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

What’s up everyone, today we have the pleasure of sitting down with Olga Andrienko, Former VP of Marketing Ops at Semrush. (00:00) - Intro (01:24) - In This Episode (03:55) - How AI Agents Reshape Marketing Ops Roles (08:53) - How To Beat AI Imposter Syndrome And Start Using Custom GPTs (13:28) - How AI Content Agents Generate Drafts Using Internal Context (24:29) - How to Use a Risk and Reward Grid to Prioritize AI Projects (33:19) - How To Use Google Workspace To Skip AI Vendor Approvals (40:00) - How To Decide Which AI Agent to Use (46:44) - How To Build an AI-First Reflex in Marketing Ops (51:59) - AI’s Endgame: Play-to-Earn and Mandatory Human Quotas (01:03:58) - What Happens When You Optimize Your Body Like a Martech Stack Summary: Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI. She started with the work she already knew, made it faster, and used that time to build better systems. Now she’s looking ahead, watching work blur into participation, prepping for human quotas, and making sure ops teams aren’t caught off guard while the rest of the company is still testing prompts.About 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...

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

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

What’s up everyone, today we have the pleasure of sitting down with Kevin White, Head of GTM Strategy at Common Room. (00:00) - Intro (01:00) - In This Episode (02:59) - How to Design a Super IC Role for Senior Marketers (09:11) - How to Get Comfortable With Public Visibility as an Introverted Leader (10:39) - sing Empathy and Product Demos to Build Authentic GTM Strategies (16:52) - How to Use Pain Points to Make Personalization Work (19:21) - How to Use Buyer Behavior Signals to Improve Outreach Timing (21:36) - Leveraging GitHub Signals to Drive High-Conversion Micro Campaigns (24:57) - Smarter Account Prioritization With Buyer Signals (29:02) - Why Messaging Drives GTM More Than Signals and Plays (31:16) - Why Overengineered Tech Stacks Fail GTM Teams (35:05) - Why AI SDR Agents Need Structured Coaching to Work (41:43) - Why The Last Mile Of AI Marketing Still Belongs To Humans (43:57) - AI Sharpens the Divide Between Experts and Amateurs (45:46) - Why Declaring Human-Written Outreach Gets Better Responses (48:00) - Futureproofing Operations Skills Through Challenge Driven Learning (51:46) - Why Data Warehouses Are Taking Over Customer Data Platforms (55:32) - Finding Career Balance Through Self Reflection Summary: Kevin rebuilt his career around the work that fuels him. After years leading teams at Segment, Retool and Common Room, he walked away from politics and board decks to create a “super IC” role focused on experiments, product evangelism, and hands‑on growth. He applies that same mindset to go‑to‑market: strip out the bloat, ditch templated outreach, and use real buyer behavior to build small, personal campaigns. He treats AI as an amplifier for skilled marketers, using it to speed research and sharpen ideas, while relying on human judgment to make the output work. Even visibility, once draining for him, became a muscle he trained through repetition. Kevin’s story is a guide for marketers who want less political fluff, more impact, and roles built around the work they actually love to do.About KevinKevin White is a seasoned go-to-market leader with over 20 years of experience driving growth for high-growth SaaS companies. He’s held senior roles at Gigya, SingleStore, HackerOne, and Twilio Segment, where he built demand generation engines and scaled marketing operations during critical growth stages.Most recently, Kevin led marketing at Retool and advanced through multiple leadership roles at Common Room, from Head of Demand Generation to Head of Marketing, and now Head of GTM Strategy. He has also advised innovative startups like Ashby, Gretel.ai, and Deepnote, helping them refine their go-to-market strategies and accelerate adoption.How to Design a Super IC Role for Senior MarketersClimbing the marketing ladder feels like progress until you realize the work at the top is entirely different. Kevin spent years running teams at Retool and Common Room. He managed a dozen people, dealt with SDR team politics, prepared board updates, and handled internal marketing. Those tasks ate up his time and dulled his energy for the work that made him great in the first place. “My day-to-day was full of things I didn’t enjoy. One-on-ones, internal marketing, SDR team drama, board updates. None of it felt like what I wanted to be doing,” he said.Kevin thrived in the early-stage chaos. He loved being the first marketer, building programs from scratch, experimenting with growth channels, and connecting directly with customers. Those environments let him create instead of coordinate. He could see the direct impact of his work and feel close to the product. As companies grew, that hands-on work disappeared. He became a coach, a manager, and a political operator. For someone who values doing over directing, that was a poor fit.He worked with Common Room’s CEO to design a role that put him back in his zone. Now, as Head of GTM Strategy, Kevin functions as a “super IC.” He runs high-leverage growth experiments, drives product evangelism, and collaborates with a few freelancers instead of managing a team. That way he can focus on the work that delivers impact while avoiding the politics and administrative load that drained him. It is a custom role built around his strengths, and it brought back his enthusiasm for the job.Kevin’s thinking extends beyond his role. He shared how Common Room rethought sales development. They hired an excellent manager who knows how to attract and retain elite talent. Then they paid those top performers well above the market rate. “Harry is one of our SDRs,” Kevin explained. “We pay him a good amount because he produces outsized results. That playbook works.” In Kevin’s view, companies should build alternative tracks for individual contributors and reward them based on their production, not their willingness to manage people.Key takeaway: Create roles that match strengths instead of forcing people up a management ladder. Build paths for senior individual contributors who can deliver massive value without leading teams. Pay top performers according to their impact, not their title. If you manage teams, audit which roles could benefit from this model and where high-performers need more autonomy. If you are an individual contributor, consider what a custom role would look like that keeps you close to the work you do best.Building Confidence With Public Visibility as an Introverted LeaderPublic visibility exhausts many introverted leaders. Kevin describes finishing a full day at a conference feeling drained, running only on caffeine to get through the next one. Sharing his voice on LinkedIn or recording videos once felt unbearable. Even now, he admits to taking multiple tries before posting anything. Despite that discomfort, he continues to do it because the repetition has transformed the work from a chore into a habit.“I was mortified at myself when I first started recording things,” Kevin said. “But I kept hearing people say how helpful it was, and that positive reinforcement made it easier.”Kevin builds on small steps instead of waiting for confidence to appear. He creates a cycle where he pushes himself into uncomfortable situations, collects positive feedback, and uses that reinforcement to do it again. Over time, the acts that once caused him anxiety, like posting thought pieces or speaking publicly, have become regular parts of his work.He views visibility as a skill that can be practiced. Instead of thinking in terms of strengths or weaknesses, he treats every new action as training. This perspective removes the pressure to “perform” and reframes the process as building a muscle. That makes posting online, speaking at events, and showing up in public spaces a set of learnable behaviors rather than personal traits.You can use his approach:Start with small, low-stakes actions like sharing short ideas on LinkedIn.Progress to more challenging mediums such as podcasts or short recorded demos.Save positive responses to use as reminders when your motivation dips.Treat every effort as practice, which builds resilience and lowers fear over time.Key takeaway: Confidence grows through repetition. Build it by starting with small visibility actions, collecting reinforcement, and gradually increasing the difficulty of your public presence. That way you can turn something that drains you into a manageable, even natural, part of your role.Using Empathy and Demos to Build Authentic GTM StrategiesKevin remembers the grind of stitching together spreadsheets, Zaps, and Salesforce automat...

What’s up everyone, today we have the pleasure of sitting down with Simon Lejeune, VP of Growth at Wealthsimple. (00:00) - Intro (01:16) - In This Episode (03:55) - How to Escape Local Maximum Traps in Growth Marketing (08:59) - Productive Laziness Mindsets (12:03) - The Psychological Trap of A/B Testing (15:55) - Balancing Clean Experiments with Bold Bets (18:43) - How to Use Incrementality to Measure Real Campaign Impact (22:32) - How to Approach Incrementality Without Large Data Sets (25:13) - The Best Use Cases for Incrementality Tests (29:58) - How to Handle ROI Conversations Without Slowing Down Growth (38:02) - Why Most A/B Testing Is a Waste of Time (47:17) - When Natural Language Becomes the Interface, Channel Expertise Stops Being a Moat (01:03:31) - How to Use Game Thinking to Stay Energized in Growth Roles Summary: Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains by deleting work that doesn’t matter, cutting projects when the lift is negligible, and measuring true incrementality with one simple question: “What would have happened if we didn’t do this?” He believes AI is accelerating this shift, turning deep channel expertise into a commodity and making curiosity, speed, and ruthless prioritization the real competitive advantages. Growth, in his view, belongs to teams who can abandon the comfort of optimization and pursue experiments big enough to change the trajectory.About SimonSimon Lejeune is a seasoned growth leader with over a decade of experience scaling some of North America’s most recognized tech brands. Currently VP of Growth at Wealthsimple, he drives client and asset growth across products like Trade, Crypto, Cash, Invest, and Tax. Before that, Simon founded Mile End Growth, a boutique agency delivering strategy, creative, and media buying for startups, and led user acquisition at Hopper, where he managed multimillion‑dollar budgets and built one of the most sophisticated in‑house ad automation engines in travel tech. His career began at Busbud and Nomad Logic, where he directed growth marketing and developed new revenue‑generating spin‑offs.Local Maximum vs Global MaximumHow to Escape Local Maximum Traps in Growth MarketingA local maximum trap happens when teams keep optimizing small features that look like wins but cap long-term growth. Simon uses the metaphor of being blindfolded on uneven terrain. You walk in every direction until each step feels lower, then assume you have reached the peak. When you take off the blindfold, you see you are standing on a hill while a much larger mountain waits in the distance. Many growth teams spend months, sometimes years, stuck on those hills.Simon experienced this lesson in an uncomfortable way. During his final interview at Hopper, CEO Fred Lalonde asked him what he would change first to grow revenue in the app. Simon answered with what felt like a logical idea. He suggested testing different price points for the $5 tip option, maybe $4 or $6, to find the best revenue point.“He looked at me and said, ‘That’s literally a local maximum, and I do not want you doing that,’” Simon recalled.That feedback forced Simon to change his perspective. He proposed a more radical idea: building a separate app that would use Hopper’s flight data to surface ultra-cheap Ryanair-style deals under five euros. It sounded risky and unconventional, but Lalonde loved it. Simon left that meeting understanding that real growth often comes from bigger, more disruptive ideas that challenge the current model instead of refining it.Growth teams can apply this lesson by actively questioning whether their experiments drive material change or simply polish what already exists. Regularly evaluate whether you are optimizing features, pricing, or flows when the real opportunity may be entirely new product lines, bold pricing experiments, or acquisition channels that look nothing like what you use today.Key takeaway: Incremental optimizations create comfort but rarely drive exponential growth. Audit your current priorities and identify one experiment that pushes far beyond incremental gains. Focus on ideas that reimagine your product, acquisition model, or customer experience. That way you can escape local maximum traps and open paths to growth that small experiments will never reach.Productive Laziness MindsetsSimon challenges his team to delete more work than they refine. “The fastest way to do something is not to do it,” he said. He encourages what he calls “productive laziness,” which means questioning why a task exists before sinking hours into improving it. Many growth teams fill their calendars with recurring meetings and busywork that provide comfort but little actual impact. Simon wants his team to hunt down and remove the 80 percent of work that clogs up progress.“You could probably not do 80 percent of what you’re doing, and you need to take the time to find it.”He distinguishes between local and global maxima. Local maxima are small, incremental wins that stack up over time. They create efficiency, but they rarely transform outcomes on their own. Simon shared a story from Wealthsimple where his team used a simple AI-driven prompt to quickly generate FAQs for new promotions. It only improved one process by a few percentage points. Combined with other similar fixes, it meaningfully reduced the time from campaign idea to launch. These small process wins compound into real operational speed.Simon points out the trap many teams fall into when these tweaks become the entire focus. He calls out A/B testing as the classic example. The industry celebrates small lifts in conversion rates, yet the promised gains rarely translate into significant growth. “If you’ve had that many wins,” he said, “your conversion rate should be 300 percent by now.” Experienced operators know that most of these improvements exist only in reports, not in the top-line numbers.He pushes for a balance between incrementalism and bold redesigns. Teams need to ask hard questions: Does this process deserve to exist? Does this experiment meaningfully impact the business? Would rebuilding this system create more value than optimizing it? Those are global maximum questions, and they require a willingness to break away from the comfort of small wins to pursue something transformative.Key takeaway: Use incremental process improvements to accelerate execution, but regularly pause to audit whether the work itself creates meaningful business impact. Remove tasks, experiments, or processes that do not clearly connect to growth. That way you can free up the capacity to pursue global maximum opportunities that drive measurable, lasting results.The Psychological Trap of A/B TestingLocal maximum thinking happens when teams equate motion with progress. Simon describes this as the pattern of chasing a string of small A/B test wins while nothing meaningful shifts in the business. Every tweak produces a minor lift, enough to justify the next experiment, yet the bigger picture remains stagnant. The illusion of progress feels convincing in the moment, which is why so many teams stay stuck in it.He experienced this firsthand at Wealthsimple. The team wanted to answer a straightforward question: should they push new users toward onboarding ...

What’s up folks, today we have the pleasure of sitting down with Alison Albeck Lindland, CMO at Movable Ink.(00:00) - Intro (01:14) - In This Episode (03:10) - 1. Movable Ink's Platform Evolution (04:19) - 2. Alison's 3 Stage Journey at Movable Ink (05:08) - 3. Using Customer Relationships to Future Proof a Marketing Career (09:50) - 4. Building AI Literacy in Marketing Teams (16:17) - 5. How to Spot AI Literacy in Marketing Hires (21:35) - 6. Fostering AI Experimentation Across Your Team (25:43) - 7. AI Point Solutions vs Platforms (30:37) - 8. Align CMOs and Boards on Long Term Marketing Goals (33:37) - 9. How to Measure and Maximize the ROI of Video Podcasts (40:23) - 10. Building a Customer Strategy Team That Drives Enterprise Growth (49:36) - 11. How To Build Lasting Influence With B2B Buyers (55:49) - 12. Creating Energy and Balance as a CMO Summary: Alison believes marketing careers thrive when you stay close to the people who buy from you, and at Movable Ink she has built that into the culture with a customer strategy team, advisory boards, and events that create real connections customers carry into new roles. She applies the same thinking to AI, starting with shared tools and boundaries, then layering in structured experimentation and custom apps that live inside daily workflows. Alison hires people who tinker on their own time, keeps experimentation alive with weekly check‑ins and show‑and‑shares, and cuts projects that do not deliver, like ending a podcast to focus on high‑impact testimonial and “hero” videos. Through it all, she builds influence by aligning teams on one scorecard, sharing loyalty stories that prove long‑term value, and helping buyers see her platform as part of their personal playbook for success.About AlisonAlison is the Chief Marketing Officer at Movable Ink, leading global marketing, brand, strategy, and communications for the AI-powered personalization platform used by the world’s top brands. In her 12+ years at Movable Ink, she’s had three distinct phases: rising through customer success, founding the company’s now-influential strategy team, and stepping into the CMO role nearly three years ago. That journey (across constant evolution and new challenges) has kept the work “never the same company for more than six months at a time,” and helped shape Movable Ink’s role as a leader in enterprise personalization.Customer Relationships Can Future Proof a Marketing CareerAlison argues that the best way to future proof a marketing career is by knowing your customers as actual people rather than abstract data points. Marketers who thrive over time make it their job to understand what customers want, how they think, and why they buy. "You have to know them personally and pretty intimately," she says. "You’ve got to be constantly advocating for their perspective around the table." That kind of understanding does not happen in a spreadsheet. It happens in conversations, often unplanned ones, that give you unfiltered context about their challenges and priorities.She has turned this belief into a repeatable practice at Movable Ink. Her team builds ongoing contact with customers through multiple channels, including:Quarterly fireside chats with CMOs who share their challenges and ideas.A hybrid customer advisory board that rotates in staff members to observe and participate.Strategic placement of marketers at in-person events where they can form real connections.These interactions do more than collect feedback. They create a loop where customer input shapes campaigns, product positioning, and content. Alison credits these relationships with Movable Ink’s staying power. Marketers who use their platform often bring it with them when they change roles or companies, expanding the brand’s reach through personal advocacy."We spend a lot of time now trying to bring our team members in close contact with our customers in more than just a servicing capacity," Alison explains. "They need to develop personal relationships that inform the work they are doing, whether it is content marketing, events, or ABM."Alison also leans on product marketing as a partner in capturing deeper customer knowledge. She highlights win-loss interviews as especially valuable. Unlike survey data, these conversations expose what is working and where gaps exist with enough specificity to guide real change. Her team uses these discussions to refine strategy and make decisions with authority. Marketers who adopt this mindset do more than execute tactics. They become trusted voices in shaping what their company brings to market.Key takeaway: Build constant, meaningful contact with your customers. Use advisory boards, interviews, and live events to hear their unfiltered perspectives. Treat these conversations as fuel for your campaigns and strategies. When you consistently advocate for customers with authority, you position yourself as someone whose work will stay relevant no matter how the tools, titles, or industry trends shift.AI Literacy in Marketing: How to Build AI Literacy in Marketing TeamsAI literacy in marketing takes shape when organizations stop treating AI like a playground and start building a framework for real, coordinated adoption. Alison Albeck Lindland pushes for a model where alignment and enablement come before experimentation. “You need to make sure you’re all singing from the same songbook,” she says. When teams skip that step, they end up with scattered projects, compliance headaches, and wasted time. A clear, shared framework turns AI from a set of personal experiments into an enterprise capability.This is why the updated Pyramid of AI Literacy begins with organizational alignment and standardized tooling at its base. These steps give teams a shared understanding of the company’s AI strategy, ethical guidelines, and compliance boundaries, along with enterprise-grade tools that build institutional knowledge instead of one-off fiefdoms. Alison’s point is direct: enterprise AI can only scale when everyone is using the same platforms and working from the same rulebook.“OpenAI is great, but we’re using a tool that lets us build institutional muscle and share learnings across teams.”The middle of the pyramid focuses on practical proficiency, experimentation, and model literacy. Teams develop real competency with structured prompts and multi-model workflows. They also learn how large language models work and how AI connects to data, workflows, and machine learning systems. Experience does not come from a training course. It comes from giving teams space to test ideas, share lessons learned, and build the muscle memory to use AI effectively.At the top sits strategic leadership. This is where marketing leaders guide the organization with clear purpose, challenge hype, and embed AI into the company’s growth strategy. At Movable Ink, this looks like dedicated business analysts building custom AI apps that plug into daily work, from a brand voice checker to a natural language search bot for surfacing industry-specific content. These tools live inside workflows, making AI part of the operating rhythm instead of a side project.Key takeaway: Use the pyramid as your blueprint for building AI literacy. Start by aligning the organization on strategy, ethics, and enterprise tools. Then train teams to get real value from AI through structured prompts, model literacy, and cross-functional experimentation. Finally, put strong leadership at the top to guide adoption with purpose. That way you can move AI from scattered experiments to a unified, scalable capability that drives ...

What’s up everyone, today we have the pleasure of sitting down with István Mészáros, Founder and CEO of Mitzu.io. (00:00) - Intro (01:00) - In This Episode (03:39) - How Warehouse Native Analytics Works (06:54) - BI vs Analytics vs Measurement vs Attribution (09:26) - Merging Web and Product Analytics With a Zero-Copy Architecture (14:53) - Feature or New Category? What Warehouse Native Really Means For Marketers (23:23) - How Decoupling Storage and Compute Lowers Analytics Costs (29:11) - How Composable CDPs Work with Lean Data Teams (34:32) - How Seat-Based Pricing Works in Warehouse Native Analytics (40:00) - What a Data Warehouse Does That Your CRM Never Will (42:12) - How AI-Assisted SQL Generation Works Without Breaking Trust (50:55) - How Warehouse Native Analytics Works (52:58) - How To Navigate Founder Burnout While Raising Kids Summary: István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster. One customer dropped their bill from $500K to $1K just by switching to seat-based pricing. István shares how AI helps, but only if you still understand the data underneath. This conversation shows what happens when marketing, product, and data finally work off the same source without second-guessing every report.About IstvánIstvan is the Founder and CEO of Mitzu.io, a warehouse-native product analytics platform built for modern data stacks like Snowflake, Databricks, BigQuery, Redshift, Athena, Postgres, Clickhouse, and Trino. Before launching Mitzu.io in 2023, he spent over a decade leading high-scale data engineering efforts at companies like Shapr3D and Skyscanner. At Shapr3D, he defined the long-term data strategy and built self-serve analytics infrastructure. At Skyscanner, he progressed from building backend systems serving millions of users to leading data engineering and analytics teams. Earlier in his career, he developed real-time diagnostic and control systems for the Large Hadron Collider at CERN. How Warehouse Native Analytics WorksMarketing tools like Mixpanel, Amplitude, and GA4 create their own versions of your customer. Each one captures data slightly differently, labels users in its own format, and forces you to guess how their identity stitching works. The warehouse-native model removes this overhead by putting all customer data into a central location before anything else happens. That means your data warehouse becomes the only source of truth, not just another system to reconcile.István explained the difference in blunt terms. “The data you’re using is owned by you,” he said. That includes behavioral events, transactional logs, support tickets, email interactions, and product usage data. When everything lands in one place first (BigQuery, Redshift, Snowflake, Databricks) you get to define the logic. No more retrofitting vendor tools to work with messy exports or waiting for their UI to catch up with your question.In smaller teams, especially B2C startups, the benefits hit early. Without a shared warehouse, you get five tools trying to guess what an active user means. With a warehouse-native setup, you define that metric once and reuse it everywhere. You can query it in SQL, schedule your campaigns off it, and sync it with downstream tools like Customer.io or Braze. That way you can work faster, align across functions, and stop arguing about whose numbers are right.“You do most of the work in the warehouse for all the things you want to do in marketing,” István said. “That includes measurement, attribution, segmentation, everything starts from that central point.”Centralizing your stack also changes how your data team operates. Instead of reacting to reporting issues or chasing down inconsistent UTM strings, they build shared models the whole org can trust. Marketing ops gets reliable metrics, product teams get context, and leadership gets reports that actually match what customers are doing. Nobody wins when your attribution logic lives in a fragile dashboard that breaks every other week.Key takeaway: Warehouse native analytics gives you full control over customer data by letting you define core metrics once in your warehouse and reuse them everywhere else. That way you can avoid double-counting, reduce tool drift, and build a stable foundation that aligns marketing, product, and data teams. Store first, define once, activate wherever you want.BI vs Analytics vs Measurement vs AttributionBusiness intelligence means static dashboards. Not flexible. Not exploratory. Just there, like laminated truth. István described it as the place where the data expert’s word becomes law. The dashboards are already built, the metrics are already defined, and any changes require a help ticket. BI exists to make sure everyone sees the same numbers, even if nobody knows exactly how they were calculated.Analytics lives one level below that, and it behaves very differently. It is messy, curious, and closer to the raw data. Analytics splits into two tracks: the version done by data professionals who build robust models with SQL and dbt, and the version done by non-technical teams poking around in self-serve tools. Those non-technical users rarely want to define warehouse logic from scratch. They want fast answers from big datasets without calling in reinforcements.“We used to call what we did self-service BI, because the word analytics didn’t resonate,” István said. “But everyone was using it for product and marketing analytics. So we changed the copy.”The difference between analytics and BI has nothing to do with what the tool looks like. It has everything to do with who gets to use it and how. If only one person controls the dashboard, that is BI. If your whole team can dig into campaign performance, break down cohorts, and explore feature usage trends without waiting for data engineering, that is analytics. Attribution, ML, and forecasting live on top of both layers. They depend on the raw data underneath, and they are only useful if the definitions below them hold up.Language often lags behind how tools are actually used. István saw this firsthand. The product stayed the same, but the positioning changed. People used Mitzu for product analytics and marketing performance, so that became the headline. Not because it was a trend, but because that is what users were doing anyway.Key takeaway: BI centralizes truth through fixed dashboards, while analytics creates motion by giving more people access to raw data. When teams treat BI as the source of agreement and analytics as the source of discovery, they stop fighting over metrics and start asking better questions. That way you can maintain trusted dashboards for executive reporting and still empower teams to explore data without filing tickets or waiting days for answers.Merging Web and Product Analytics With a Zero-Copy ArchitectureMost teams trying to replace GA4 end up layering more tools onto the same mess. They drop in Amplitude or Mixpanel for product analytics, keep something else for marketing attribution, and sync everything into a CDP that now needs babysitting. Eventually, they start building one-off pipelines just to feed the same events into six different systems, all chasing slightly different answers to the same question.István sees this fragmentation as a byproduct of treating product and marketing analytics as separate functions. In categorie...

What’s up everyone, today we have the pleasure of sitting down with Tiankai Feng, Data & AI Strategy Director at Thoughtworks and Author of Humanizing Data Strategy. (00:00) - Intro (01:06) - In This Episode (03:18) - How Data and Marketing Create a Symbiotic Relationship (06:00) - If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel Alliance (08:26) - How to Organize Data Teams and Improve Marketing Collaboration (14:49) - Handling Healthy Data Conflicts Without Crushing Creativity (25:23) - How to Use Shadowing to Fix Broken Marketing Alignment (36:44) - The Comeback of Data Quality (43:20) - How Natural Language BI Tools Change Data Analyst Work (46:50) - How Composable Data Management Works in Marketing (53:30) - How to Use Authentic Communication to Build Influence in Marketing Ops (56:40) - Happiness Summary: Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic. He’s seen teams thrive when they pick trade-offs upfront, document how everyone fits together, and take ownership of clean, reliable inputs instead of trusting AI to fix sloppy work later. Even the best tools still need humans to design the logic behind the scenes. When teams care about context and build real relationships, data becomes the backbone that keeps marketing strong under pressure.About TiankaiTiankai Feng is Director of Data & AI Strategy at Thoughtworks, where he leads global service offerings spanning data governance, AI strategy, and modernization initiatives. He is the author of Humanizing Data Strategy – Leading Data with the Head and the Heart, and serves on the Education Advisory Board at DataQG. Previously, Tiankai spent over six years at Adidas as Senior Director of Product Data Governance, shaping data practices across global teams. He is also Head of Marketing at DAMA Germany, helping grow the country’s leading data management community. Earlier in his career, Tiankai worked as a senior consultant with TD Reply, advising major brands on digital strategy and performance. Recognized as a top data product thought leader, he is passionate about bridging the gap between technical excellence and human-centered data cultures.How Data and Marketing Create a Symbiotic RelationshipIt is interesting to consider how many data professionals started their careers by obsessing over why advertising can make people feel something. Tiankai shared that he studied campaigns as a kid and felt driven to decode the hidden mechanics behind each message. He called it the science behind the feeling. He wanted to understand why a phrase could trigger a decision and what evidence proved it actually worked.When he chose his degree, he blended marketing with database systems because he believed data could ground creative work in reality. He wanted a way to measure the effectiveness of ideas instead of relying on gut reactions. That decision led him into marketing analytics, where he learned to balance instinct with structured evidence. He described this period as the moment he first saw every click, conversion, and impression as a trail of signals pointing to what people valued most.Tiankai shared that many companies separate marketing from data in ways that weaken both. He believes that every creative idea grows stronger when it gets tested by proof. He said, “You have a lot of thoughts and gut feelings, but what if you could actually rely on proof to make better decisions?” He still asks this question whenever he evaluates a strategy or decides how to communicate the value of a data project.He also applies marketing principles inside his own teams. He treats internal projects like product launches and focuses on storytelling as much as reporting. He learned that evidence alone rarely convinces stakeholders. People respond when data feels relevant and easy to act on. He credits this mindset to his early work in brand campaigns, which taught him that information becomes meaningful when it connects to someone’s goals and emotions.“By heart, I’m still a marketer,” he said. “Even now, I’m applying what I learned in marketing to convince stakeholders to work with me.”This blend of skills helps teams create strategies that people believe in and understand. When marketing and data share the same goals, campaigns feel both credible and inspiring.Key takeaway: Blending marketing analytics with creative thinking lets you challenge assumptions and build strategies that people trust. When you share data work, present it like a product launch. Frame the message in relatable stories, make the numbers clear, and show how the information supports better decisions. That way you can help teams act with confidence and prove the impact of their ideas.If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel AllianceIt is interesting to consider how marketing teams keep borrowing Star Wars metaphors to make sense of the work. Tiankai described clean, governed data as the Jedi Council, the calm authority that brings order and discipline. He shared that marketing operations always felt more like the Rebel Alliance, a team of underdogs improvising bold plans and building strategies out of whatever they could find in the hangar.In those early years, nobody had a clear guidebook. Teams cobbled together workflows, tested ideas with half-finished data, and celebrated any dashboard that did not explode during a quarterly review. Tiankai remembered feeling like every small win was a victory against the Empire of bad processes. This scrappy environment fueled creativity, but it also came with plenty of late nights and occasional panic.Today, marketing ops feels more settled. > “There’s more experience and more best practices to be shared,” he said. Teams now have detailed frameworks, polished documentation, and tools that mostly work the way they promise. That way you can spend less time guessing and more time refining campaigns that drive results. You can treat the Jedi Council as a helpful ally rather than an unreachable ideal.Tiankai still believes good operators keep a bit of rebel spirit. Even the best-governed data will sometimes contradict reality on the ground. When those moments happen, it helps to trust your instincts and build something that makes sense for your business, not just the standard playbook. The Jedi Council can provide discipline, but someone still has to step into the hangar and fly the mission.Marketing operations has grown up, but it never lost the urge to experiment. The work feels rewarding when you blend clear frameworks with your own curiosity and a willingness to bend the rules when the stakes demand it.Key takeaway: Data governance acts like a steady Jedi Council, giving your marketing operations clarity, trust, and a strong backbone. To get the most from it, combine those proven systems with the resourcefulness of a rebel team. Stay ready to challenge assumptions, tweak the plan, and follow your judgment when data alone does not tell the full story. That way you can build workflows that are disciplined enough to scale and flexible enough to handle reality without falling apart.How to Organize Data Teams and Improve Marketing CollaborationIt is interesting to consider how data ownership used to feel like an afterthought in early SaaS companies. Tiankai remembered scraping together metrics by hand, jumping between marketing dashboards and...

What’s up everyone, today we have the pleasure of sitting down with Guta Tolmasquim, CEO at Purple Metrics. Summary: Brand measurement often feels like a polite performance nobody fully believes, and Guta learned this firsthand moving from performance marketing spreadsheets to startup rebrands that showed clear sales bumps everyone could feel. She kept seeing blind spots, like a bank’s soccer sponsorship that quietly cut churn or old LinkedIn pages driving conversions no one tracked. When she built Purple Metrics, she refused to pretend algorithms could explain everything, designing tools that encourage gradual shifts over sudden upheaval. She watched CMOs massage attribution settings to fit their instincts and knew real progress demanded something braver: smaller experiments, simpler language, and the courage to say, “We tried, we learned,” even when results stung. Her TikTok videos in Portuguese became proof that brand work can pay off fast if you track it honestly. If you’re tired of clean stories masking messy reality, her perspective feels like a breath of fresh air.How Brand Measurement Connects to RevenueBrand measurement drifted away from commercial reality when marketers decided to chase every click and impression. Guta traced this pattern back to the 1970s when companies decided to separate branding and sales into distinct functions. Before that split, teams treated branding as a sales lever that directly supported revenue. The division created two camps that rarely spoke the same language. One camp focused on lavish creative campaigns, and the other became fixated on dashboards filled with shallow metrics.Guta started her career in performance marketing because she valued seeing every dollar accounted for. She described those years as productive but ultimately unsatisfying. She moved to big enterprises and spent nearly a decade trying to make brand lift reports feel credible in boardrooms. She eventually turned her focus to startups and noticed a clearer path. Startups often have budgets that force prioritization. They pick one initiative, implement it, and measure its direct impact on revenue without dozens of overlapping campaigns.“When you only have money to do one thing, it becomes obvious what’s working,” Guta explained. “You almost get this A/B test without even planning for it.”That clarity shaped her view of brand measurement. She learned that disciplined isolation of variables makes results easier to trust. When a startup rebranded, sales moved in a way that confirmed the decision. The data was hard to ignore. Guta saw purchase volumes increase after brand updates, and she knew these signals were stronger than any generic awareness metric. The companies she worked with never relied on sentiment scores alone because they tracked actual transactions.Guta later built her own product to modernize brand research with a sharper focus on financial outcomes. She designed the system to map brand activities to revenue signals so marketing could prove its impact without resorting to vague reports. The product found traction because it respected the mindset of finance leaders and offered direct evidence that branding drives growth. Guta believed this connection was essential for any team that wants to secure resources and build trust across departments.Key takeaway: Brand measurement works best when you focus on one clear change at a time and track its impact on revenue without distractions. You can earn credibility with your finance partners by showing how brand decisions move purchase behavior in measurable ways. When you build discipline into measurement and align it with actual sales, you transform branding from a creative exercise into a proven growth lever.Examples Where Brand Investments Shifted Real Business OutcomesBrand investments often get treated as trophies that decorate a budget presentation. Guta shared a story that showed how sponsorships can drive specific business results when you track them properly. A Brazilian bank decided to sponsor a soccer championship. On the surface, the campaign looked like a glossy PR move. When Guta’s team measured what they called “mindset metrics,” they found that soccer fans reported higher loyalty toward the bank. The data set off a chain reaction that forced everyone involved to reconsider how they viewed sponsorships.The bank pulled internal reports and discovered a clear pattern. Fans who followed the soccer sponsorship churned at much lower rates than other customers. Guta said the marketing team realized they were sitting on a revenue engine they never fully understood. They began to see sponsorship as a serious retention tool rather than a vanity spend. That shift did not happen automatically. Someone had to ask whether the big brand push was connected to any measurable outcomes, and then look carefully for the link between sentiment and behavior.Guta described another client who rebranded their product suite under one name. They planned to delete the old LinkedIn pages that showed the previous brand identities. The team assumed nobody cared about those pages because LinkedIn conversions looked low in standard reports. Guta’s data proved otherwise. Those profiles accounted for more than 10% of conversions. Even though LinkedIn often buries links and limits reach, buyers visited those profiles before searching on Google and converting later.“Organic is a myth. It’s just conversions you forgot to measure.”Guta said this with the calm certainty of someone who has studied enough attribution to see where the gaps live. She explained that once you recognize how long it takes for a sponsorship impression to spark a branded search or a sale, you change how you plan. You stop guessing about campaign timing. You start working backward from the conversion window. If you expect a surge in July, you begin your campaigns in May so your budget has time to mature into real conversions instead of wasted impressions.Key takeaway: Map the path between your brand investments and your conversions with concrete data instead of assumptions. Use mindset metrics to identify early loyalty signals, then confirm whether those signals correlate with retention and branded search. When you see exactly how long each channel takes to drive revenue, you can plan campaigns months in advance and protect your budget with evidence that proves your strategy is working.The Tangible Outcomes of Brand: Purchase Intent and Memory StructuresBranding often carries a reputation as a soft layer of sentiment layered on top of performance campaigns, but Guta shares that it operates through a more rigorous mechanism than most teams realize. Branding creates memory structures that store signals in a person’s mind. When customers enter the market ready to buy, they retrieve those signals almost instantly. Their brains pull up familiar visuals, a sense of trust, or a specific promise that speeds up the choice. Guta has seen this happen repeatedly when people move straight from awareness to purchase without even visiting the company’s website again.Guta describes the reality that many marketing teams get stuck in a single-track mindset. They keep trying to hammer home immediate behaviors without any effort to create longer-term recall. She shares that brands can think about their work in two tracks running side by side:One track plants attributes in memory so customers can recall the brand later.The other track activates specific behaviors like trying, subscribing, or purchasing.When companies only focus on activation, they may end up with viral content that does not translate into any buying behavior. Guta has watched teams measure short-term engagement while ignoring whether the campaign ...

What’s up everyone, today we have the pleasure of sitting down with Chris O'Neill, CEO at GrowthLoop. Summary: Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. Chris also breaks down how reinforcement learning helps avoid a sea of sameness by letting campaigns evolve mid-flight based on live data. To support velocity without sacrificing control, top teams are running red team drills, assigning clear data ownership, and introducing internal AI regulation roles that manage risk while unlocking scale.The 2025 AI and Marketing Performance IndexThe 2025 AI and Marketing Performance Index that GrowthLoop put together is excellent, we’re honored to have gotten our hands on it before it went live and getting to unpack that with Chris in this episode. The report answers timely questions a lot of teams are are wrestling with:Are top performers ahead of the AI curve or just focused on solid foundations? Are top performers focused on speed and quantity or does quality still win in a sea of sameness?We’ve chatted with plenty of folks that are betting on patience and polish. But GrowthLoop’s data shows the opposite.🤖🏃 Top performerming marketing teams are already scaling with AI and their focus on speed is driving growth. For some, this might be a wake-up call. But for others, it’s confirmation and might seem obvious: Teams that are using AI and working fast are growing faster. We all get the why. But the big mystery is the how. So let’s dig into the how teams can implement AI to grow faster and how to prepare marketers and marketing ops folks for the next 5 years.Reframing AI in Marketing Around Outcomes and VelocityMarketing teams love speed. AI vendors promise it. Founders crave it. The problem is most people chasing speed have no idea where they’re going. Chris prefers velocity. Velocity means you are moving fast in a defined direction. That requires clarity. Not hype. Not generic goals. Clarity.AI belongs in your toolkit once you know exactly which metric needs to move. Chris puts it plainly: revenue, lifetime value, or cost. Pick one. Write it down. Then explain how AI helps you get there. Not in vague marketing terms. In business terms. If you cannot describe the outcome in a sentence your CFO would nod at, you are wasting everyone’s time.“Being able to articulate with precision how AI is going to drive and improve your profit and loss statement, that’s where it starts.”Too many teams start with tools. They get caught up in features and launch pilots with no destination. Chris sees this constantly. The projects that actually work begin with a clearly defined business problem. Only after that do they start choosing systems that will accelerate execution. AI helps when it fits into a system that already knows where it’s going.Velocity also forces prioritization. If your AI project can't show directional impact on a core business metric, it does not deserve resources. That way you can protect your time, your budget, and your credibility. Chris doesn’t get excited by experiments. He gets excited when someone shows him how AI will raise net revenue by half a percent this quarter. That’s the work.Key takeaway: Start with a business problem. Choose one outcome: revenue, lifetime value, or cost reduction. Define how AI contributes to that outcome in concrete terms. Use speed only when you know the direction. That way you can build systems that deliver velocity, not chaos.How to Use Agentic AI for Marketing Campaign ExecutionMany marketing teams still rely on AI to summarize campaign data, but stop there. They generate charts, read the output, and then return to the same manual workflows they have used for years. Chris sees this pattern everywhere. Teams label themselves as “data-driven,” while depending on outdated methods like list pulls, rigid segmentation, and one-off blasts that treat everyone in the same group the same way.Chris calls this “waterfall marketing.” A marketer decides on a goal like improving retention or increasing lifetime value. Then they wait in line for the data team to write SQL, generate lists, and pass it back. That process often takes days or weeks, and the result is usually too narrow or too broad. The entire workflow is slow, disconnected, and full of friction.Teams that are ahead have moved to agent-based execution. These systems no longer depend on one-off requests or isolated tools. AI agents access a shared semantic layer, interpret past outcomes, and suggest actions that align with business goals. These actions include:Identifying the best-fit audience based on past conversionsSuggesting campaign timing and sequencingLaunching experiments automaticallyFeeding all results back into a single data source“You don’t wait in line for a data pull anymore,” Chris said. “The agent already knows what audience will likely move the needle, based on what’s worked in the past.”Marketing teams using this model no longer debate which list to use or when to launch. They build continuous loops where agents suggest, execute, and learn at every stage. These agents now handle tasks better than most humans, especially when volume and speed matter. Marketers remain in the loop for creative decisions and audience understanding, but the manual overhead is no longer the cost of doing business.Key takeaway: AI agents become effective when they handle specific steps across your marketing workflow. By assigning agents to segmentation, timing, testing, and feedback collection, you can move faster and operate with more precision. That way you can replace the long list of disconnected tasks with a tight loop of execution that adapts in real time.How Reinforcement Learning Optimizes GenAI ContentReinforcement learning gives marketers a way to optimize AI-generated content without falling into repetition. Chris has seen firsthand how most outbound sequences feel eerily similar. Templates dominate, personalization tags glitch, and every message sounds like it was assembled by the same spreadsheet. The problem does not stem from the idea of automation but from its poor execution. Teams copy tactics without refining their inputs or measuring what actually works.Chris points to reinforcement learning as the fix for this stagnation. He contrasts it with more rigid machine learning models, which make predictions but often lack adaptability. Reinforcement learning works differently. It learns by doing. It tracks real-world feedback and updates decision-making logic in motion. That gives marketers an edge in adjusting timing, sequencing, and delivery based on signals from actual behavior.“It would be silly to ignore all the data from previous experiments,” Chris said. “Reinforcement learning gives us a way to build on it without starting over each time.”Chris believes this creates space for creative work rather than replacing it. Agents should own the tedious tasks. That includes segmenting lists, building reports, and managing repetitive logic. Human teams can then focus on storytelling, taste, and trend awareness. Chris referenced a conversation with a senior designer at Gap who shared a similar view. This designer believes AI lets him expand his creative range by clearing room for deep work. Chris sees the same opportunity in marketing. The system works best when agents handle the mechanical layers, and humans bring energy, weirdness, and originality.Many leaders are still caught in operational quicksand. Their teams wrestle with bl...