Humans of Martech

Careers place a ton of demand on energy and attention way before results start to stabilize. Many operators discover that health and routine determine how long they can operate at a high level.I spoke with 50 people working in martech and operations about how they stay happy under pressure. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now.Today we start with part 1: stability through routines, boundaries, and systems that protect the body and mind. We’ll hear from 15 people:(00:00) - Teaser (01:05) - Intro (01:30) - In This Episode (04:09) - Austin Hay: Building Non Negotiables (08:06) - Sundar Swaminathan: Systems That Prevent Stress (12:33) - Elena Hassan: Normalizing Stress (14:32) - Sandy Mangat: Managing Energy (16:31) - Constantine Yurevich: Designing Work That Matches Personal Energy (19:05) - Keith Jones: Intentional Work Rhythms (23:58) - Olga Andrienko: Daily Health Routines (26:06) - Sarah Krasnik Bedell: Outdoor Routines (27:21) - Zach Roberts: Physical Reset Rituals Outside Work (28:57) - Jane Menyo: Recovery Cycles (31:56) - Angela Vega: Chosen Challenges and Recovery Cycles (36:09) - Megan Kwon: Presence Built Into the Day (37:50) - Nadia Davis: Calendar Discipline (39:36) - Henk-jan ter Brugge: Planning the Week as a Constraint System (43:15) - Ankur Kothari: Personal Metrics (44:07) - Outro Austin Hay: Building Non NegotiablesOur first guest is Austin Hay, he’s a co-founder, a teacher, a martech advisor, but he’s also a husband, a dog dad, a student, water skiing fanatic, avid runner, a certified financial planner, and a bunch more stuff... Daily infrastructure shows up through repetition, discipline, and choices that protect energy before anything else competes for it. Austin grounds happiness in curiosity, but that curiosity only thrives when supported by sleep, movement, and time that belongs to no employer. Learning stays fun because it is not treated as another performance metric. It remains part of who he is rather than something squeezed into the margins of an already crowded day.Mental and physical health shape his schedule in visible ways. Austin treats them as operating requirements rather than aspirations. His days include a short list of behaviors that carry disproportionate impact:Regular sleep with a consistent bedtime.Exercise that creates physical fatigue and mental quiet.Relationships that exist entirely outside work.Hobbies and games that feel restorative rather than productive.These habits rarely earn praise, which explains why they erode first under pressure. In his twenties, Austin chased work, clients, and money with intensity. He told himself the rest would come later. That promise held eventually, but the gap years carried a cost. He remembers moments of looking in the mirror and feeling uneasy about the life he was assembling, despite checking every external box.Trade-offs now anchor his thinking. Austin frames decisions as equations involving time, energy, and outcomes. Goals demand inputs, and inputs consume limited resources. Avoiding that math leads to exhaustion and resentment. Facing it creates clarity. Many people resist this step because it forces hard choices into daylight. The industry rewards the appearance of doing everything, even when the math never works.“I view a lot of decisions and outcomes in life as trade-offs. At the end of the day, that’s what most things boil down to.”Sleep makes the equation tangible. Austin aims for bed around 9 or 9:30 each night because his mornings require focus, training, and sustained energy. He needs seven and a half hours of sleep to function well. That requirement dictates the rest of the day. Social plans adjust. Work compresses. Goals remain achievable because the system supports them.He defines what he wants to pursue.He calculates the energy required.He locks in non negotiables that keep the math honest.That structure removes constant negotiation with himself. The system holds even when motivation dips or distractions multiply.Key takeaway: Daily infrastructure depends on non negotiables that protect sleep, health, and energy. Clear priorities, visible trade-offs, and repeatable routines create careers that stay durable under pressure.Sundar Swaminathan: Systems That Prevent StressNext up is Sundar Swaminathan, Former Head of Marketing Science at Uber, Author & Host of the experiMENTAL Newsletter & Podcast. He’s also a husband, a father and a well traveled home chef, amateur chess master.Stress prevention sits at the center of Sundar’s daily system for staying happy and effective at work. A concentrated period of personal loss collapsed any illusion that stress deserved patience or tolerance. Three deaths in three weeks compressed time, sharpened perspective, and forced a reassessment of what stress actually costs. Stress drains energy first, then attention, then presence. A career cannot outrun that erosion for long.Control defines the structure of his days. Sundar organizes work and life decisions around what he can actively influence and treats everything else with intentional distance. That discipline reduces noise and preserves energy. The system stays practical because complexity invites self-deception.Work within control receives effort, follow-through, and care.Work outside control receives acknowledgment and release.Outcomes matter, but the quality of effort matters more.Emotional reactions get examined instead of amplified.That repetition builds resilience as a habit rather than a personality trait. Over time, the body learns that urgency does not improve outcomes, while steadiness often does.Long-term thinking provides ballast when short-term chaos shows up. Sundar frames happiness the way experienced investors frame capital. Daily decisions compound quietly. Some weeks produce visible setbacks. The trend still moves when investments stay consistent. He invests daily in relationships, energy, and directionally sound choices. Moving his family to Amsterdam followed that logic. The decision carried friction and uncertainty, yet it expanded daily happiness in ways that cautious planning rarely delivers.“If you keep investing in yourself and the relationships that matter every day, the long-term trend moves up.”Priorities reinforce the system. Sundar grew up with career dominance baked into identity. Family now anchors that identity with clarity. That hierarchy shapes calendars, boundaries, and energy allocation. Work performance benefits from this structure because focus sharpens when limits exist. Activities that drain energy lose priority quickly. Unhappiness spreads fast and contaminates every adjacent part of life.Environment completes the infrastructure. Daily systems matter as much as mindset. Living in a place where flexibility exists without negotiation removes friction before it forms. Parenting logistics do not create anxiety. Time away from work does not require justification. Many expat families notice similar relief because daily life carries less ambient pressure. When systems support people, stress loses room to grow.Key takeaway: Sustainable careers rely on daily infrastructure that prevents stress before it accumulates. Clear control boundaries, long-term thinking, and supportive environments create stability that protects energy and compounds over time.Elena Hassan: Normalizing Str...

What’s up everyone, today we have the pleasure of sitting down with Phyllis Fang, Head of Marketing at Transcend.(00:00) - Intro (01:23) - In This Episode (04:13) - Uber Safety Marketing Shaped A Trust First Marketing Playbook (10:12) - How Permissioned Data Systems Power Personalization at Scale (15:22) - How Consent Infrastructure Improves Personalization Performance (19:20) - How to Audit Consent and Compliance in Marketing Data (23:24) - What Consent Management Does Across AI Data Lifecycles (28:29) - How to Build a Marketing Trust Stack (30:49) - Consent Management as a Revenue Lever (35:10) - Designing Marketing Teams for Freakish Curiosity (41:19) - Skills That Define Great Marketing Operations (45:33) - Why System Level Marketing Experience Builds Career Leverage (50:13) - System for Happiness Summary: Phyllis learned how fragile marketing becomes when systems move faster than trust while working between lifecycle execution and product marketing at Uber. Safety work around emergencies, verification, and COVID forced messages to withstand scrutiny from riders, drivers, regulators, and the public. That experience shapes how she approaches consent and personalization today. Permission signals decide what data moves and how confidently teams can act. When those signals stay connected, work holds. When they drift, confidence erodes across systems, teams, and careers.About PhyllisPhyllis Fang leads marketing at Transcend, where enterprise growth depends on clear choices about data, consent, and accountability. Her work shapes how privacy becomes part of how companies operate, communicate, and earn confidence at scale.Earlier in her career, she spent several years at Uber, working on global product marketing for safety during periods of intense public scrutiny. She helped bring new safety features to market at moments when user behavior, policy decisions, and brand credibility were tightly linked. The work required precision, restraint, and an understanding of how people respond when stakes feel personal.Across roles in e-commerce, lifecycle marketing, and platform strategy, a pattern holds. Fang gravitates toward systems that must work under pressure and messages that must hold up in practice. Her career reflects a belief that marketing earns its place when it reduces uncertainty and helps people move forward with confidence.Uber Safety Marketing Shaped A Trust First Marketing PlaybookTrust-focused marketing depends on people who can move between systems work and narrative work without losing credibility in either space. Phyllis built that fluency by operating inside lifecycle programs while also leading product marketing initiatives at Uber. One side of that work lived in tools, triggers, and delivery logic. The other side lived in rooms where progress depended on persuasion, alignment, and patience. That dual exposure trained her to see how fragile big ideas become when they cannot survive real execution.Lifecycle and marketing operations reward control and repeatability. Product marketing inside a global organization rewards influence and restraint. Phyllis describes moments where moving a single initiative forward required negotiation across regions, channels, and internal politics. Every message faced review from people who owned distribution and reputation in their markets. Messaging tightened quickly because weak logic did not survive long. Campaigns became sharper because every assumption had to hold up under pressure.“We were all in the same company, but I still had to convince people to resource things differently or prioritize a message.”Safety marketing pushed that pressure even further. The work focused on features designed for rare, high-stakes moments, including emergency assistance and large-scale verification during COVID. Measurement shifted away from habitual usage and toward confidence and credibility. The audience expanded well beyond active users. Phyllis had to speak clearly to riders, drivers, regulators, and the general public at the same time. Each group carried different fears, incentives, and consequences. Messaging succeeded only when it respected those differences without creating confusion.That mindset carries directly into her work at Transcend. Privacy and consent buyers often sit in legal or compliance roles where personal and professional risk overlap. These buyers read closely and remember details. Phyllis explains that proof needs to operate on two levels at once. It must withstand careful review, and it must connect to human motivation. Career safety, internal credibility, and long-term reputation shape decisions more than feature depth ever will.“You have to understand the human behind the role, because their motivation usually has very little to do with your product.”Many martech teams still lean on urgency and fear to move deals forward. That habit collapses quickly in trust-driven categories. Buyers trained to manage risk respond to clarity, evidence, and empathy. Marketing teams that understand systems and human cost create messages people can defend internally, even when scrutiny rises.Key takeaway: Trust product marketing works best when teams pair operational rigor with persuasive clarity. Build messages that survive legal review, internal debate, and public scrutiny, then ground those messages in the real career risks your buyer carries. When proof holds at the detail level and the story respects human motivation, credibility compounds instead of eroding under pressure.How Permissioned Data Systems Power PersonalizationPermissioned data systems sit quietly underneath every durable personalization program. Phyllis describes them as the machinery that keeps experiences coherent when traffic spikes, regulations tighten, and teams ship faster than documentation can keep up. When privacy and data infrastructure receive the same attention as creative and lifecycle planning, personalization gains endurance. It stops wobbling every time a new channel, region, or regulation enters the picture.When asked about what a system of permission actually contains, Phyllis anchors the idea in everyday user choice. Preferences, opt-ins, unsubscribes, and topic interests form the marketing layer most teams recognize. Consent records, deletion rights, and data sharing controls form the privacy layer that usually lives elsewhere. Together, these signals decide what data you collect, where it flows, how long it lives, and which systems get to act on it. That layer governs every downstream decision you make about segmentation, targeting, and automation.“We are talking about a layer of user controls that determine what personal data a company collects, how it is collected, how it is stored, how long it is stored, and what gets shared across systems.”Phyllis points out that teams often rush toward tooling before understanding their own surface area. She pushes marketers to start with an audit that feels closer to whiteboarding than compliance. That work cuts across marketing, product, privacy, and partnerships, and it usually exposes uncomfortable overlaps and blind spots. Most organizations already run this exercise for campaigns and funnels, and they rarely include consent in the room. When permission signals stay disconnected from journey design, personalization feels impressive in demos and brittle in production.Operationalizing consent requires discipline across systems. Preference signals need to flow cleanly into the CDP, CRM, messaging platforms, and analytics tools. That way campaigns, audiences, and triggers operate on live, permissioned data ins...

What’s up everyone, today we have the pleasure of sitting down with Jordan Resnick, Senior Director, Marketing Operations at CHEQ.(00:00) - Intro (01:10) - In This Episode (03:47) - Demystifying Go-to-Market Security (06:14) - The Fake Traffic Surge (08:14) - How the Dead Internet Theory Connects to Bot Traffic Growth (12:31) - How to Detect Bot Traffic Through Behavioral Patterns (16:13) - How Go To Market Teams Reduce Fake Traffic And Lead Pollution (30:03) - Preventing Fake Leads From Reaching Sales (34:17) - How to Calculate Revenue Impact of Fake Traffic (38:09) - How to Report Marketing Performance When Bot Traffic Skews Metrics (43:58) - Trust Erosion From Fake Traffic (49:49) - How Marketing Ops Should Adapt Systems for Machine Customers (53:59) - Funnel Audits With Security Teams to Reduce Bot Traffic (57:47) - Detachment as a Career Survival Skill Summary: Distinguishing fake traffic from real machine customers starts where metrics break down. Jordan shows how AI-driven bots now scroll, click, submit forms, and pass validation while quietly filling dashboards with activity that never turns into revenue. The tell is behavioral texture. Sessions move too fast. Paths skip learning. Engagement appears without intent. Real machine customers behave with rhythm and purpose, returning, evaluating, integrating. Teams that recognize the difference lock down the conversion point, block synthetic demand before it reaches core systems, keep sales calendars clean, and only report once traffic has earned trust.About JordanJordan Resnick is Senior Director of Marketing Operations at CHEQ, where he leads the systems, data, and workflows that support go-to-market security across a global customer base. His work sits at the intersection of marketing operations, revenue operations, attribution, automation, and analytics, with a clear focus on building infrastructure that holds up under scale and scrutiny.Before CHEQ, Jordan led marketing operations at Atlassian, where he supported complex GTM motions across multiple business units and global markets. Earlier roles at Perkuto and MERGE combined hands-on execution with customer-facing leadership, integration design, and process ownership. His career also includes more than a decade as an independent operator, delivering marketing operations, automation, content, and technical solutions across a wide range of organizations. Jordan brings a deeply practical, execution-driven perspective shaped by years of building, fixing, and maintaining real systems in production environments.Demystifying Go-to-Market SecurityGo-to-market security shows up when growth metrics look strong and revenue outcomes feel weak. Marketing operations teams live in that gap every day. Jordan describes GTM security as a business-facing discipline that protects the integrity of acquisition, funnel data, and downstream decisions that depend on clean signals. The work sits inside marketing operations because that is where traffic quality, lead flow, and revenue attribution converge.When asked about how GTM security differs from traditional fraud prevention, Jordan frames the difference through decision-making pressure. Security teams historically focus on defending infrastructure and minimizing exposure. Marketing ops teams focus on maintaining momentum while spending real budget. GTM security evaluates risk in context, with an eye toward revenue impact, forecasting accuracy, and operational trust across teams that rely on shared data.Jordan grounds the concept in specific failure points that operators recognize immediately. GTM security examines where bad inputs quietly enter systems and multiply.Paid traffic that inflates sessions without creating buyers.Analytics skewed by automated interactions that look legitimate.Form submissions that pass validation and still never convert.Sales pipelines filled with activity that drains time and morale.Each issue compounds because systems assume the data is real. Teams keep optimizing against numbers that feel precise and still point in the wrong direction.“You are putting money into driving people to your website, and the first question should be how many of those people are real and able to buy.”Invalid traffic behaves like a contaminant. It flows from acquisition into attribution models, forecasting tools, CRMs, and revenue dashboards. Marketing celebrates growth, sales chases shadows, and finance questions confidence in the entire funnel. The problem rarely announces itself as a security incident. It surfaces as confusion, missed targets, and internal friction.GTM security matters because it gives marketing ops teams a framework to protect the inputs that shape every downstream decision. The work runs alongside traditional security while staying anchored in go-to-market outcomes. That way you can spend with confidence, trust your reporting, and hand sales teams signals grounded in real buying behavior.Key takeaway: Treat go-to-market security as part of your core marketing operations workflow. Validate traffic quality, filter lead integrity, and block funnel contamination before data enters analytics and sales systems. That way you can protect budget efficiency, restore confidence in reporting, and align growth decisions with real customer behavior.The Fake Traffic SurgeAI-powered automation now sits at the center of the fake traffic surge, and the data from CHEQ makes that pattern hard to dismiss. The jump from 11.3 percent to 17.9 percent happened because automation became accessible to almost anyone with intent. Writing scripts once required time, skill, and trial and error. AI removes that friction and replaces it with speed and scale, which changes who can participate and how quickly abuse spreads.Jordan ties that accessibility directly to incentives that marketing teams quietly tolerate. Fraud still generates money. Inflated traffic still props up dashboards. Higher visit counts still circulate in board decks without hard questions attached. AI accelerates activity that already existed and widens the group capable of producing it. That combination turns fake traffic into background noise instead of a visible threat, especially when volume metrics continue to earn praise.“You don’t need to be a hardcore coder to write a script anymore. You can get AI to do it for you.”Automation also introduces a layer of ambiguity that most teams are not prepared to handle. Bots now perform legitimate tasks that look suspicious inside analytics tools. Some scan pricing pages. Some analyze product specs. Some gather research for downstream purchasing decisions. Jordan points out that people already configure agents to place orders, and that behavior blends seamlessly into traffic logs. Marketing systems treat those visits the same way they treat fraud, which creates confusion across attribution and forecasting.That confusion pushes teams toward blunt fixes that create new problems. Blanket blocking removes useful signals. Loose filtering leaves waste untouched. Jordan frames the real work as classification rather than suppression. Teams now need to separate intent categories instead of chasing a single definition of fake traffic. That work forces uncomfortable conversations about which metrics deserve trust and which exist only because nobody benefits from challenging them.Fake traffic keeps growing because systems reward volume and rarely penalize distortion. AI makes production easier, incentives keep demand high, and measurement practices lag behind reality. Marketing ops teams that continue to treat traffic as a vanity me...

What’s up everyone, today we have the honor of sitting down with Aleyda Solís, SEO and AI search consultant. (00:00) - Intro (01:17) - In This Episode (04:55) - Crawlability Requirements for AI Search Engines (12:21) - LLMs As A New Search Channel In A Multi Platform Discovery System (18:42) - AI Search Visibility Analysis for SEO Teams (29:17) - Creating Brand Led Informational Content for AI Search (35:51) - Choosing SEO Topics That Drive Brand-Aligned Demand (45:50) - How Topic Level Analysis Shapes AI Search Strategy (50:01) - LLM Search Console Reporting Expectations (52:09) - Why LLM Search Rewards Brands With Real Community Signals (55:12) - Prioritizing Work That Matches Personal Purpose Summary: AI search is rewriting how people find information, and Aleyda explains the shift with clear, practical detail. She has seen AI crawlers blocked without anyone noticing, JavaScript hiding full sections of sites, and brands interpreting results that were never based on complete data. She shows how users now move freely between Google, TikTok, Instagram, and LLMs, which pushes teams to treat discovery as a multi-platform system. She encourages you to verify your AI visibility, publish content rooted in real customer language, and use topic clusters to anchor strategy when prompts scatter. Her closing point is simple. Community chatter now shapes authority, and AI models pay close attention to it.About AleydaAleyda Solís is an international SEO and AI search optimization consultant, speaker, and author who leads Orainti, the boutique consultancy known for solving complex, multi-market SEO challenges. She’s worked with brands across ecommerce, SaaS, and global marketplaces, helping teams rebuild search foundations and scale sustainable organic growth.She also runs three of the industry’s most trusted newsletters; SEOFOMO, MarketingFOMO, and AI Marketers, where she filters the noise into the updates that genuinely matter. Her free roadmaps, LearningSEO.io and LearningAIsearch.com, give marketers a clear, reliable path to building real skills in both SEO and AI search.Crawlability Requirements for AI Search EnginesCrawlability shapes everything that follows in AI search. Aleyda talks about it with the tone of someone who has seen far too many sites fail the basics. AI crawlers behave differently from traditional search engines, and they hit roadblocks that most teams never think about. Hosting rules, CDN settings, and robots files often permit Googlebot but quietly block newer user agents. You can hear the frustration in her voice when she describes audit after audit where AI crawlers never reach critical sections of a site."You need to allow AI crawlers to access your content. The rules you set might need to be different depending on your context."AI crawlers also refuse to process JavaScript. They ingest raw markup and move on. Sites that lean heavily on client-side rendering lose entire menus, product details, pricing tables, and conversion paths. Aleyda describes this as a structural issue that forces marketers to confront their technical debt. Many teams have spent years building front-ends with layers of JavaScript because Google eventually figured out how to handle it. AI crawlers skip that entire pipeline. Simpler pages load faster, reveal hierarchy immediately, and give AI models a complete picture without extra processing.Search behavior adds new pressure. Aleyda points to OpenAI’s published research showing a rise in task-oriented queries. Users ask models to complete goals directly and skip the page-by-page exploration we grew up optimizing for. You need clarity about which tasks intersect with your offerings. You need to build content that satisfies those tasks without guessing blindly. Aleyda urges teams to validate this with real user understanding because generic keyword tools cannot describe these new behaviors accurately.Authority signals shift too. Mentions across credible communities carry weight inside AI summaries. Aleyda explains it as a natural extension of digital PR. Forums, newsletters, podcasts, social communities, and industry roundups form a reputation map that AI crawlers use as context. Backlinks still matter, but mentions create presence in a wider set of conversations. Strong SEO programs already invest in this work, but many teams still chase link volume while ignoring the broader network of references that shape brand perception.Measurement evolves alongside all of this. Aleyda encourages operators to treat AI search as both a performance channel and a visibility channel. You track presence inside responses. You track sentiment and frequency. You monitor competitors that appear beside you or ahead of you. You map how often your brand appears in summaries that influence purchase decisions. Rankings and click curves do not capture the full picture. A broader measurement model captures what these new systems actually distribute.Key takeaway: Build crawlability for AI search with intention. Confirm that AI crawlers can access your content, and remove JavaScript barriers that hide essential information. Map the task-driven behaviors that align with your products so you invest in content that meets real user goals. Strengthen your reputation footprint by earning mentions in communities that influence AI summaries. Expand your measurement model so you can track visibility, sentiment, and placement inside AI-generated results. That way you can compete in a search environment shaped by new rules and new signals.LLMs As A New Search Channel In A Multi Platform Discovery SystemSEO keeps getting declared dead every time Google ships a new interface, yet actual search behavior keeps spreading across more surfaces. Aleyda reacted to the “LLMs as a new channel” framing with immediate agreement because she sees teams wrestling with a bigger shift. They still treat Google as the only gatekeeper, even though users now ask questions, compare products, and verify credibility across several platforms at once. LLMs, TikTok, Instagram, and traditional search engines all function as parallel discovery layers, and the companies that hesitate to accept this trend end up confused about where SEO fits.Aleyda pointed to the industry’s long dependence on Google and described how that dependence shaped expectations. Many teams built an entire worldview around a single SERP format, a single set of ranking factors, and a single customer entry point. Interface changes feel existential because the discipline was defined too narrowly for too long. She sees this tension inside consulting projects when stakeholders ask whether SEO is dying instead of asking where their audience now searches for answers.Retail clients provided her clearest examples. They already treat TikTok and Instagram as core search environments. They ask for guidance on how to structure content so it gets discovered through platform specific signals. They ask for clarity on how product intent gets inferred through tags, comments, watch time, and creator interactions. Their questions treat search as a distributed system, and their behavior hints at what the wider market will adopt. Aleyda considers this a preview, because younger customers rarely begin their journey inside a traditional search engine.Her story from a conference in China made the point even sharper. She explained how Baidu no longer carries the gravitational pull many Western marketers assume. People gather information through Red Note, Douyin, and several specialized platforms, and they assemble answers through a blend of formats. That experience changed Aleyda’s expectations for Western markets. She believes...

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 People Summary: 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 pace that competitors cannot easily match.Scott’s message carries emotional weight. He does not downplay the exhaustion in the field, but he reinforces that reinvention often happens inside the work, not outside of it. People who move toward new capabilities build careers that feel less fragile and more future proof.Key takeaway: Mid career marketers build real leverage by running small AI experiments inside their current roles, documenting the results, and using those learnings to influence how their companies adapt. Start with narrow tests that affect your daily work, share clear outcomes with your team, and repeat the cycle. That way you can build rare credibility and position yourself as the person who accelerates organizational change.If You Started Over in Martech, What Would You Learn FirstCross functional fluency shapes careers in a way that shiny frameworks never will, and Scott calls this out with blunt honesty. He shares how his early career lived in two worlds, writing brittle code on one side and trying to understand marketers on the other. He laughs about being a “very mediocre software engineer” who built things that probably should not have survived contact with production. That imperfect background still gave him an edge, because technical fluency mixed with genuine curiosity about marketing created a role no one else was filling. He could explain system behavior in a language marketers understood, and he could explain marketer behavior in a language engineers tolerated. That unusual pairing delivered force inside teams that usually worked in isolation.Scott makes the case that readers can build similar momentum by leaning into roles where disciplines collide. He argues that the most useful skills often come from pairing two domains and learning how they influence each other. He highlights combinations like:Marketing and IT for people who enjoy systems.Marketing and finance for people drawn to modeling and forecasting.Marketing and sales for people who want to connect customer signals with revenue conversations.He believes these intersections are crowded with opportunity because organizations rarely invest enough in communication across teams. You can create real leverage when you speak multiple operational languages with confidence.“The ability to serve as a bridge of cross pollinating between multiple disciplines has a lot of opportunity.”Scott also shares the part he would invest in first if he were twenty two again. He spent years focusing almost entirely on what systems could do. He cared deeply about architecture diagrams and technical possibility, and he assumed people would adopt anything that worked. He later realized that adoption follows trust,...

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 Satisfaction Summary: 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, run experiments responsibly, and adjust budgets quickly. He wants measurement to operate in the background while marketers focus on creative and channel strategies with confidence that the numbers can keep up with the pace of execution.Key takeaway: Run experiments where they matter most by combining the biggest budgets with the widest uncertainty. Use triangulated signals like MMM bounds, lift tests, and creative engagement to identify channels that deserve deeper testing. Give regional teams embedded data scientists so they can respond to real conditions without waiting for central approval queues. Build light guardrails, not heavy process, so experimentation strengthens day to day marketing decisions with speed and confidence.What Happened When Canva Turned Off Branded SearchGeographic holdout tests gave Matt a practical way to challenge long-standing spend patterns at Canva without turning measurement into a philosophical debate. He described how many new team members arrived from environments shaped by attribution dashboards, and he needed something concrete that demonstrated why experiments belong in the measurement toolkit. Experiments produced clearer decisions because they created evidence that anyone could understand, which helped the organization expand its comfort with more advanced measurement methods.The turning point started with a direct question from Canva’s CEO. She wanted to understand why the company kept investing heavily in bidding on the keyword “Canva,” even though the brand was already dominant in organic search. The company had global awareness, strong default rankings, and a product that people searched for by name. Attribution platforms treated branded search as a powerhouse channel because those clicks converted at extremely high rates. Matt knew attribution would reinforce the spend by design, so he recommended a controlled experiment that tested actual incrementality."We just turned it off or down in a couple of regions and watched what happened."The team created several regional holdouts across the United States. They reduced bids in those regions, monitored downstream behavior, and let natural demand play out. The performance barely moved. Growth held steady and revenue held steady. The spend did not create additional value at the level the dashboards suggested. High intent users continued converting, which showed how easily attribution can exaggerate impact when a channel serves people who already made their decision.The outcome saved Canva millions of dollars, and the savings were immediately reallocated to areas with better leverage. The win carried emotional weight inside the company because it replaced speculati...

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 Leadership Summary: 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 decision cycle. Tooling is evolving at a pace that makes six month old assumptions feel stale. She wants teams to revisit build versus buy questions frequently and to treat modern tools as a prompt to redraw boundaries rather than defend old ones. She frames it as an ongoing practice rather than a one time decision.Key takeaway: Reassess your build versus buy decisions every quarter by measuring two factors. First, identify whether the workflow strengthens a core competency that deserves internal ownership. Second, gauge the domain complexity and decide whether the function needs constant tuning or specialized expertise. Use modern integration layers, workflow builders, and direct LLM access to assemble internal systems quickly. Build the pieces that reinforce your strengths, buy the pieces that demand specialized depth, and share internal workflows so other teams can expand your progress.Why In House AI Provides Better Economics And ControlAI tooling has grown into a marketplace crowded with vendors who promise intelligence, automation, and instant transformation. Anna watches teams fall into these patterns with surprising ease. Many of the tools on the market run the same public models under new branding, yet buyers often assume they are purchasing deeply specialized systems trained on inaccessible data. She laughs about driving down the 101 and seeing AI billboards every few minutes, each one selling a glossy shortcut to operational excellence. The overcrowding makes teams feel like they should buy something simply because everyone else is buying something, and that instinct shifts AI procurement from a strategic decision into a reflex."A one year agreement might as well be a decade in AI right now."Anna has seen how annual vendor contracts slow companies down. The moment a team commits to a year long agreement, the urgency to evaluate alternatives vanishes. They adopt a “set it and forget it” mindset because the tool is already purchased, the budget is already allocated, and the contract already sits in legal. AI development moves fast. Contract cycles do not. That mismatch creates friction that becomes expensive, especially when new models launch every few weeks and outperform the ones you purchased only months earlier. Teams do not always notice the cost of stagnation because it creeps in quietly.Anna lays out a practical build versus buy framework. Teams should inspect whether the capability touches their core competency, their customer experience, or their strategic distinctiveness. If it does, then in house AI provides more long term value. It lets the company shape the model around real customer patterns. It keeps experimentation in motion instead...

What’s up everyone, today we have the pleasure of sitting down with Pam Boiros, Fractional CMO and Marketing advisor, and Co-Founder Women Applying AI.(00:00) - Intro (01:13) - In This Episode (03:49) - How To Audit Data Fingerprints For AI Bias In Marketing (07:39) - Why Emotional Intelligence Improves AI Prompting Quality (10:14) - Why So Many Women Hesitate (15:40) - Why Collaborative AI Practice Builds Confidence In Marketing Ops Teams (18:31) - How to Go From AI Curious to AI Confident (24:32) - Joining The 'Women Applying AI' Community (27:18) - Other Ways to Support Women in AI (28:06) - Role Models and Visibility (32:55) - Leadership’s Role in Inclusion (35:57) - Mentorship for the AI Era (38:15) - Why Story Driven Communities Strengthen AI Adoption for Women (42:17) - AI’s Role in Women’s Worklife Harmony (45:22) - Why Personal History Strengthens Creative Leadership Summary: Pam delivers a clear, grounded look at how women learn and lead with AI, moving from biased datasets to late-night practice sessions inside Women Applying AI. She brings sharp examples from real teams, highlights the quiet builders shaping change, and roots her perspective in the resilience she learned from the women in her own family. If you want a straightforward view of what practical, human-centered AI adoption actually looks like, this episode is worth your time.About PamPam Boiros is a consultant who helps marketing teams find direction and build plans that feel doable. She leads Marketing AI Jump Start and works as a fractional CMO for clients like Reclaim Health, giving teams practical ways to bring AI into their day-to-day work. She’s also a founding member of Women Applying AI, a new community that launched in Sep 2025 that creates a supportive space for women to learn AI together and grow their confidence in the field.Earlier in her career, Pam spent 12 years at a fast-growing startup that Skillsoft later acquired, then stepped into senior marketing and product leadership there for another three and a half years. That blend of startup pace and enterprise structure shapes how she guides her clients today.How To Audit Data Fingerprints For AI Bias In MarketingAI bias spreads quietly in marketing systems, and Pam treats it as a pattern problem rather than a mistake problem. She explains that models repeat whatever they have inherited from the data, and that repetition creates signals that look normal on the surface. Many teams read those signals as truth because the outputs feel familiar. Pam has watched marketing groups make confident decisions on top of datasets they never examined, and she believes this is how invisible bias gains momentum long before anyone sees the consequences.Pam describes every dataset as carrying a fingerprint. She studies that fingerprint by zooming into the structure, the gaps, and the repetition. She looks for missing groups, inflated representation, and subtle distortions baked into the source. She builds this into her workflow because she has seen how quickly a model amplifies the same dominant voices that shaped the data. She brings up real scenarios from her own career where women were labeled as edge cases in models even though they represented half the customer base. These patterns shape everything from product recommendations to retention scores, and she believes many teams never notice because the numbers look clean and objective."Every dataset has a fingerprint. You cannot see it at first glance, but it becomes obvious once you look for who is overrepresented, who is underrepresented, or who is misrepresented."Pam organizes her process into three cycles that marketers can use immediately.The habit works because it forces scrutiny at every stage, not just at kickoff.Before building, trace the data source, the people represented, and the people missing.While building, stress test the system across groups that usually sit at the margins.After launch, monitor outputs with the same rhythm you use for performance analysis.She treats these cycles as an operational discipline. She compares the scale of bias to a compounding effect, since one flawed assumption can multiply into hundreds of outputs within hours. She has seen pressure to ship faster push teams into trusting defaults, which creates the illusion of objectivity even when the system leans heavily toward one group’s behavior. She wants marketers to recognize that AI audits function like quality control, and she encourages them to build review rituals that continue as the model learns. She believes this daily maintenance protects teams from subtle drift where the model gradually leans toward the patterns it already prefers.Pam views long term monitoring as the part that matters most. She knows how fast AI systems evolve once real customers interact with them. Bias shifts as new data enters the mix. Entire segments disappear because the model interprets their silence as disengagement. Other segments dominate because they participate more often, which reinforces the skew. Pam advocates for ongoing alerts, periodic evaluations, and cross-functional reviews that bring different perspectives into the monitoring loop. She believes that consistent visibility keeps the model grounded in the full customer base.Key takeaway: You can reduce AI bias by treating audits as part of your standard workflow. Trace the origin of every dataset so you understand who shapes the patterns. Stress test during development so you catch distortions early. Monitor outcomes after launch so you can identify drift before it influences targeting, scoring, and personalization. This rhythm gives you a reliable way to detect biased fingerprints, keep systems accountable, and protect real customers from skewed automation.Why Emotional Intelligence Improves AI Prompting QualityEmotional intelligence shapes how people brief AI, and Pam focuses on the practical details behind that pattern. She sees prompting as a form of direction setting, similar to guiding a creative partner who follows every instruction literally. Women often add richer context because they instinctively think through tone, audience, and subtle cues before giving direction. That depth produces output that carries more human texture and brand alignment, and it reduces the amount of rewriting teams usually do when prompts feel thin.Pam also talks about synthetic empathy and how easily teams misread it. AI can generate warm language, but users often sense a hollow quality once they reread the output. She has seen teams trust the first fluent result because it looks polished on the surface. People with stronger emotional intelligence detect when the writing lacks genuine feeling or when it leans on clichés instead of real understanding. Pam notices this most in content meant for sensitive moments, such as apology emails or customer care messages, where the emotional miss becomes obvious."Prompting is basically briefing the AI, and women are natural context givers. We think about tone and audience and nuance, and that is what makes AI output more human and more aligned with the brand."Pam brings even sharper clarity when she moves into analytics. She observes that many marketers chase the top performer without questioning who drove the behavior. She describes moments where curiosity leads someone to discover that a small, highly engaged audience segment pulled the numbers upward. She sees women interrogating patterns by asking:Who showed upWhy they behaved the way they didWhat made the pattern appear more universal than it isThose questions shift analytics from scoreboar...

What’s up everyone, today we have the pleasure of sitting down with Anna Leary, Director of Marketing Operations at Alma.(00:00) - Intro (01:15) - In This Episode (04:38) - How to Prevent Burnout (05:46) - What Companies Can Do Better (07:50) - Agility of Planning (08:53) - Why Saying No Strengthens Marketing Operations (13:48) - How to Decide When to Push Back (18:03) - Hill To Die On (20:03) - How to Handle Constant Pushback (29:55) - Wishlist (37:06) - How to Use Asynchronous Communication to Reduce Stress (44:24) - How To Evaluate Martech Tools Based On Real Business Impact (48:45) - Why Marketing Ops Needs Visible Work Systems (51:24) - Health Awareness (52:56) - How to Recognize and Prevent Burnout in Marketing Operations Summary: Anna built systems to keep marketing running smoothly, but the real lesson came when those same systems failed to protect her. In this episode, she shares how saying no became her survival skill, why visibility is the antidote to burnout, and how calm structure (not constant hustle) keeps teams sharp and human. It’s a story about boundaries, balance, and learning to lead without losing yourself.About AlmaAnna Leary is the Director of Marketing Operations at Alma, where she builds scalable systems that help marketing teams work smarter. With a focus on lead flow, data architecture, and enablement, she’s known for creating centers of excellence that turn fragmented operations into cohesive, measurable programs. As a Marketo Certified Solutions Architect and Marketo Measure (Bizible) specialist, Anna brings a rare balance of technical depth and strategic clarity to every initiative she leads.Before joining Alma, Anna spent more than a decade shaping marketing operations strategies for brands like Uber, Teamwork, Sauce Labs, and Bitly. Whether optimizing attribution models or training teams to adopt new workflows, Anna’s work always centers on efficiency, empowerment, and driving impact across the full marketing ecosystem.Burnout and BalanceMarketing ops work demands constant precision. Teams juggle system integrations, data cleanups, and new tech rollouts, often all before lunch. The job requires mental endurance and a tolerance for chaos. Anna understands this well. “Everyone’s trying to be the person who knows the newest tech,” she said. “It’s hard to keep up, and that adds to the mental load.” The competition to stay relevant has turned into a quiet stress test that too many operators fail without noticing.The strange part is that ops teams often create systems designed to protect their organizations but rarely use those same systems to protect themselves. Anna explained how Service Level Agreements (SLAs) can lose their meaning when teams treat them as flexible. Urgent requests push through, exceptions pile up, and structure dissolves. Each “quick favor” chips away at the purpose of having defined processes. She put it plainly:“If we’re making an exception for everything, then we’re not respecting the process.”When teams stop respecting their own boundaries, burnout follows quickly. SLAs exist to create stability, and stability is what keeps people sane. Following process is not bureaucracy; it is protection. It gives operators time to think clearly, plan ahead, and make fewer reactive decisions. That way you can build predictability into your week instead of letting other people’s emergencies define it.Anna also shared how her team reworked its entire planning system to reduce stress. “We used to do quarterly capacity planning,” she said, “but half the projects fell apart by week four.” She scrapped the process and replaced it with smaller, rolling cycles that fit the unpredictable nature of marketing requests. For someone who identifies as Type A, letting go of that much structure felt risky, but the tradeoff was worth it. Her team now works with more energy, less anxiety, and a better sense of control.“Giving up some of that control is actually good in the end because it’s less stressful.”Her story shows how burnout prevention depends on structure that adapts. Ops professionals do their best work when their systems reflect real life, not an idealized version of it. Boundaries, planning, and discipline should support humans, not box them in.Key takeaway: Protect your team’s mental health by enforcing the systems you build. Treat SLAs as promises, not preferences. Review your planning cycles regularly and adjust them to match the actual pace of work. Stability in ops comes from building rules that people respect and structures that evolve as the business changes.The Power of NoSaying no is one of the hardest and most necessary skills in marketing operations. Every week brings a new request, a “quick fix,” or a last-minute idea that someone swears will only take five minutes. Anna treats these moments as boundary checks. They test whether her team can protect their focus without losing trust or influence across the company.“Boundaries in your personal life mirror boundaries in your professional life. You can’t sustain either without learning to say no.”Anna connects this discipline to mental health. After years of therapy, she learned that setting boundaries preserves energy and prevents resentment from creeping into work. In marketing ops, that means understanding when to say no and why. A no can be temporary, like “no for now,” or conditional, like “come back once X, Y, and Z are ready.” That clarity gives teams space to plan properly instead of reacting in chaos.Too many ops teams still act like order-takers. They manage tickets, fix errors, and scramble to meet every demand, even when requests come without context. Anna believes teams must reposition themselves as strategic partners. That means asking sharper questions such as, “How does this connect to our business goals?” or “Which KPI does this move?” Every yes should come with evidence, not obligation. When ops speaks in the language of impact, their boundaries start to hold.To back that up, Anna recommends showing the work already in motion. Pull up your team’s Notion or Asana board, point to the commitments everyone approved, and remind stakeholders that priorities are already locked for this sprint. That way you can shift the conversation from emotion to logic. Plans exist for a reason. If the company wants to keep changing direction, it must accept the cost of constant interruption.Anna’s approach creates psychological safety for her team. She recently told a contractor to stop overthinking a request that was technically impossible. Her words were simple: “It’s okay to tell them we can’t do this.” Those six words carried permission to rest, to stop chasing unrealistic expectations, and to respect the limits of their tools and time. Teams that learn this kind of confidence avoid burnout and deliver better results with less noise.Key takeaway: Boundaries are an operational discipline, not an act of defiance. Use clear priorities, visible sprint boards, and company KPIs as your guardrails. Frame every no around impact and alignment. That way you can protect focus, maintain trust with stakeholders, and keep your team mentally healthy while still driving the business forward.Hiring Experts Only to Tell Them What to DoEvery marketing ops professional eventually faces a request that makes their skin crawl. For Anna, it was the “no-reply” email debate. A stakeholder wanted to send a campaign from a no-reply address in Marketo. She had explained countless times why that idea goes against every principle of customer experience. It blocks responses, damages trust, and kills engagemen...

What’s up everyone today we have the pleasure of chatting with Blair Bendel, Senior Vice President of Marketing at Foxwoods Resort Casino.(00:00) - Intro (00:49) - In This Episode (03:39) - Evolution of Casino Martech (06:11) - Customer Loyalty & Personalization (09:36) - Using the Right Marketing Channel for the Right Goal in Hospitality (12:38) - Foxwood’s Martech and Customer Data Migration to MoEngage (15:05) - Picking MoEngage (20:07) - Why Change Tools?? (22:46) - Implementing a New Platform (24:58) - Building Structure for 24/7/365 Casino Marketing (31:20) - Key Things to Track (33:15) - Fail Fast, Learn Faster (37:25) - Balancing Big Data with Privacy (40:23) - Why AI Will Not Fix Casino Marketing Overnight (43:23) - Exploring AI (46:59) - Human Experience Drives Long-Term Casino Revenue (49:05) - Human Side (52:12) - Why Face-to-Face Conversations Strengthen Marketing Teams Summary: The casino floor never sleeps. Lights hum, cards shuffle, and people come not just to gamble but to feel alive. While other industries went digital overnight, casinos stayed grounded in human moments, and Blair’s mission has been to connect those experiences through smarter tech. At Foxwoods, he replaced a maze of disconnected martech with a single platform, giving his team one clear view of every guest. Push messages became quick nudges, emails carried depth, and silence built trust. In a business that runs 24/7/365, his team moves fast, learns constantly, and protects what matters most: guest privacy. About BlairBlair Bendel has spent nearly two decades shaping brands that make casinos feel alive. As SVP of Marketing at Foxwoods Resort Casino, one of the world’s largest gaming and entertainment destinations, he leads strategy across brand, digital, loyalty, and guest experience for a property owned by the Mashantucket Pequot Tribal Nation.Before Foxwoods, Blair drove marketing for Boyd Gaming and Pinnacle Entertainment, guiding multi-property teams through high-stakes launches and rebrands. Known for blending data and instinct, he’s built campaigns that turn foot traffic into fandom and moments into measurable growth.The Evolution of Casino MartechCasinos thrive on the energy of real people in real spaces. Blair has spent his career in that environment, where the hum of slot machines and the rhythm of foot traffic define success. He points out that while other industries rushed to digitize, gaming and hospitality focused on the on-property experience that drives most of their revenue. Technology in this world serves the guest standing in front of you, not a distant audience online.“There’s a lot of innovation, but it’s all centered around that customer and that on-property experience,” Blair said.Walk across a modern casino floor and you see how far that innovation has gone. Slot machines now reach twelve feet high, lit by curved screens that feel more like immersive art installations than games. Even bingo, once a paper-and-pen ritual, lives on tablets. These changes reflect more than aesthetic upgrades. They mark the blending of digital personalization with in-person entertainment. Each new machine and experience collects data, interprets patterns, and helps casinos understand what keeps players coming back.Blair sees the next phase of progress in the pairing of martech systems and artificial intelligence. Casinos have long collected data on player habits, but much of it stayed locked in isolated databases. AI now connects those dots, linking preferences, visit frequency, and loyalty activity into one living profile. That way you can predict what a guest wants before they ask for it. Personalized dining offers, targeted game promotions, or well-timed follow-up messages all become part of a continuous loop that strengthens engagement.Still, Blair focuses on the human side of this transformation. “People assume tech makes everything easier, and it doesn’t,” he said. Each new platform requires training, integration, and trust. Martech without people who know how to use it becomes clutter. Blair spends much of his time ensuring his team understands the technology deeply enough to keep the guest experience effortless. The strategy depends on teams who can think like data analysts and act like hosts.Key takeaway: Martech and AI can elevate on-property hospitality when used to deepen human connection instead of replacing it. Integrate systems that unify guest data, but prioritize training and comfort among your team. When your people trust the tools and your guests feel known, technology quietly fades into the background while loyalty takes center stage.Customer Loyalty and Personalization in Casino MarketingCasino marketing has operated on autopilot for too long. Guests still get dropped into massive segmentation buckets, treated as if their weekend habits, entertainment tastes, and spending patterns are interchangeable. Blair describes it bluntly: “We still send show offers to guests who’ve never been to a concert in their life.” That single sentence captures the outdated logic behind much of hospitality marketing. The data is there, but the systems fail to translate it into actual relevance.Blair’s vision for Foxwoods looks very different. He wants every guest communication to reflect an individual’s real-world behavior across the property. The system should recognize the guest who booked a John Legend concert last year, scheduled a spa visit before dinner at the steakhouse, and played slots into the night. That pattern should generate communications that align with their habits instead of contradicting them. The goal is not another loyalty campaign; it is a personalized experience that extends far beyond the walls of the casino.“Pre-booking, post-booking, everything in between should feel connected and meaningful,” Blair says. “It should never just be noise.”The complexity behind that ambition is immense. Each behavioral variable—favorite artist, time of year, dining preference, game type—multiplies the possible outcomes. A small addition in logic can create thousands of potential message combinations. Casinos also face stricter rules on data sensitivity than most industries, so scaling personalization demands precision. The technical lift is enormous, but the payoff is real: when every offer feels relevant, engagement increases without resorting to gimmicks or discounts.The most important shift is cultural, not technological. Marketing teams need to stop thinking of messages as promotions and start thinking of them as part of the guest experience. When personalization is treated as hospitality, not marketing automation, it starts to feel natural. That mindset transforms every text, push notification, and offer into something that extends the stay rather than interrupts it.Key takeaway: One-to-one personalization in casino marketing depends on operational discipline, unified data, and a mindset shift. Start by mapping how guests actually experience your property, then use that data to inform relevant communication across every channel. That way you can replace noise with value, and marketing becomes an extension of the hospitality experience itself.Using the Right Marketing Channel for the Right Goal in HospitalityCoordinating multiple marketing systems inside a casino is like running a live concert with half the band still tuning. Each channel (email, mobile, social, in-property signage) operates on a separate timeline, using different data and often speaking a different language. Blair knows this chaos well. His goal is to make those systems play in harmony, producing a s...

What’s up folks, today we have the pleasure of sitting down with Megan Kwon, Director, Digital Customer Communications at Loblaw Digital.(00:00) - Intro (01:26) - In This Episode (04:11) - Building a Career Around Conversations That Scale (06:25) - Customer Journey Pods and Martech Team Structures (09:08) - Martech Team Structures (11:23) - Customer Journey Martech Pods (12:54) - How to Assign Martech Tool Ownership and Drive Real Adoption (14:54) - Martech Training and Onboarding (17:30) - How To Integrate New Martech Into Daily Habits (19:59) - Why Change Champions Work in Martech Transformation (24:11) - Change Champion Example (28:25) - How To Manage Transactional Messaging Across Multiple Brands (32:35) - Frequency and Recency Capping (35:59) - Why Shared Ownership Improves Transactional Messaging (41:50) - Why Human Governance Still Matters in AI Messaging (47:11) - Why Curiosity Matters in Adapting to AI (53:08) - Creating Sustainable Energy in Marketing Leadership Summary: Megan leads digital customer communications at Loblaw Digital, turning enterprise-scale messaging into something that feels personal. She built her teams around the customer journey, giving each pod full creative and data ownership. The people driving results also own the tools, learning by building and celebrating small wins. Her “change champions” make new ideas stick, and her view on AI is grounded; use it to go faster, not think for you. Curiosity, she says, is what keeps marketing human.About MeganMegan Kwon runs digital customer communications at Loblaw Digital, the team behind how millions of Canadians hear from brands like Loblaws, Shoppers Drug Mart, and President’s Choice. She’s part strategist, part systems thinker, and fully obsessed with how data can make marketing feel more human, not less.Before returning to Loblaw, Megan helped reshape how people discover and trust local marketplaces at Kijiji, and before that, she built growth engines in the fintech world at NorthOne. Her career has been a study in scale; from scrappy e-commerce tests to national lifecycle programs that touch nearly every Canadian household. What sets her apart is the way she leads: with deep curiosity, radical ownership, and a bias for collaboration. She believes numbers tell stories, and that the best marketing teams build movements around insight, empathy, and accountability.Building a Career Around Conversations That ScaleRunning digital messaging at Loblaw means coordinating communication at a scale that few marketers ever experience. Megan oversees the systems that deliver millions of emails and texts across brands Canadians interact with daily, including Loblaws, Shoppers Drug Mart, and President’s Choice. Her team manages both marketing and transactional messages, making sure each one aligns with a specific stage in the customer journey. The workload is immense. Each division has its own priorities, and every campaign needs to fit within a shared infrastructure that still feels personal to the customer.“We work with a lot of different business divisions across the entire organization. Our job is to make sure their strategies and programs come to life through the customer lifecycle.”Megan’s team operates more like a connective tissue than a broadcast engine. They bridge the gaps between marketing, product, and data teams, translating disconnected strategies into a unified experience. That work involves building systems capable of:Managing multiple brand voices while keeping messaging consistentTriggering real-time communications that respond to customer behaviorIntegrating old and new technologies without breaking operational flowEvery campaign becomes part of a continuous conversation with the customer. Each message is one step in a long dialogue, not a one-off announcement.Megan’s perspective comes from experience earned in very different industries. She began her career at Loblaw during the early days of online grocery, a time when digital operations were experimental and resourceful. She later worked across fintech, marketplaces, and paid media before returning to Loblaw. That journey helped her understand every layer of the customer funnel, from acquisition through retention. It also taught her how to combine growth marketing tactics with enterprise-level communication systems, that way she can scale personalization without losing humanity.Most large organizations still treat messaging as a collection of isolated programs. Megan treats it as an ecosystem. Her work shows that when lifecycle and acquisition efforts operate within a shared framework, communication becomes more coherent and far more effective. Alignment between data, channels, and teams reduces noise and builds trust with customers who engage across multiple brands.Key takeaway: Building a unified messaging ecosystem starts with structure, not volume. Create systems that connect channels, data, and brand voices into one coordinated experience. Treat messaging as a relationship that continues long after the first conversion. That way you can make enterprise-scale communication feel personal, intentional, and consistent across every touchpoint.Customer Journey Pods and Martech Team StructuresRunning digital communications at Loblaw means managing one of the largest customer ecosystems in the country. The team sends millions of messages across grocery, pharmacy, and e-commerce brands every week. Each interaction has to feel personal, relevant, and timely, even when it comes from a massive organization. Megan explains that the only way to handle that kind of scale is to treat data as the operating system and collaboration as the backbone.Her team relies on analytics to shape every message. Real-time signals from dozens of digital properties guide what customers see, when they see it, and how those experiences evolve. It is a constant feedback loop between behavior and communication. “We lean a lot into the data that we gather,” Megan says. “That pretty much drives almost everything that we do.” The systems are only half the story, though. The other half is how her team stays connected across offices, divisions, and projects. They share knowledge in Coda, manage progress in Jira, and rely on Slack to keep conversations fluid. Even their emojis have purpose, creating a shared language that makes collaboration faster and more human.“Everything that we do, we share that knowledge back and forth so that we can continue to learn off each other,” Megan said.The team structure used to follow the company’s business units. Each division had its own specialists who acted like small internal agencies. It worked for speed, but it made collaboration harder. Megan reorganized everything around the customer journey instead. Her teams now work in “pods” that align with stages such as onboarding, discovery, shopping, and post-purchase. Each pod has both data and creative ownership over its domain. That way, a single team can experiment, learn, and apply what works across multiple brands.Megan also built intentional overlap between pods to keep ideas moving. For example, the loyalty and early engagement pod owns both new-member activation and retention. That connection helps them understand the full customer arc, from first purchase to repeat visits. The result is a flexible structure that shares expertise fluidly without losing focus. Large enterprises tend to slow down under their own weight, but this model keeps Loblaw’s marketing engine fast, synchronized, and grounded in customer behavior.The work Megan’s team does might look complex from the out...

What’s up everyone today we have the pleasure of sitting down with Jane Menyo, Sr. Director, Solutions & Customer Marketing @ Gong.(00:00) - Jane-audio (01:01) - In This Episode (04:43) - How Solutions Marketing Turns Customer Insights Into Strategy (09:22) - Using AI to Mine Real Customer Intelligence from Conversations (13:18) - Why Stitching Research Sequences Works in Customer Marketing (17:09) - Using AI Trackers to Uncover Buyer Behavior in Sales Conversations (23:21) - How Standardized Prompts Improve Sales Enablement Systems (29:43) - Building Messaging Systems That Scale Across Industries (34:15) - How Gong’s Research Assistant Slack Bot Delivers Instant Customer Proof (38:26) - Avoiding Mediocre AI Marketing Research (43:42) - Why Customer Proof Outperforms AI-Generated Marketing (45:41) - Why Rest Strengthens Creative Output in Marketing Summary: Jane built her marketing practice around listening. At Gong, she turned raw customer conversations into a live feedback system that connects sales calls, product strategy, and messaging in real time. Her team uses AI to surface patterns from the field and feed them back into content that actually reflects how people buy. She runs on curiosity and recovery, finding her best ideas mid-run. In a world obsessed with producing more, Jane’s work reminds marketers to listen better. The smartest strategies start in the quiet moments when someone finally hears what the customer’s been saying all along.About JaneJane Menyo leads Solutions and Customer Marketing at Gong, where she’s known for fusing strategy with storytelling to turn customers into true advocates. She built Gong’s customer marketing engine from the ground up, scaling programs that drive adoption, retention, and community impact across the company’s revenue intelligence ecosystem.Before Gong, Jane led customer and solutions marketing at ON24, where she developed go-to-market playbooks and launched large-scale advocacy initiatives that connected customer voice to product innovation. Earlier in her career, she helped shape demand generation and brand strategy at Comprehend Systems (a Y Combinator and Sequoia-backed life sciences startup) laying the operational groundwork that fueled growth.A former NCAA All-American and U.S. Olympic Trials contender, Jane brings a rare blend of discipline, creativity, and competitive energy to her leadership. Her approach to marketing is grounded in empathy and powered by data; a balance that turns customer stories into growth engines.How Solutions Marketing Turns Customer Insights Into StrategyJane’s role at Gong evolved from building customer advocacy programs to leading both customer and solutions marketing. What began as storytelling and adoption work expanded into shaping how Gong positions its products for different personas and industries. The shift moved her from celebrating customer wins to architecting how those wins inform the company’s broader go-to-market strategy.Persona marketing only works when it goes beyond demographics and titles. Jane treats it as an operational system that connects customer understanding with product truth. Her team studies how real people use Gong, where they get stuck, what outcomes they care about, and how their teams actually make buying decisions. Those details guide every message Gong sends into the market. It is a constant feedback loop that keeps the company close to how customers think and work.Her solutions marketing team functions like a mirror to product marketing. Product marketers focus on what the product can do, while Jane’s team translates that into why it matters to specific audiences. They do not write from feature lists. They write from the field. When a sales manager spends half her day in Gong but still struggles to coach reps efficiently, Jane’s team crafts stories and materials that speak directly to that pain. The goal is to make every communication feel like it was written from inside the customer’s daily workflow.“Our work is about meeting customers where they are and helping them get to outcomes faster,” Jane said.That perspective only works when every team in the company has equal access to the customer’s voice. Gong’s own technology makes that possible. Conversations, feedback, and usage patterns are captured and shared automatically, so customer knowledge is no longer limited to those on the front lines. Jane’s group uses that visibility to deepen persona profiles, test new positioning, and identify emerging trends before they reach scale. It makes the company more responsive and keeps messaging grounded in real behavior instead of assumption.For anyone building customer marketing systems, the lesson is practical. Treat persona development as a live system, not a static report. Use customer data to update your understanding regularly. Create tools that let everyone in your company hear what customers say in their own words. That way you can write content, sales materials, and product messaging that actually aligns with how people buy, not how you wish they did.Key takeaway: Persona marketing works when it functions as an always-on loop between customer data and company action. Map real behaviors, refresh those insights often, and share them widely. When everyone in your company hears the customer directly, you can shape messaging that feels relevant, personal, and authentic. That way you can scale customer understanding instead of guessing at it.Using AI to Mine Real Customer Intelligence from ConversationsAI is reshaping how teams understand their customers. Jane uses it as a force multiplier for customer research, not a replacement for human interpretation. Her process starts inside Gong’s platform, where every call, email, and deal interaction holds untapped evidence of what customers actually think. Instead of relying on small surveys or intuition, her team digs into those real conversations to extract patterns that explain why deals move forward or stall.When the team explores a new persona or market, they begin with what customers have already said. They gather every interaction tied to that persona and run it through a standardized set of research questions. In one project focused on CIOs, Jane’s team analyzed hundreds of calls to understand how these executives engage in deals. They wanted to know what information CIOs request, what they challenge, and how their questions differ from other buyers.“We were able to run a series of questions across hundreds of calls and get standardized insights in a couple of days,” Jane said. “That changed the tempo of how we learn.”Once they finish mining internal conversations, they widen their view to external data. They use AI tools like ChatGPT to scan analyst reports, trade publications, and articles that mention the same personas. That process identifies what topics are rising in the market and how those trends align with what Gong’s customers are discussing in their calls. The result is a dual-layered map of reality: what customers say in private conversations and what the market signals in public forums.This kind of research produces better decisions because it pairs scale with nuance. AI speeds up analysis across thousands of data points, but empathy gives meaning to those patterns. That way you can identify where customer perception shifts are happening and adjust messaging, enablement, or product focus before the market catches up.Key takeaway: Use AI to process the noise, not to replace your judgment. Start with the data you already have; call recordings, customer emails, and deal transcripts, and create a structured framework for what you want to learn. Th...

What’s up everyone, today we have the pleasure of sitting down with David Joosten, Co-Founder and President at GrowthLoop and the co-author of ‘First-Party Data Activation’.(00:00) - Intro (01:02) - In This Episode (03:47) - Earning The Right To Transform Martech (08:17) - Why Internal Roadshows Make Martech Wins Stick (10:52) - Architecture Shapes How Teams Move and What They Believe (16:25) - Bring Order to Customer Data With the Medallion Framework (21:33) - The Real Enemy of Martech is Fragmented Data (28:39) - Stop Calling Your CRM the Source of Truth (34:47) - Building the Tech Stack People Rally Behind (38:18) - Why Most CDP Failures Start With Organizational Misalignment (44:18) - Why Tough Conversations Strengthen Lifecycle Marketing (55:15) - Why Experimentation Culture Strengthens Martech Leadership (01:00:00) - How to Use a North Star to Stay Focused in Leadership Summary: David learned that martech transformation begins with proof people can feel. Early in his career, he built immaculate systems that looked impressive but delivered nothing real. Everything changed when a VP asked him to show progress instead of idealistic roadmaps. From that moment, David focused on momentum and quick wins. Those early victories turned into stories that spread across the company and built trust naturally. Architecture became his silent advantage, shaping how teams worked together and how confidently they moved. About DavidDavid is the co-founder of GrowthLoop, a composable customer data platform that helps marketers connect insights to action across every channel. He previously worked at Google, where he led global marketing programs and helped launch the Nexus 5 smartphone. Over the years, he has guided teams at Indeed, Priceline, and Google in building first-party data strategies that drive clarity, collaboration, and measurable growth.He is the co-author of First-Party Data Activation: Modernize Your Marketing Data Platform, a practical guide for marketers who want to understand their customers through direct, consent-based interactions. David helps teams move faster by removing data friction and building marketing systems that adapt through experimentation. His work brings energy and empathy to the challenge of modernizing data-driven marketing.Earning The Right To Transform MartechEvery marketing data project starts with ambition. Teams dream of unified dashboards, connected pipelines, and a flawless single source of truth. Then the build begins, and progress slows to a crawl. David remembers one project vividly. His team at GrowthLoop had connected more than 200 data fields for a global tech company, yet every new campaign still needed more. The setup looked impressive, but nothing meaningful was shipping.“We spent quarters building the perfect setup,” David said. “Then the VP of marketing called me and said, ‘Where are my quick wins?’”That question changed his thinking. The VP wasn’t asking for reports or architecture diagrams. He wanted visible proof that the investment was worth it. He needed early wins he could show to leadership to keep momentum alive. David realized that transformation happens through demonstration, not design. Theoretical perfection means little when no one in marketing can point to progress.From then on, he started aiming for traction over theory. That meant focusing on use cases that delivered impact quickly. He looked for under-supported teams that were hungry to try new tools, small markets that moved fast, and forgotten product lines desperate for attention. Those early adopters created visible success stories. Their enthusiasm turned into social proof that carried the project forward.Momentum built through results is what earns the right to transform. When others in the organization see evidence of progress, they stop questioning the system and start asking how to join it.Key takeaway: Martech transformations thrive on proof, not perfection. Target high-energy teams where quick wins are possible, deliver tangible outcomes fast, and use that momentum to secure organizational buy-in. Transformation is granted to those who prove it works, one visible success at a time.Why Internal Roadshows Make Martech Wins StickAn early martech win can disappear as quickly as it arrives. A shiny dashboard, a clean sync, or a new workflow can fade into noise unless you turn it into something bigger. David explains that the real work begins when you move beyond Slack celebrations and start building visibility across the company. The most effective teams bring their success to where influence actually happens. They show up in weekly leadership meetings for sales, data, and marketing, and they connect their progress to the company’s larger mission. That connection transforms an isolated result into shared purpose.“If you can get invited to those regular meetings and actually tie the win back to the larger vision, you’ll bring people along in a much bigger way,” David said.The mechanics of this matter. A martech team can create genuine momentum by turning their story into a live narrative that other departments care about. Each meeting becomes a checkpoint where others see how their world benefits. Instead of flooding channels with metrics, show impact in person. When people see faces, hear real stories, and feel included in the mission, adoption follows naturally.David has seen that the most credible voices are not the ones who built the system, but those who benefited from it. He encourages marketers to bring those users along. When a sales manager or a CX leader shares how a workflow saved hours or unlocked new visibility, trust deepens. One authentic endorsement in a meeting will do more for your reputation than a dozen slide decks.Momentum also depends on rhythm. Passionate advocates move ideas forward, not mass announcements. David’s playbook involves building a few strong allies who believe in your work, keeping promises, and maintaining a consistent drumbeat of delivery. Predictable progress creates confidence, and confidence earns permission to take bigger swings next time.Key takeaway: Wins that stay private fade fast. Present them live, in front of the right rooms, and connect them to the company’s shared mission. Bring along the people most impacted to tell their side of the story, and focus on nurturing a few genuine allies instead of broadcasting to everyone. That way you can turn one early success into a pattern of momentum that fuels every project that follows.Architecture Shapes How Teams Move and What They BelieveTechnology architecture does more than keep the lights on. It defines how much teams trust each other, how quickly they adapt, and how confidently a brand competes. David describes it as invisible scaffolding, the kind that quietly dictates how an organization moves. Once the systems are in place, the defaults harden into habits. Those habits shape behavior long after anyone remembers who set them.“People can get used to almost anything,” David said. “You acquire habits from architectural decisions made long ago, and it’s not conscious. You just walk into the context and act within it.”That pattern shows up inside every marketing organization. Data teams often build for accuracy and control, while marketers push for agility and access. The architecture decides which side wins. When the design prioritizes risk management, marketers spend months waiting for queries to be approved. When it prioritizes freedom without governance, trust breaks down the first time a campaign misfires. Neither version scales.Composable system...

What’s up everyone, today we have the pleasure of sitting down with Angela Vega, Director, Capabilities and Operations at Expedia Group.(00:00) - ‌Intro (01:18) - In This Episode (04:55) - Building an ADHD Techstack (11:11) - Why ADHD Shapes Better Decision-Making in Marketing Operations (19:06) - How to Turn ADHD Patterns Into Martech Leadership Strengths (23:38) - Why ADHD Helps Marketers Build Better Systems (31:25) - Building a Bridge Between Strategy and Execution in Marketing Ops (37:21) - Execution Defines Whether Ideas Live or Die (41:19) - Why Recent Execution Experience Builds Better Marketing Leaders (46:09) - How to Build Discernment in Martech Leadership (52:52) - Energy Economics for Marketing Ops Leaders (01:00:39) - How to Build a Personal Growth Formula in Marketing Leadership Summary: Angela built her ADHD tech stack as a way to survive the noise in her own head, turning distraction into design. Her workflow (Offload, Shape, Prototype, Loop, and Anchor) channels restless thought into motion through AI tools like Whisper and GPT. After her second pregnancy and a diagnosis that reframed her chaos, Angela stopped fighting her wiring and built systems that worked with it. Her fast, pattern-driven brain now thrives in marketing operations, where complexity rewards connection. She reads emotion like data, earning trust through clarity and transparency, and reminds leaders that execution, not strategy decks, moves companies forward. These days she measures success in energy and her mantra is “It’s just marketing, we’re not in the ER”.About AngelaAngela Vega has spent over 13 years in FinTech, health, and travel, she has unified global martech stacks, accelerated execution ninefold, and led CRM for Expedia, Vrbo, and Hotels.com, supporting over a billion monthly customer interactions. Her leadership grows both teams and ideas. She blends creative intuition with operational rigor, translating insight into systems that last. As a late-diagnosed ADHD professional, she experiments with AI to help neurodivergent leaders thrive, proving that marketing can be both human and scalable.Building an ADHD TechstackAngela built her ADHD Tech Stack to make her brain an ally instead of a hurdle. The system blends ADHD science with AI practicality, turning common executive function challenges into structured momentum. Each part of her workflow (Offload, Shape, Prototype, Loop, and Anchor) acts as a circuit for channeling mental noise into clarity. It is both a workflow and a survival strategy for people who juggle too many tabs at once, whether they are digital or mental.Her starting point came from frustration. Lists, sticky notes, and phone alarms worked for a while but always hit a ceiling. The real struggle was never remembering to do things but figuring out where to start. Executive function is about getting from idea to action, and for ADHD professionals, that gap can feel massive. Angela found her bridge in AI tools that could listen, capture, and organize her thinking in real time. Whisper transcribes her thoughts. GPT shapes them into frameworks. Gemini helps her plan and communicate with clarity.“I talk out loud all the time. Instead of saying things into the abyss, I say them into AI,” Angela said. “One system holds my to-dos, another handles updates for my boss, and another helps me break big goals into smaller steps.”Her stack follows five steps that anyone can adapt:Offload: Speak or type ideas into AI to clear mental clutter.Shape: Ask AI to sort and group ideas into logical categories.Prototype: Turn thoughts into quick drafts or mockups to trigger dopamine and action.Loop: Use AI for feedback, reflection, and gentle nudges that replace self-criticism.Anchor: Set reminders, templates, and adaptive systems that help you return to projects smoothly.Angela’s framework works because it aligns tools with real human behavior instead of forcing people into rigid systems. The design rewards momentum over perfection. It gives permission to think out loud, change direction, and experiment without shame. Every ADHD brain operates differently, so every system should too. AI’s flexibility makes that possible by turning scattered thoughts into structured workflows without losing the spark that drives creativity.Key takeaway: Treat productivity as a design challenge, not a discipline test. Use AI to capture ideas before they vanish, shape them into usable form, and build adaptive anchors that forgive interruptions. That way you can create a personal martech system that channels ADHD energy into consistent output, steady progress, and fewer moments of paralysis.Why ADHD Shapes Better Decision-Making in Marketing OperationsADHD rewires how people handle complexity, and marketing operations thrive on complexity. Angela discovered that her diagnosis reframed everything about her work and leadership. Years of restless multitasking, late-night thought spirals, and endless side projects suddenly made sense. Her mind was not unfocused. It was constantly building new connections, scanning for patterns, and searching for stimulation that most work environments suppress.Her diagnosis arrived during a storm of personal and professional change. After her second pregnancy, her coping systems stopped working. Therapy no longer grounded her, medication clashed with her body, and grief from losing her father-in-law blurred her focus. Meanwhile, the pressure at work continued to grow. Leadership demanded stability while her world spun faster each week. Reaching for help was not an act of surrender. It was a recalibration of survival.“I have a lot of thoughts in my head. It’s sometimes super hard to fall asleep. I think of the twenty things that might go wrong and the hundred hobbies I have,” Angela said.When testing confirmed ADHD combined type, disbelief gave way to validation. The diagnosis gave shape to her chaos. She stopped labeling her quirks as flaws and started understanding them as traits with purpose. Her curiosity was a strength, not a distraction. Her brain thrived in dynamic systems where rules shifted and creativity met precision. That explained her pull toward marketing operations, where nothing stays still and every campaign or data sync has moving parts that need decoding.Angela began building systems that complemented her wiring instead of fighting against it. She used visual workflows to clear mental clutter, broke large tasks into tight sprints, and surrounded herself with teammates who balanced her energy with structure. ADHD did not make her less capable. It made her more adaptive. In a field that rewards fast problem-solving and parallel thinking, her mind became her greatest operational advantage.Key takeaway: ADHD changes how leaders process and prioritize information, and awareness turns that difference into strategy. Identify where your energy peaks and build workflows around those cycles. Use external systems to store what your brain refuses to hold. Protect deep-focus windows instead of forcing consistency. The goal is not to tame your wiring but to design with it, that way you can turn what once felt chaotic into sustainable momentum.How to Turn ADHD Patterns Into Martech Leadership StrengthsADHD often gets framed as distraction, but in martech leadership, it can function as accelerated pattern recognition. Angela’s brain fires fast. She sees connections before most people finish explaining the problem. “I can jump from one topic to another pretty quickly because in my mind I’ve already created the five c...

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 OperationsAgentic 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 AgentsAutonomous 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

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-janHenk-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 MartechPirates 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 OpsSpeaking 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 EnterprisesComposable 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...

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 AditiAditi 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 DataDashboards 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 ConversationsMarketers 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 ChannelsMarketers 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...

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 RebeccaRebecca 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 LayerRebecca 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 OrgLifecycle 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 FadingChannel specialists are facing a turning point. Rebecca explains that AI agents now handle many of the mechanical tasks that ...

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 SilosAgencies 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 DashboardsDashboards 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...

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 OlgaOlga 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 RolesOlga 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 entirelyShe 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 GPTsAI 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 PerfectAI 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...