Episodios

  • 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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    1 h y 8 m
  • 185: Jonathan Kazarian: Platforms vs point solutions and the marketing operator’s dilemma

    185: Jonathan Kazarian: Platforms vs point solutions and the marketing operator’s dilemma
    Sep 2 2025
    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 BalancedSummary: 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 ...
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    58 m
  • 184: Nadia Davis: How to decide if attribution data is good enough to guide strategy

    184: Nadia Davis: How to decide if attribution data is good enough to guide strategy
    Aug 26 2025
    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 FulfillmentSummary: 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 ...
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    1 h y 9 m
  • 183: Kevin White: Building a super IC role to escape management burnout and fixing the broken promise of AI SDRs

    183: Kevin White: Building a super IC role to escape management burnout and fixing the broken promise of AI SDRs
    Aug 19 2025
    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 ReflectionSummary: 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 ...
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    1 h
  • 182: Simon Lejeune: Wealthsimple’s VP of Growth on 2 keys to be a top 5% marketer

    182: Simon Lejeune: Wealthsimple’s VP of Growth on 2 keys to be a top 5% marketer
    Aug 12 2025
    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 RolesSummary: 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 ...
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    1 h y 7 m
  • 181: Alison Albeck Lindland: Climb the AI Literacy Pyramid and Stand Out as a Customer‑First Marketer

    181: Alison Albeck Lindland: Climb the AI Literacy Pyramid and Stand Out as a Customer‑First Marketer
    Aug 5 2025
    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 CMOSummary: 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 ...
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    59 m
  • 180: István Mészáros: Merging web and product analytics on top of the warehouse with a zero-copy architecture

    180: István Mészáros: Merging web and product analytics on top of the warehouse with a zero-copy architecture
    Jul 29 2025
    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 KidsSummary: 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 ...
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    59 m
  • 179: Tiankai Feng: The comeback of data quality and how NLP is changing the data analyst role

    179: Tiankai Feng: The comeback of data quality and how NLP is changing the data analyst role
    Jul 22 2025
    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) - HappinessSummary: 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, ...
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