens Operational LeadershipSummary: AI has reshaped how operators work, and Anna lays out that shift with the clarity of someone who has rebuilt real systems under pressure. She breaks down how old build versus buy habits hold teams back, how yearly AI contracts quietly drain momentum, and how modern integrations let operators assemble powerful workflows without engineering bottlenecks. She contrasts scattered one-off AI tools with the speed that comes from shared patterns that spread across teams. Her biggest story lands hard. Civic replaced slow dashboards and long queues with orchestration that pulls every system into one conversational layer, letting people get answers in minutes instead of mornings. That speed created nerves around sensitive identity data, but tight guardrails kept the team safe without slowing anything down. Anna ends by pushing operators to think like system designers, not tool babysitters, and to build with the same clarity her daughter uses when she describes exactly what she wants and watches the system take shape.About AnnaAnna Aubuchon is an operations executive with 15+ years building and scaling teams across fintech, blockchain, and AI. As VP of Operations at Civic Technologies, she oversees support, sales, business operations, product operations, and analytics, anchoring the company’s growth and performance systems.She has led blockchain operations since 2014 and built cross-functional programs that moved companies from early-stage complexity into stable, scalable execution. Her earlier roles at Gyft and Thomson Reuters focused on commercial operations, enterprise migrations, and global team leadership, supporting revenue retention and major process modernization efforts.How AI Flipped the Build Versus Buy DecisionAI tooling has shifted so quickly that many teams are still making decisions with a playbook written for a different era. Anna explains that the build versus buy framework people lean on carries assumptions that no longer match the tool landscape. She sees operators buying AI products out of habit, even when internal builds have become faster, cheaper, and easier to maintain. She connects that hesitation to outdated mental models rather than actual technical blockers.AI platforms keep rolling out features that shrink the amount of engineering needed to assemble sophisticated workflows. Anna names the layers that changed this dynamic. System integrations through MCP act as glue for data movement. Tools like n8n and Lindy give ops teams workflow automation without needing to file tickets. Then ChatGPT Agents and Cloud Skills launched with prebuilt capabilities that behave like Lego pieces for internal systems. Direct LLM access removed the fear around infrastructure that used to intimidate nontechnical teams. She describes the overall effect as a compression of technical overhead that once justified buying expensive tools.She uses Civic’s analytics stack to illustrate how she thinks about the decision. Analytics drives the company’s ability to answer questions quickly, and modern integrations kept the build path light. Her team built the system because it reinforced a core competency. She compares that with an AI support bot that would need to handle very different audiences with changing expectations across multiple channels. She describes that work as high domain complexity that demands constant tuning, and the build cost would outweigh the value. Her team bought that piece. She grounds everything in two filters that guide her decisions: core competency and domain complexity.Anna also calls out a cultural pattern that slows AI adoption. Teams buy AI tools individually and create isolated pockets of automation. She wants teams to treat AI workflows as shared assets. She sees momentum building when one group experiments with a workflow and others borrow, extend, or remix it. She believes this turns AI adoption into a group habit rather than scattered personal experiments. She highlights the value of shared patterns because they create a repeatable way for teams to test ideas without rebuilding from scratch.She closes by urging operators to update their ...
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    1 h
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