Episodios

  • 194: Jane Menyo: How Gong democratized customer proof with AI research and standardized prompts

    194: Jane Menyo: How Gong democratized customer proof with AI research and standardized prompts
    Nov 4 2025
    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 MarketingSummary: 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 ...
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    53 m
  • 193: David Joosten: The Politics and architecture of martech transformation

    193: David Joosten: The Politics and architecture of martech transformation
    Oct 28 2025
    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 LeadershipSummary: 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,” ...
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    1 h y 3 m
  • 191: Aboli Gangreddiwar: Self healing data agents, hivemind memory curators and living documentation

    191: Aboli Gangreddiwar: Self healing data agents, hivemind memory curators and living documentation
    Oct 14 2025
    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 LeaderSummary: 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 ...
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    1 h y 3 m
  • 190: Henk-jan ter Brugge: The Head of Martech at Philips thinks martech has outgrown marketing and it’s time we lead like pirates

    190: Henk-jan ter Brugge: The Head of Martech at Philips thinks martech has outgrown marketing and it’s time we lead like pirates
    Oct 7 2025
    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 EnergySummary: 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...
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    1 h
  • 189: Aditi Uppal: How to capture, activate and measure voice of customer across go to market efforts

    189: Aditi Uppal: How to capture, activate and measure voice of customer across go to market efforts
    Sep 30 2025
    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 BalanceSummary: 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 ...
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    53 m
  • 188: Rebecca Corliss: Why lifecycle marketers will thrive in the agentic marketing org

    188: Rebecca Corliss: Why lifecycle marketers will thrive in the agentic marketing org
    Sep 23 2025
    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 LeaderSummary: 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 ...
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    57 m
  • 187: John Saunders: Building the ultimate operating engine for a modern agency

    187: John Saunders: Building the ultimate operating engine for a modern agency
    Sep 16 2025
    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 ExperimentationSummary: 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 ...
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    54 m
  • 186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops

    186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops
    Sep 9 2025
    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 StackSummary: 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 ...
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