Humans of Martech

Phil Gamache
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  • HETENTE FRISSÜL

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.

  1. 191: Aboli Gangreddiwar: Self healing data agents, hivemind memory curators and living documentation

    3 NAPJA

    191: Aboli Gangreddiwar: Self healing data agents, hivemind memory curators and living documentation

    What’s up everyone, today we have the pleasure of sitting down with Aboli Gangreddiwar, Senior Director of Lifecycle and Product Marketing at Credible.  (00:00) - Intro (01:10) - In This Episode (04:54) - Agentic Infrastructure Components in Marketing Operations (09:52) - Self Healing Data Quality Agents (16:36) - Data Activation Agents (26:56) - Campaign QA Agents (32:53) - Compliance Agents (39:59) - Hivemind Memory Curator (51:22) - AI Browsers Could Power Living Documentation (58:03) - How to Stay Balanced as a Marketing Leader Summary: Aboli and Phil explore AI agent use cases and the operational efficiency potential of AI for marketing Ops teams. Data quality agents promise self-healing pipelines, though their value depends on strong metadata. QA agents catch broken links, design flaws, and compliance issues before launch, shrinking review cycles from days to minutes. An AI hivemind memory curator that records every experiment and outcome, giving teams durable knowledge instead of relying on long-tenured employees. Documentation agents close the loop, with AI browsers hinting at a future where SOPs and playbooks stay accurate by default. About AboliAboli Gangreddiwar is the Senior Director of Lifecycle and Product Marketing at Credible, where she leads growth, retention, and product adoption for the personal finance marketplace. She has previously led lifecycle and product marketing at Sundae, helping scale the business from Series A to Series C, and held senior roles at Prosper Marketplace and Wells Fargo. Aboli has built and managed high-performing teams across acquisition, lifecycle, and product marketing, with a track record of driving customer growth through a data-driven, customer-first approach. Agentic Infrastructure Components in Marketing Operations Agentic infrastructure depends on layers that work together instead of one-off experiments. Aboli starts with the data layer because every agent needs the same source of truth. If your data is fragmented, agents will fail before they even start. Choosing whether Snowflake, Databricks, or another warehouse becomes less about vendor preference and more about creating a system where every agent reads from the same place. That way you can avoid rework and inconsistencies before anything gets deployed. Orchestration follows as the layer that turns isolated tools into workflows. Most teams play with a single agent at a time, like one that generates subject lines or one that codes email templates. Those agents may produce something useful, but orchestration connects them into a process that runs without human babysitting. In lifecycle marketing, that could mean a copy agent handing text to a Figma agent for design, which then passes to a coding agent for HTML. The difference is night and day: disconnected experiments versus a relay where agents actually collaborate. “If I am sending out an email campaign, I could have a copy agent, a Figma agent, and a coding agent. Right now, teams are building those individually, but at some point you need orchestration so they can pass work back and forth.” Execution is where many experiments stall. An agent cannot just generate outputs in a vacuum. It needs an environment where the work lives and runs. Sometimes this looks like a custom GPT creating copy inside OpenAI. Other times it connects directly to a marketing automation platform to publish campaigns. Execution means wiring agents into systems that already matter for your business. That way you can turn novelty into production-level work. Feedback and human oversight close the loop. Feedback ensures agents learn from results instead of repeating the same mistakes, and human review protects brand standards, compliance, and legal requirements. Tools like Zapier already help agents talk across systems, and protocols like MCP push the idea even further. These pieces are developing quickly, but most teams still treat them as experiments. Building infrastructure means treating feedback and oversight as required layers, not extras. Key takeaway: Agentic infrastructure requires more than a handful of isolated agents. Build it in five layers: a unified data warehouse, orchestration to coordinate handoffs, execution inside production tools, feedback loops that improve performance, and human oversight for brand safety. Draw this stack for your own team and map what exists today. That way you can see the gaps clearly and design the next layer with intention instead of chasing hype. Self Healing Data Quality Agents Autonomous data quality agents are being pitched as plug-and-play custodians for your warehouse. Vendors claim they can auto-fix more than 200 common data problems using patterns they have already mapped from other customers. Instead of ripping apart your stack, you “plug in” the agent to your warehouse or existing data layer. From there, the system runs on the execution layer, watching data as it flows in, cleaning and correcting records without waiting for human approval. The promise is speed and proactivity: problems handled in real time rather than reports generated after the damage is already done. The mechanics are ambitious. These agents rely on pre-mapped patterns, best practices, and the accumulated experience of diverse customer sources. Their features go beyond simple alerts. Vendors market capabilities like: Data issue detection that flags anomalies as records arrive.Auto-generated rules so you do not have to write manual SQL for every edge case.Auto-resolution workflows that decide which record wins in conflict scenarios.Self-healing pipelines that reroute or repair flows before they break downstream dashboards. Aboli noted that the concept makes sense in theory but still depends heavily on the quality of metadata. She recalled using Snowflake Copilot and asking it for user lists by specific criteria. The model understood her intent, but it pulled from the wrong tables. “If it had the right metadata, the right dictionary, or if I had access to the documentation, I could have navigated it better and corrected the tables it was looking at,” Aboli said. Phil highlighted how this overlaps with data observability tools. Companies like Informatica, Qlik, and Ataccama already dominate Gartner’s “augmented data quality” quadrant, while newcomers are rebranding the category as “agentic data management.” DQ Labs markets itself as a leader in this space. Startups like Acceldata in India and Delpha in France are pitching autonomous agents as the future, while Alation has gone further by releasing a suite of agents under an “Agentic Data Intelligence” platform. The buzz is loud, but the mechanics echo tools that ops teams have worked with for years. Aboli stressed that marketers and ops leaders should resist jumping straight to procurement. Demoing these tools can spark useful ideas, and sometimes the exposure itself inspires practical fixes in-house. The key is to connect adoption to a specific pain point. If your team loses days untangling duplicates and broken joins, the ROI might be obvious. If your pipelines already hold together through strict governance, then the spend may not pay off. Key takeaway: Autonomous data quality agents can detect issues, generate rules, resolve conflicts, and even heal pipelines in real time. Their effectiveness depends on metadata discipline and the actual pain of bad data in your org. Use vendor demos as a scouting tool, then match the investment to measurable business problems. That way you can avoid buzzword chasing and apply agentic tools where they drive the most immediate value. Data Activation Agents

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  2. 190: Henk-jan ter Brugge: The Head of Martech at Philips thinks martech has outgrown marketing and it’s time we lead like pirates

    OKT. 7.

    190: Henk-jan ter Brugge: The Head of Martech at Philips thinks martech has outgrown marketing and it’s time we lead like pirates

    What’s up everyone, today we have the pleasure of sitting down with Henk-jan ter Brugge, Head of global digital programs and Martech at Philips. (00:00) - Intro (01:17) - In This Episode (05:11) - Embracing the Digital Pirate Mindset in Martech (16:18) - Why Clean Data Is the Real Treasure Map for AI in Marketing Ops (19:20) - Why Composable Martech Stacks Work in High Seas Regulated Enterprises (24:35) - Rethinking Martech as People Tech (32:51) - Elevating Martech Teams Beyond Button Pushing (37:16) - Where Martech Should Report in the Organization (42:58) - Unlocking Innovation Through the Long Tail of Martech (47:42) - The Limits of Vendor Isolation in Martech (52:12) - Philips Digital Marketing & e-Commerce Stack (55:10) - How to Use Weekly Prioritization to Protect Energy Summary: Henk-jan works like a pirate inside the navy, exposing inefficiency with data, redesigning roles around real capabilities, and breaking AI promises into measurable wins backed by clean data and clear standards. He treats composability as an operating model with budgets tied to usage, gives local teams autonomy within guardrails, and measures martech by how it serves people and drives revenue. Ops leaders earn influence by pulling in allies and securing executive sponsorship, while reporting debates matter less than accountability and outcomes. Real innovation comes from embracing the long tail of smaller tools, working with vendors who integrate into the ecosystem, building adoption models with champions, and protecting energy through ruthless prioritization.About Henk-jan Henk-jan ter Brugge is Head of Digital Programs and Martech at Philips, where he leads the global digital marketing and ecommerce technology team. With over a decade at Philips, he has driven transformation across CRM, ecommerce, sales enablement, web experience, ad tech, analytics, and AI innovation.  Henk-jan is a lean and agile certified leader who believes technology is an enabler, but it’s people who create the real impact. His career spans international experience in Seoul, Paris, and Shanghai, and he is a frequent keynote speaker on martech, salestech, and digital transformation. Passionate about improving health and wellbeing through meaningful innovation, he connects strategy, technology, and change management to deliver customer value at scale. Embracing the Digital Pirate Mindset in Martech Pirates were early system hackers. They rewrote rules on their ships, experimented with shared decision-making, and introduced ideas like equal pay centuries before they reached land. That spirit of rewriting norms has carried into Henk-jan’s work in martech. He frames the pirate as someone inside the navy, pushing the big ship to move differently, rather than a rogue causing chaos on the outside. Corporate inertia creates its own myths. Vendor onboarding still takes 12 to 18 months in some organizations. Translation cycles hold content hostage for weeks. Colleagues accept these delays as culture, with a shrug and a “that’s just how we do things.” Henk-jan refuses to let tradition dictate output. He arms himself with data and turns it into proof. If a team claims a translation cycle takes three months, he presents the real number: 10, 15, maybe 20 days. “Everything we say can be data driven. If someone tells me translation takes three months, I can show with data that it takes 10, 15, maybe 20 days. The data talks there.” The pirate mindset works only when it builds coalitions. Lone rebels fade out in corporate structures. Movements form when people across teams share the same impatience for inefficiency and the same hunger for progress. That is why Henk-jan focuses on allies who welcome change. With them, he introduces controlled experiments that rewire expectations step by step until the new way becomes the default. One of his boldest moves came in team design. He rebranded product owners as platform managers. They stopped acting like ticket clerks and became capability builders, consultants, and business partners. They handled strategy, education, and enablement, while still owning the backlog. A time study revealed that 70 percent of team energy had been going into internal operations. After the shift, 60 percent went directly into business-facing work. The lesson was clear: titles shape behavior, and behavior shapes impact. Key takeaway: The digital pirate mindset thrives when you expose inefficiency with data, recruit allies who share your appetite for change, and redesign roles so teams build capabilities instead of servicing tickets. Work inside the system, use transparency to gain trust, and experiment in controlled steps. That way you can redirect energy from internal bureaucracy toward direct customer value, creating momentum that compounds over time. Why Clean Data Is the Real Treasure Map for AI in Marketing Ops Speaking of chasing treasures… AI has forced leadership teams to finally pay attention to the quality of their data. Henk-jan described it with a simple observation: “Everybody in the company becomes a technologist in a way, even the CEO.” Executives want automation, optimization, and sharper analytics, but none of those things matter without reliable data flowing through the system. Requests for a CDP illustrate the problem. Leaders hear the acronym and assume it represents an instant fix. Henk-jan has seen this cycle many times and insists the smarter move is to break the vision into small, practical wins. CEOs need short stories they can tell at the end of a quarter, stories that show how clean data lifted conversion or reduced wasted spend. Large programs gain momentum when they stack up these smaller wins rather than selling one massive transformation. “The only way to do that well is to slice it up, basically to show some promising use cases. Talking CEO, they need some impactful stories they need to have at the end of the quarter to show what we have delivered.” Clean data depends on discipline across the organization. Henk-jan stressed the need for rules: standards for how data is collected, shared definitions across content systems, and taxonomies that keep categories consistent. Integrations and lifecycle management depend on that structure. Without it, AI experiments turn into siloed pilots that never scale. AI becomes useful only when the groundwork is finished. Leaders may chase demos that look impressive, but real value comes from standards, integration discipline, and lifecycle maturity. These foundations create systems that grow stronger over time rather than projects that fizzle out after launch. Key takeaway: Clean data gives AI something to stand on. Break big promises into small, measurable wins that executives can celebrate at the end of a quarter. Pair those wins with clear rules on data standards, integration discipline, and taxonomy. That way you can build credibility quickly, prove value, and create a foundation where AI programs expand instead of stall. Why Composable Martech Stacks Work in High Seas Regulated Enterprises Composable stacks sound exciting in theory, but at enterprise scale the question is always about execution. Henk-jan calls it the “cradle to grave” lifecycle of martech, and he is not exaggerating. Every new tool at Philips runs through a process: onboarding, building and deploying, adopting, improving, and eventually decommissioning. Each step matters because every skipped detail becomes someone’s day-to-day problem. He warns against the common trap of treating tools like silver bullets. Buying a platform for insights or personalization only matters if there are people inside the business who can operate it. Henk-jan has seen too many o...

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  3. 189: Aditi Uppal: How to capture, activate and measure voice of customer across go to market efforts

    SZEPT. 30.

    189: Aditi Uppal: How to capture, activate and measure voice of customer across go to market efforts

    What’s up everyone, today we have the pleasure of sitting down with Aditi Uppal, Vice President, Digital Marketing and Demand Generation at Teradata. (00:00) - Intro (01:15) - In this Episode (04:03) - How to Use Customer Conversations to Validate Marketing Data (10:49) - Balancing Quantitative Data with Customer Conversations (16:14) - Gathering Customer Insights From Underrated Feedback Channels (22:00) - Activating Voice of Customer with AI Agents (29:09) - Voice of Customer Martech Examples (34:48) - How to Use Rapid Response Teams in Marketing Ops (39:07) - Building Customer Obsession Into Marketing Culture (43:44) - Why Voice of Customer Works Differently in B2B and B2C (48:26) - Why Life Integration Works Better Than Work Life Balance Summary: Aditi shows how five honest conversations can reshape how you read data, because customer language carries context that numbers miss. She points to overlooked signals like product usage trails, community chatter, sales recordings, and event conversations, then explains how to turn them into action through a simple pipeline of capture, tag, route, track, and activate. Tools like BrightEdge and UserEvidence prove their worth by removing grunt work and delivering usable outputs. The system only works when culture supports it, with rapid response channels, proposals that start with customer problems, and councils that align leaders around real needs. Blend the speed of B2C listening with the discipline of B2B execution, and you build strategies grounded in reality.About Aditi Aditi Uppal is a data-driven growth leader with over a decade of experience driving digital transformation, product marketing, and go-to-market strategy across India, Canada, and the U.S. She currently serves as Vice President of Digital Marketing and Demand Generation at Teradata, where she leads global strategies that fuel pipeline growth and customer engagement. Throughout her career, Aditi has built scalable marketing systems, launched partner programs delivering double-digit revenue gains, and led multi-million-dollar campaign operations across more than 50 technologies. Recognized as a B2B Revenue Marketing Game Changer, she is known for blending strategy, operations, and technology to create high-performing teams and measurable business impact. How to Use Customer Conversations to Validate Marketing Data Dashboards create scale, but they do not always create confidence. Aditi explains that marketers often stop at what the model tells them, without checking whether real people would ever phrase things the same way. Early in her career she spent time talking directly to retailers, truck drivers, and mechanics. Those interactions were messy and slow, filled with handwritten notes, but they gave her words and patterns that no software could generate. That language still shapes how she thinks about campaigns today. She argues that even a small number of conversations can sharpen a marketer’s decisions. Five well-chosen interviews can give more clarity than months of chasing analytics dashboards. Once you hear a customer describe a problem in their own terms, the charts you already have feel more trustworthy. As Aditi put it: “If you get an insight that says this is their pain point, it helps so much to hear a customer saying it. The words they use resonate with them in ways marketers’ words often do not.” She points out that B2C teams benefit from built-in feedback loops since their channels naturally keep them closer to customers. B2B teams, on the other hand, often hide behind personas and assumptions. Aditi suggests widening the pool by talking to students and early-career professionals who already use enterprise software. They may not be buyers today, but they become decision makers tomorrow. Those conversations cost almost nothing and create raw material more valuable than agency-produced content. She frames the real task as choosing the right method for the right question. If you want to refine messaging, talk to your most active customers. If you want to understand adoption patterns, run reports. If you want to pressure test a product roadmap, combine both and compare the results. Decide upfront what you need and when you need it. Then continue adjusting, because customer understanding is not a one-time project, it is an ongoing discipline. Key takeaway: Use customer conversations as a validation layer for your data. Pair five direct interviews with your dashboards, and you gain language, context, and trust that numbers alone cannot provide. Always define why you need an insight, then pick the method that gets you there fastest. That way you can build messaging, campaigns, and roadmaps grounded in reality rather than in assumptions. Balancing Quantitative Data with Customer Conversations Marketers keep adding dashboards, yet confidence in the numbers rarely grows. Aditi argues that a few customer conversations often do more to build certainty than a warehouse of metrics. Early in her career she spent long days interviewing retailers, truck drivers, and mechanics. She filled notebooks with their words, then worked through the mess to find common threads. The process was slow, but it created clarity that still guides her perspective today. “You do not need hundreds of those conversations. You just need five, and you will come out so much more confident in the data you are looking at.” That perspective challenges a common assumption in B2B marketing. Models can predict buying intent, but they cannot capture the urgency or tone that customers bring to their own words. Dashboards may flag data scientists as target buyers, yet when you sit with an aspiring data scientist, you hear frustrations and motivations that algorithms miss. Real language often carries sharper meaning than the polished words marketers invent for campaigns. Aditi warns that relying only on quantitative signals pushes teams into a self-referential loop. Marketers build strategies based on metrics, then describe those strategies in their own buzzwords. Direct conversations break that loop. Even five interviews can ground your messaging, highlight gaps in the data, and validate where models are directionally right. B2C teams often benefit from tighter feedback loops through customer-facing channels. B2B teams need to create their own versions of those loops by talking to users directly, including students and early-career practitioners who represent the next generation of decision makers. Every stage of marketing benefits from this practice. Roadmaps become sharper, content becomes more resonant, and campaign ideas carry more weight when tested against real voices. Customer interviews cost little compared to polished content campaigns, yet they create a foundation of confidence that technology alone cannot replicate. Key takeaway: Five direct customer conversations can build more confidence than a room full of dashboards. Capture the exact words your buyers use, compare them with your data models, and use both inputs together. That way you can validate your metrics, sharpen your messaging, and trust that your strategy connects with the people who matter most. Gathering Customer Insights From Underrated Feedback Channels Marketers love surveys. They love sending out NPS links, post-purchase forms, and satisfaction checkboxes that make dashboards look busy. Aditi is blunt about the limits of this ritual. A buying committee has users, influencers, and decision makers. Each group has different needs, and you cannot lump them into a single “customer voice.” If you want useful signals, you have to decide who you are li...

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  4. 188: Rebecca Corliss: Why lifecycle marketers will thrive in the agentic marketing org

    SZEPT. 23.

    188: Rebecca Corliss: Why lifecycle marketers will thrive in the agentic marketing org

    What’s up folks, today we have the pleasure of sitting down with Rebecca Corliss, VP Marketing at GrowthLoop.  (00:00) - Intro (01:20) - In This Episode (03:46) - The Future Agentic Marketing Org (07:59) - The Rise of the Marketing Dispatch Layer (14:47) - Lifecycle Marketers Belong at the Center of Every Agentic Org (21:19) - Why Channel Specialists Must Shift to Journey Orchestration (25:06) - How To Actually Become More Strategic (29:28) - This Team Promoted ChatGPT to Director of Product Marketing (32:55) - What it Means to Be a Specialist in the Moment Works (37:12) - How Systems Thinking Helps Lifecycle Marketers Shine in Agentic AI (40:10) - How AI Expands the Role of Marketing Ops (43:37) - The Speculative Future of Marketing With Compute Allocation and Machine Customers (46:35) - Mesh of Agents Coordinating Across Departments (50:07) - The Rise of Machine Customers (53:55) - How to Stay Energized as a Marketing Leader Summary: Rebecca imagines a future marketing org built on three layers: leadership fluent in data and AI, a dispatch control tower staffed by engineers and privacy experts, and pods that design customer journeys while agents handle scale. Lifecycle marketers are essential to this dispatch layer and provide the “heart,” keeping campaigns authentic. Her own path as a “specialist in the moment” shows the power of adaptability, diving deep where it counts and moving on with impact. The marketers who thrive will be those who pair technical fluency with empathy and judgment.About Rebecca Rebecca is a veteran marketing executive known for building engines that drive outsized growth. She is currently VP of Marketing at GrowthLoop, shaping the go-to-market for its Compound Marketing Engine. Previously, she scaled VergeSense from Series A through Series C with over 8X ARR growth, and at Owl Labs she took the company from launch to 35,000 customers worldwide while establishing it as a future-of-work leader. She also spent eight years at HubSpot, where she grew demand generation to 60K leads per month, doubled blog-driven leads, and built leadership programs that developed the next generation of marketers. Across every role, Rebecca has consistently turned early-stage momentum into durable, scalable growth. The Future Agentic Marketing Org and the Rise of the Marketing Dispatch Layer Rebecca lays out a future where marketing org charts gain an entirely new layer. She predicts three core structures: leadership, dispatch, and pods. Leadership continues to steer strategy, but the demands on CMOs change. They will need fluency in data systems, architecture, and AI operations. Rebecca explains that “CMOs have to flex their technical chops and their data systems and architecture chops,” a shift for leaders who have historically leaned on brand or budget narratives. The dispatch layer functions as the operational hub for campaigns. This group manages data flows, AI orchestration, and channel activations. It operates like a control room for all outbound communication. Dispatch is staffed with people who rarely sat in marketing orgs before. Data engineers move in from IT, privacy specialists join the table, and Rebecca even describes “traffic cops” who arbitrate which campaigns reach a customer when multiple business units compete for the same audience. “Imagine this new dispatch layer, the group that is thinking about the systems, the data, the AI, the architecture, and campaign activation for the entire marketing org holistically.” Pods sit at the edge of this system, each one tasked with a specific objective. A retail pod might obsess over repeat purchases and next best product recommendations. Pods shape customer journeys, creative work, and product presentation. They do not execute campaigns directly. Instead, they work with dispatch to push scaled, AI-driven activations that tie back to their mission. This structure gives pods focus while ensuring campaign execution remains coordinated and efficient. Rebecca stresses that humans remain responsible for organizing this system. Agents will handle execution, but people set goals, decide structures, and elevate the skills required to manage AI effectively. The companies that thrive will be the ones that invest in human fluency now, especially in data architecture and cross-functional collaboration. Marketing leaders cannot wait for agents to make the org smarter. They have to build teams ready to use agents well. Key takeaway: Treat dispatch as a new operational hub inside marketing. Staff it with cross-functional talent such as data engineers, privacy experts, and campaign traffic managers. Align pods around clear business outcomes, and let them focus on customer journeys and creative execution. Give dispatch responsibility for scaling campaigns through AI agents. Start by training CMOs and their leadership peers to speak the language of data and AI strategy. That way you can prepare your organization to actually run an agentic structure instead of scrambling when competitors already have it in place. Lifecycle Marketers Belong at the Center of Every Agentic Org Lifecycle marketers thrive in environments where customer signals drive execution. Rebecca describes them as the people who study every stage of the journey, then translate that understanding into activation rules that actually serve the customer. Agents may handle the heavy lifting, but lifecycle marketers decide what matters and when it matters. They are the human layer that keeps the entire system from drifting into mechanical noise. “If it supports the customer, it supports the business objectives. That is the way everyone wins.” Rebecca explains that lifecycle marketers split into two groups. Some will lean technical and operate directly in the dispatch layer. They will define activation strategies, ensure campaigns run with precision, and use data to protect customer-first thinking. Others will integrate into pods and shape the full journey, using systems thinking to design one-to-one experiences at scale. Both groups carry the same DNA: empathy paired with curiosity about how AI can extend their reach. This structure becomes even more important in content. Generative AI can produce endless material, but personalization collapses if the output feels artificial. Lifecycle marketers bring the judgment required to keep content aligned with customer needs. They will be the people asking hard questions about tone, timing, and authenticity while still leveraging AI to handle scale. The combination of empathy and technical curiosity will keep campaigns human, even as agents flood the stack. Rebecca calls this quality “heart,” and she sees it as the non-negotiable element that AI cannot replicate. Lifecycle marketers carry responsibility for maintaining authenticity while still driving one-to-one marketing. Their role is not to fight against automation but to guide it toward outcomes that respect the customer experience. Key takeaway: Lifecycle marketers should sit at the center of every agentic org. Place technical lifecycle marketers in the dispatch layer to design activation rules that protect the customer. Embed strategic lifecycle marketers inside pods to architect journeys that scale with authenticity. Treat empathy as the operational safeguard, and give lifecycle marketers the authority to enforce it. That way you can use AI to expand capacity without sacrificing trust. Why Marketing Channel Specialists are Fading Channel specialists are facing a turning point. Rebecca explains that AI agents now handle many of the mechanical tasks that ...

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  5. 187: John Saunders: Building the ultimate operating engine for a modern agency

    SZEPT. 16.

    187: John Saunders: Building the ultimate operating engine for a modern agency

    What’s up everyone, today we have the pleasure of sitting down with John Saunders, VP of Product at Nova / Power Digital Marketing. Power Digital is a San Diego-based growth marketing firm. Nova is their proprietary marketing technology. (00:00) - Intro (01:15) - In This Episode (03:26) - How an Agency Operating System Reduces Silos (05:47) - Why Context Driven Analytics Replaces Dashboards (09:15) - Building a Single Source of Truth in Marketing Data (16:00) - Building an AI Cockpit Before AI Copilots (18:26) - Why Data Accuracy and Transparency Build AI Trust (28:28) - Building Internal Data Products for Agencies (34:09) - Reducing Complexity in Martech Product Development (39:16) - How To Tell If An AI Tool Is More Than A Wrapper (46:49) - How to Build Client Portals That Clients Actually Use (49:50) - Finding Happiness in Building and Experimentation Summary: Agencies are drowning in tools, dashboards, and AI gimmicks, but John Saunders has spent years building something that actually works. Nova started as an internal fix and grew into an operating system that strips away noise, delivers context with every number, and gives AI a cockpit filled with real operational data. Along the way John learned that trust comes from accuracy, speed, and transparency, and that adoption only happens when products remove steps instead of adding them. From client portals to analytics to AI, his story shows how clarity beats complexity and why agencies that chase it finally get technology that feels like leverage instead of liability.About JohnJohn Saunders is the Vice President of Product at Power Digital Marketing. He leads strategy, UX, operations, and AI for nova, the agency’s enterprise marketing technology platform that connects with more than 2,000 integrations. Since 2021, he has grown the technology team from 2 to 40 members, delivered more than 20 production-ready applications, and developed intelligence tools that improve client retention and increase lifetime value. He has also built partnerships with Google, Meta, TikTok, and Amazon that resulted in multi-million-dollar funding and new product capabilities. Prior to his current role, John served as Vice President of Technology. He built the first applications that became the foundation of nova and improved scalable systems, API integrations, cloud performance, and automation for the firm. He previously worked as Software Development Project Manager at Internet Marketing Inc. (now REQ), and Co-Founder of Brightside Network Media, a platform that combined technical design with storytelling to highlight culture and music. John has also mentored students at the Lavin Entrepreneurship Center at San Diego State University. He guided undergraduates in UX, product strategy, and agile workflows while encouraging leadership and collaboration in a hands-on environment. How an Agency Operating System Reduces Silos Agencies are drowning in tools. CRMs handle sales, project boards track tasks, invoicing software manages billing, and analytics dashboards measure performance. Each tool may solve a specific problem, but together they create a scattered system where every team works in isolation. John Saunders has seen this problem repeat across agencies, and his solution is direct. Build a single operating system that reflects how the agency actually works rather than relying on disconnected platforms that never sync. John described Nova as that system. Instead of forcing teams to reinvent contracts or pricing every time, Nova uses a service library with set rates and guidelines. Automation handles the repetitive work, so teams spend less time drafting proposals and more time serving clients. Nova acts as a hub for the agency’s real workflows. It connects sales, operations, and delivery into one shared environment where everyone can see the same information. "With an agency OS, we are trying to fix this problem where there are so many tools and platforms that people work on, and that inherently creates silos. With one system focused on operations, it provides a central spot for everybody to work from, which creates efficiency and alignment." The need for this kind of system is obvious once you look closely at agency life. Account managers keep their own spreadsheets, sales leaders adjust pricing rules on the fly, and creative teams use tools that never connect with operations. The result is misalignment, duplicated effort, and wasted hours. An operating system forces the agency to define its rules and then codify them into the platform. That way you can cut the daily noise and create repeatable workflows that scale. Agencies often assume the next SaaS subscription will solve their problems. The reality is that the core problems are internal. Building an operating system like Nova does not replace tools, it makes them work together. It creates one place where every team operates from the same playbook. That way you can reduce inefficiency, strengthen alignment, and free people to focus on client work instead of wrestling with tool silos. Key takeaway: An agency operating system reduces silos by centralizing contracts, pricing, and service guidelines inside one platform. Standardized rules and automation save time, while a shared hub keeps every team aligned. Instead of adding another tool to an already bloated stack, define your workflows, codify them into an operating system, and create an environment where teams work together with speed and clarity. Why Context Driven Analytics Replaces Dashboards Dashboards impress people for about five minutes. They get pasted into a slide deck, admired in a meeting, and then forgotten. They look sleek but rarely change how teams actually work. John Saunders describes them as “dead weight,” and he is right. Most dashboards are static trophies, not decision-making tools. John insists that analytics must carry a point of view. Agencies do their best work when they stop presenting raw numbers and start tying those numbers to judgment. Nova, the product his team builds at Power Digital, bakes that opinion into everything it produces. Every measurement is run through a filter: does this reflect the right way to evaluate performance? If the answer is no, it never makes it to the client. That rule sounds simple, yet it separates meaningful analytics from the noise of charts that show data without direction. He also points out that numbers without context fail to tell the full story. Performance depends on more than what a database records. It depends on client conversations, launch dates, migrations, and campaign decisions that live outside structured tables. Nova integrates those details directly into the analytics layer. The result is data that reads like a story, not a sterile snapshot. “Performance isn’t just the data itself. It’s everything around it.” John sees analytics moving toward systems that feel conversational. Static dashboards freeze data in time, while teams need a living engine that blends numbers with the narrative behind them. Instead of flipping between charts and email threads, the analysis itself should surface both at once. That way analytics become a dialogue with context, not a set of disconnected metrics. Key takeaway: Treat dashboards as disposable and focus on analytics that combine three things: a strong opinion about what matters, context from the real world, and delivery in a format that feels like a conversation. When you give your team numbers plus narrative, you give them clarity that drives decisions. Replace static charts with context driven analytics so people act faster, waste less energy, and actually understand what the data is te...

    54 perc
  6. 186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops

    SZEPT. 9.

    186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops

    What’s up everyone, today we have the pleasure of sitting down with Olga Andrienko, Former VP of Marketing Ops at Semrush.  (00:00) - Intro (01:24) - In This Episode (03:55) - How AI Agents Reshape Marketing Ops Roles (08:53) - How To Beat AI Imposter Syndrome And Start Using Custom GPTs (13:28) - How AI Content Agents Generate Drafts Using Internal Context (24:29) - How to Use a Risk and Reward Grid to Prioritize AI Projects (33:19) - How To Use Google Workspace To Skip AI Vendor Approvals (40:00) - How To Decide Which AI Agent to Use (46:44) - How To Build an AI-First Reflex in Marketing Ops (51:59) - AI’s Endgame: Play-to-Earn and Mandatory Human Quotas (01:03:58) - What Happens When You Optimize Your Body Like a Martech Stack Summary: Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI. She started with the work she already knew, made it faster, and used that time to build better systems. Now she’s looking ahead, watching work blur into participation, prepping for human quotas, and making sure ops teams aren’t caught off guard while the rest of the company is still testing prompts.About Olga Olga Andrienko spent nearly 12 years at Semrush, where she helped build one of the strongest B2B marketing brands in tech. She started by leading social media, then expanded into global marketing, eventually becoming VP of Brand and later VP of Marketing Operations. She helped guide the company through its IPO, launched brand campaigns that drove massive reach, and scaled AI systems that saved her teams hundreds of hours. Most recently, she built out a marketing and AI ops function from scratch, automating reporting, content feedback, and influencer analytics across the org. Recently, Olga announced she was leaving Semrush to go out on her own. She’s now building a marketing SaaS product while advising companies on how to use AI agents to rethink marketing operations from the inside out. How AI Agents Reshape Marketing Ops Roles Olga had already logged countless hours with Claude and ChatGPT. She was building chatbots, fine-tuning prompts, and staying sharp on every update. Then she joined a weekend course on agent-based AI. At first, it felt like overkill. By the end of day two, she had completely changed direction. That course forced her to realize she had been spending time in the shallow end. Agent AI wasn’t just a smarter assistant. It was a structural overhaul. It changed what could be automated and who was needed to do it. Agent AI builds systems instead of just responding to inputs. Olga described a clean divide between tools that help you finish tasks faster and agents that actually run the tasks for you. How agent AI differs from task-level tools:Traditional tools require manual input for each useAgent systems operate autonomously and initiate actionsTools accelerate individual workAgents orchestrate end-to-end processesTools help you move fasterAgents help you step away entirely She saw use cases stacking up that didn’t fit inside marketing’s current playbook. Systems could now operate without manual checkpoints. Processes that once relied on operators could be built into fully autonomous loops. “I went into panic mode. Even with our tech stack at Semrush, I realized we were behind. Every company is behind.” The realization came with a cost model. Internal adoption of Claude and ChatGPT was rising fast. Olga noticed growing subscription bills across teams, with everyone spinning up individual accounts. She ran the numbers and saw the future expense curve. Giving each person their own sandbox didn’t scale. What made sense was building shared tools through APIs, designed to solve repeatable tasks. That way you can maintain quality, cut costs, and still give everyone access to powerful AI systems. Timing mattered. Olga was coming off a quarter where she had high visibility, internal trust, and a direct line to leadership. Instead of waiting for AI priorities to come down from the top, she used that leverage to move. She pitched a new team and made the case for shifting from brand to ops. She had technical interest, political capital, and an urgent belief that velocity mattered more than perfection. Key takeaway: Marketing ops leaders are uniquely positioned to build agent-level systems that scale across teams. Instead of waiting for strategy teams to greenlight AI plans, use cost data to make the case for shared infrastructure. Build with APIs, not individual tool access. Push for automation at the system level, not just task-level assistance. If you understand the workflows, know the tools, and already have trust inside the org, you are the one who should be building what comes next. How To Beat AI Imposter Syndrome And Start Using Custom GPTs AI imposter syndrome shows up fast. It tells you the developers will handle it, the data team will figure it out, and you should stick to writing copy or launching campaigns. Olga ignored that voice. She opened up ChatGPT, looked at the most repetitive task on her plate, and started testing. No credentials. No roadmap. Just frustration, curiosity, and a weekend. “Anybody who says they have figured AI out or that they’re on top of this, they’re lying to you.” She did not wait for a manager to assign her an AI project. She looked for work she already understood. Rewriting vague marketing text. Fixing formatting issues. Translating copy into other languages without sounding robotic. These were not moonshot experiments. They were annoyances. She built a custom GPT for each one. That work gave her traction. It also gave her time back. She found herself reclaiming an hour a day just by handing off the small, repeatable parts of her job. That time opened up new space to build more. The learning came naturally because it was grounded in daily tasks she already owned. “If we look at this like a Maslow pyramid, the repetitive tasks are the base layer. That’s where you start.” Confidence grows when the work starts to feel useful. That shift does not come from reading whitepapers or watching LinkedIn demos. It comes from applying the tool to one thing you do every week and watching it cut your time in half. That is how you build fluency. Not all at once. One custom GPT at a time. Key takeaway: Choose a task you already know well and automate it with a custom GPT. Keep the instructions specific and tied to your current workflow. Run it repeatedly until it saves you real time. Then build another. Confidence in AI tools comes from using them to solve work you already understand, not from waiting until you feel qualified. AI Use Cases in Marketing: AI Agents Creating Drafts from Context That Humans Perfect AI content agents are getting better, but they are not off the leash. Olga built two systems to test how far automation can go without turning content into generic filler. One starts with human writers. The other starts with a structured form. Both rely on real performance data, brand knowledge, and experienced editors. The first system runs inside Google Docs. Writers draft copy. The AI overlay scores it using past campaign performance, conversion data, and hand-labeled examples of strong and weak copy. It flags weak headlines, vague CTAs, bloated structure. Then it explains why. Olga’s team noticed that when the starting draft is weak, AI only sm...

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  7. 185: Jonathan Kazarian: Platforms vs point solutions and the marketing operator’s dilemma

    SZEPT. 2.

    185: Jonathan Kazarian: Platforms vs point solutions and the marketing operator’s dilemma

    What’s up everyone, today we have the pleasure of sitting down with Jonathan Kazarian, Founder & CEO of Accelevents. (00:00) - Intro (01:35) - In This Episode (03:41) - Are Point Solutions Actually a Distraction for Marketing Teams? (09:32) - Data Models Can Decide Platforms or Point Solutions (14:20) - Contact Based Pricing Skews Platform Versus Point Solution Costs (19:44) - Integration Depth Can Decide Platforms Versus Point Solutions (31:32) - Point Solutions Provide Faster and Smarter Support Than Platforms (37:28) - Documentation Shapes Point Solution Stacks (42:01) - How to Manage Shiny Object Syndrome in Marketing Ops (49:35) - A Founder's Admiration for Marketing Operators (54:42) - Why Continuous Growth Keeps Founders Balanced Summary: Jonathan framed point solutions as late-night distractions that add baggage, while Phil argued they solve real constraints platforms can’t touch, like global routing or multilingual campaigns. Darrell pulled the lens to data models, showing how shared schemas keep stacks clean but warehouse-native teams lean on composability for speed and control. Money made the tradeoffs clear when Phil cut HubSpot costs from $150k to $70k with Ghost, ConvertFlow, and Zapier, and Jonathan countered that the problem was platform fit, not price alone. Support stories added texture, with Phil praising startups that fix issues in Slack within hours and Jonathan noting how urgency and empathy thrive in smaller teams. The thread ran through every topic: platforms provide coherence and stability, point solutions unlock lift when constraints demand it, and the operator’s job is knowing which moment they are in.About JonathanJonathan Kazarian is the Founder & CEO of Accelevents, an all-in-one event management platform trusted by over 12,500 organizations worldwide. Since launching in 2015, he has led the company’s growth into a leader in powering in-person, virtual, and hybrid events with enterprise-grade features and 24/7 customer support. Before Accelevents, Jonathan worked in investment management and business development at Windham Labs and Windham Capital, where he supported strategy and client relationships across $1.5B in global assets. Based in Miami, he’s passionate about building technology that makes life easier for event organizers. Are Point Solutions Actually a Distraction for Marketing Teams? We all know the cycle of startups and enterprise. Point tools surge to fix sharp pains, a small group wins, platforms acquire them, founders spin out, and the next crop floods your feed. Jonathan thinks that those shiny tools pull teams off the work that actually moves numbers. He describes a scene every operator recognizes, the glow of a laptop at 3 a.m. and a to-do list that did not get shorter by sunrise. “I will see something, get excited about it, and then I am up until 3 a.m. playing with it. It distracts me from the things that actually matter.” Jonathan sets a firm bar for focus. Ship on a platform first, then layer selectively when a real constraint shows up. He treats events as a pillar beside CRM and marketing automation, so his platform must deliver value on day one without a four-tool puzzle. He stays explicit about the work that pays the bills: Tighten positioning so buyers understand you in one scroll.Communicate with customers in their language, not vendor speak.Make the core stack usable for sales, finance, and ops, not only for marketing.That way you can add niche tools later without freezing adoption while integrations sprawl. Phil takes the other corner and argues for composability with lived examples. He respects HubSpot and has shipped plenty on it, but real constraints demand specialists. Example: territory routing across pooled rep availability needs a product built for that job, which is why RevenueHero exists. Example: global email collaboration with dozens of languages and brand guardrails needs serious template control, which is why Knak clears roadblocks. Phil speaks to the operator who needs real lift: Match routing logic to the sales org rather than bending the org to the tool.Scale content production with permissions, templates, and translation workflows that teams actually follow. “I have built stacks that blended platform basics with pointed upgrades for specific constraints, and those upgrades paid off when growth demanded it.” Jonathan agrees on the destination, then anchors the sequence. Buy, go live, and prove value within weeks. Add point tools only when a named constraint blocks revenue or customer experience. Keep the stack boring where it should be boring. Run a simple playbook that your team can execute: Stand up your platform baseline and drive daily use from sales and marketing.Write down the first constraint that limits revenue or adoption.Choose one specialist that removes that constraint end to end.Set a 14-day integration target with one success metric tied to pipeline or retention.Move to the next constraint when the metric shows lift. Key takeaway: Point solutions can give shiny object syndrome to the undisciplined, but for the trained ops folks, they are upgrades on a platform backbone that are used to remove constraints that block revenue or adoption. Ship a platform baseline, then add specialists when the job requires things like territory routing, multilingual content control, or workflow depth that platforms rarely specialize in. Treat this as an operating rule, decide by trigger rather than trend, and tie every addition to a single metric that moves pipeline or retention. Why Data Models Decide Platforms or Point Solutions Darrell sets the table with a consumer gut check, iOS versus Android, and he leans into reliability as the buying trigger. He points to the calm moment when AirPods pair and everything just works, which mirrors the promise of packaged platforms that share a core operating system. He still sees sharp edges, like deduplication, that call for extra tooling and he asks for a push off the fence. "I love it when you buy a new Apple device and it just connects." Jonathan makes the platform case with a concrete pattern, two full platforms that cooperate. He points to Gmail on iOS as normal behavior rather than a bolt-on oddity, and he maps that to how customers pair Accelevents with HubSpot or Salesforce across the event-to-CRM vertical. He calls out a hard truth that veterans recognize, some big-suite acquisitions integrate worse than third parties. He zeroes in on the backbone that actually saves time, a consistent data model. A shared schema speeds onboarding and shortens the time from login to first useful outcome.Common structures reduce UTM and conversion mapping that steals cycles from the team.Clear seams across products limit the need for specialist tool owners. "When you connect Gmail on iOS, you are bolting two platforms together." Phil answers with the warehouse-first pattern that many modern teams now run. A team with a data engineer, quality checks, and a lakehouse or warehouse prefers composable tools and custom models. That team treats the suite as a source, not the system of record, and wires APIs or bypasses based on need. He warns that a single vendor model can force the business into shapes that never fit. A staffed data function supports attribution and identity stitching in code you control.A warehouse-centered stack concentrates transforms, lineage, and governance where you already work.Custom metrics move faster when they live in versioned models, not tucked inside a vendor UI.API-first wiring keeps you from waiting on a roa...

    58 perc
  8. 184: Nadia Davis: How to decide if attribution data is good enough to guide strategy

    AUG. 26.

    184: Nadia Davis: How to decide if attribution data is good enough to guide strategy

    What’s up everyone, today we have the pleasure of sitting down with Nadia Davis, VP Marketing at CaliberMind. (00:00) - Intro (01:12) - In This Episode (02:53) - Understanding the Attribution Periodic Table Framework (07:49) - Why Marketing Teams Face Higher ROI Pressure Than Other Departments (20:15) - Why Attribution Fails Without Data Stewardship (33:02) - Treating Multi-Touch Attribution as an Analytical Tool (39:05) - Exploring Chain Based Attribution Models for B2B Marketers (46:31) - Why Customizing Markov Chain Attribution Improves Accuracy (50:56) - How to Decide When Attribution Data Is Good Enough to Guide Strategy (01:00:00) - Why Marketing Operations Defines Multi Touch Attribution Success (01:04:50) - Why Time Management Drives Career Fulfillment Summary: Nadia learned early that attribution keeps you in business, proving to executives why the budget, the team, and the work matter. Seeing “attribution is dead” posts, she built her Attribution Periodic Table to show data modeling, measurement rules, and cross-team alignment as one connected system. In B2B, where budgets are treated like investment portfolios, she uses multi-touch attribution to connect brand and demand to revenue in CFO terms. For her, it’s an analytics tool, not a scoreboard, shaped by sequences like her govtech playbook where event conversations plus on-demand webinars moved deals forward. Chain-based and Markov models help her cut noise, drop vanity metrics, and ground decisions in logged, meaningful touches, all anchored in strong marketing operations that make multi-touch attribution something teams actually trust.About Nadia Nadia Davis is the VP of Marketing at CaliberMind, where she leads demand generation, ABM, and marketing operations. She is known for building teams from scratch, overhauling martech stacks, and creating data-driven programs that sales teams can act on immediately. With over 15 years in B2B marketing, she has worked across SaaS, IT automation, healthcare tech, and data platforms, consistently delivering measurable growth by aligning marketing execution with revenue goals. Her career includes senior roles at PayIt, Stonebranch, LexisNexis Risk Solutions, Informa, and ND Medica Inc., as well as nearly a decade as an ABM and digital strategy consultant. She has led global campaigns, designed persona-driven targeting, run high-profile industry events, and built marketing programs that continue to deliver pipeline well beyond launch. A former Girls in Tech board member, Nadia combines hands-on technical expertise with the leadership skills to grow both teams and results. The Periodic Table of Marketing Attribution Elements Nadia has worked in revenue marketing long enough to know attribution is a survival tool. In every demand generation and performance role, she carried it like part of her standard kit. It was how she justified headcount, protected budgets, and kept the lights on in her department. Attribution helped her prove progress in a language executives understood. When she took over marketing at CaliberMind, she noticed the volume of “attribution is dead” posts climbing in her feed. The pattern felt familiar. Marketing tactics often get declared obsolete the moment they fail for someone, then replaced with whatever is trending. From her perspective, most of those posts came from SMB marketers moving on after a bad run. Meanwhile, enterprise teams were applying attribution with discipline, pairing it with strong data modeling, and getting measurable results. They simply were not talking about it publicly. That split in sentiment drove her to dig deeper. She wanted to measure the gap between what people were saying and what they were actually doing. The outcome was the State of 2025 Attribution report, anchored by her Revenue Marketing Periodic Table. Nadia built it to show attribution as part of an integrated framework, not a lone tactic. She broke it down into interconnected components: Data modeling that improves accuracy and removes noiseMeasurement frameworks that define terms and keep reporting consistentCross-functional alignment that ensures teams interpret the data the same way "So many things may seem completely disconnected, yet they all come together within a bigger ecosystem." The iceberg metaphor stuck with her. Most marketers focus on the visible metrics, but the real forces driving success are below the surface. Choosing the periodic table format brought this idea into focus. It showed each element as part of a larger system, each with its own role and complexity. Nadia even remembered struggling with chemistry in school, to the point where she once cheated on a test because she could not memorize the valency of certain elements. That frustration helped her appreciate the value of a clear visual framework when dealing with something complicated. The periodic table worked because it grouped related elements, revealed their relationships, and made the whole system easier to navigate. Key takeaway: Build attribution like a connected ecosystem. Pair it with precise data modeling, clear measurement frameworks, and strong cross-team alignment so every metric connects to a broader strategy. Map your system like a periodic table, where each element has a defined purpose and a place in the structure, that way you can spot gaps, diagnose problems faster, and prove impact without relying on surface-level numbers. Why Marketing Teams Face Higher ROI Pressure Than Other Departments Marketing leaders manage one of the most lopsided jobs in business. One half of the work runs on instinct, creativity, and the psychology of memory. The other half is rooted in measurement, analytics, and financial accountability. Nadia points out that most marketers do not come from a statistics-heavy background, yet they are expected to operate as if they did. The pressure is not just to build campaigns that inspire but to show how those campaigns directly affect the bottom line. In B2B, the stakes climb even higher. Sales cycles can drag for months or even years, and the money behind your budget often comes from venture capital or private equity. Those investors see marketing spend as growth capital, not operational overhead. That means they expect a return. Nadia compares it to giving a retirement manager your savings. You would not leave them unchecked. You would want to see exactly how those dollars are working and why certain investments are made. Other departments do not face the same revenue-tied scrutiny. Finance manages operating budgets. Sales has smaller discretionary pools for travel and entertainment. HR spends what it takes to keep the team functioning. None of those groups is routinely asked to tie their activities to closed-won revenue. Marketing is, because its budget is treated as a bet on future growth, not a cost of maintaining the business. The challenge is translating marketing results into terms that matter to the C-suite. Nadia frames it clearly: “You are here because you got money to spend that we invested with you, and we want to have the responsible output from how this money is performing.” But that translation is rarely straightforward. Engagement, recall, and psychological impact are powerful, yet they do not speak the same language as pipeline targets and closed deals. In SaaS and tech, that disconnect is shrinking fast as investor pressure mounts. Marketing leaders who can quantify the financial impact of creative work are the ones who keep their budgets, and their seat at the table. Some people struggle with making decisions without near-perfect certainty, relying on data ...

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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.

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  • Létrehozó
    Phil Gamache
  • Aktív évek
    2020. - 2025.
  • Epizódok
    192
  • Besorolás
    Szókimondó
  • Szerzői jog
    ©2025 Humans of Martech Inc.
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    Humans of Martech

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