Humans of Martech Podcast Por Phil Gamache arte de portada

Humans of Martech

Humans of Martech

De: Phil Gamache
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Future-proofing the humans behind the tech. Follow Phil Gamache and Darrell Alfonso on their mission to help future-proof the humans behind the tech and have successful careers in the constantly expanding universe of martech.©2026 Humans of Martech Inc. Economía Exito Profesional Marketing Marketing y Ventas
Episodios
  • 212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science

    212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science
    Mar 24 2026
    Summary: Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima. It also fuels his skepticism of unleashing agentic AI on historical data without a causal layer. If you want to change outcomes instead of forecast them, your systems need to understand levers and log decisions you can actually audit.(00:00) - Intro (01:22) - In This Episode (04:07) - Why Predictive Models Fail Without Causal Inference (09:49) - How to Validate Causal Impact on Customer Lifetime Value (13:04) - Reducing Uncertainty Around Causal Effects by Optimizing Levers, Not Labels (17:01) - Why Dynamic Allocation Works Better Than Fixed Horizon A B Testing (31:54) - The Boomerang Effect and Why Uninformed AI Sabotages Early Results (40:15) - Escaping Local Maxima and The Failure of Randomly Initialized Decisioning (44:04) - Why Agentic AI Trained on Data Warehouse Correlations Reinforces Bias (49:00) - The Power of Composable Decisioning (53:06) - How Machine Decisioning Transcends Marketing (01:01:41) - Why Clear Priority Hierarchies Improve Executive Decision MakingAbout TobiasTobias Konitzer, PhD is VP of AI at GrowthLoop, where he’s chasing closed-loop marketing powered by reinforcement learning, causality, and agentic systems. He’s spent the past decade focused on one core problem: moving beyond prediction to actually influencing outcomes.Previously, Tobi was Chief Innovation Officer at Fenix Commerce, helping major eCommerce brands modernize checkout and delivery with machine learning. He also founded Ocurate, a venture-backed startup that predicted customer lifetime value to optimize ad bidding in real time, raising $5.5M and scaling to $500K+ ARR before its acquisition. Earlier, he co-founded PredictWise, building psychographic and behavioral targeting models that drove over $2M in revenue.Tobi earned his PhD in Computational Social Science from Stanford and worked at Facebook Research on large-scale ML and bias correction. Originally from Germany and based in the Bay Area since 2013, he writes frequently about causal thinking, machine decisioning, and the future of marketing.Why Predictive Models Fail Without Causal InferencePrediction dominates most marketing roadmaps. Teams invest months refining churn models, tightening confidence intervals, and debating which threshold deserves a campaign. Tobi built an entire company on that logic. His team produced highly accurate lifetime value predictions using deep learning and granular event data. The forecasts were sharp. The lift curves were clean. Buyers were impressed.Then lifecycle marketers asked a more uncomfortable question: what action should follow the score?A predictive model encodes the current trajectory of a customer under existing policies. It describes what will likely happen if nothing changes. Marketing changes things constantly. The moment you intervene, you alter the system that generated the prediction. The forecast reflects yesterday’s conditions, not tomorrow’s strategy.> “Prediction tells you the future if you do nothing. Causation tells you how to change it.”Consider the Prediction Trap.On the left, the status quo labels a person as high churn risk. The function is observation. The outcome is a description of what happens if you leave the system untouched. On the right, a lever gets pulled. The function is intervention. The outcome is directional change.That shift in function changes how you work.Prediction thinking centers on segmentation:Who is likely to churn?Who is likely to buy?Who looks like high LTV?Causal thinking centers on levers:Which incentive reduces churn?Which sequence increases repeat purchase?Which offer raises lifetime value incrementally?Tobi often uses an LTV example to expose the trap. Suppose high LTV customers frequently viewed a specific product early in their journey. A team might redesign the onboarding flow to feature that product more aggressively. The correlation looks persuasive. The causal effect remains unknown.Several alternative explanations could drive the pattern:The product may correlate with a specific acquisition channel.The product may have been highlighted during a limited campaign.The product view may signal prior brand familiarity.Only an intervention test can estimate incremental impact. Correlation can guide hypothesis generation, but it cannot validate the lever itself.Tobi also highlights a deeper issue. Acting on predictions introduces compounding uncertainty across multiple layers:The predictive model carries statistical variance.The translation from model features to campaign strategy introduces interpretation bias.The ...
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    1 h y 5 m
  • 211: Jenna Kellner: Overcoming frankenstacks and AI uncertainty with first principles and business judgement

    211: Jenna Kellner: Overcoming frankenstacks and AI uncertainty with first principles and business judgement
    Mar 17 2026
    What’s up everyone, today we have the pleasure of chatting with Jenna Kellner, VP Marketing at Workleap.(00:00) - Intro (01:14) - In This Episode (04:30) - How to Manage Marketing Tech Debt During Rapid Growth (10:10) - How to Prioritize RevOps Tech Debt Without Perfect ROI Models (14:23) - Reasoning Through Broken Systems and Imperfect Data (19:23) - How High Performers Progress Anyway (24:28) - How to Build Confidence With AI Through Small Experiments (33:06) - How to Use Exit Planning and Cost Benefit Analysis for AI Tool Selection (35:57) - First principles matter more than tools (38:59) - Why Staying Close to Execution Improves Marketing Leadership (45:13) - Why Critical Thinking Skills Drive Marketing Career Growth (49:33) - How to Build Business Judgment in Technical Marketing Roles (53:03) - Why Confidence Without Humility is Dangerous (55:47) - How Revenue Leaders Prioritize Daily Energy (59:49) - Growing up (01:01:10) - Book recSummary: Jenna is a VP of marketing that can talk about the weeds of messy systems, uncertain decisions, and personal growth. You can’t hide from it, every company accumulates tech debt as teams rush to hit revenue targets. She frames tech debt as a leadership responsibility and urges executives to reinvest in core systems when patchwork begins to outweigh building. If leadership doesn’t get it, the best way to prioritize it is to shape it as an opportunity cost and lost leverage that will drain revenue the longer we wait. In the face of AI uncertainty, she argues that judgment compounds faster than technical knowledge, and that the marketers who become indispensable blend business awareness, proximity to execution, and decisive action grounded in humility.About JennaJenna Kellner is Vice President of Marketing at Workleap and a revenue-focused marketing leader who has spent more than a decade building marketing teams and scaling companies. She brings experience across Enterprise, SMB, D2C, SaaS, two-sided marketplaces, venture studios, and other high-growth environments.Her career spans senior leadership roles at Minerva, On Deck, RBCx, and Ownr, where she led marketing, growth, and revenue functions inside complex, evolving organizations. At RBCx, she served as Chief Growth Officer for Ampli and directed marketing and growth initiatives within a large financial institution setting. She has also co-founded communities such as GrowthToronto and Little Traders, reflecting her commitment to building networks and businesses in parallel.Jenna operates with a strong sense of ownership and accountability, grounded in her belief that every challenge ultimately becomes her responsibility to solve. Recognized as a WXN Top 100 Women in Canada, she focuses on developing high-performing teams that connect strategy to execution and translate marketing into measurable revenue impact.The Frankenstein Reality of Managing Tech Debt: How to Manage Marketing Tech Debt During Rapid GrowthYou know it.. Most marketers are operating inside half-connected systems. No company has a pristine, perfectly synchronized tech stack. Even if they think they do, it doesn’t last. Growth creates pressure, and pressure produces shortcuts. Jenna has seen the same cycle in startups and enterprise environments. In the early days, teams build whatever gets the job done. They start in spreadsheets, layer on point solutions, wire tools together with lightweight integrations, and move fast because revenue matters more than architecture.Those early decisions never disappear. They compound. Years later, larger organizations inherit layers of systems that were added at different stages of maturity. Tools do not scale in sync. One platform gets upgraded. Another stays frozen because a team depends on it. Reporting becomes an exercise in orchestration. Jenna recalls walking into an organization where a sales leader pulled her weekly report from eight separate tools. That routine consumed time, drained energy, and normalized operational friction.“You have to Frankenstein your way through them to get the answers you need.”That sentence captures the daily reality inside many marketing and revenue teams. Quarter-end reporting still happens. Board decks still go out. The numbers get assembled through exports, CSV files, manual joins, and late-night reconciliation. Leadership often tolerates the strain because revenue continues to land. But the cost isn’t super visible:Reporting cycles stretch longer each quarter.Forecast confidence erodes.Team morale dips as manual work expands.Strategic decisions rely on partial or inconsistent data.So how do we get out of this mess? Jenna views this as a leadership obligation. Someone has to decide that cleaning house earns priority alongside pipeline generation. She describes working with a founder who paused other initiatives to repair core systems. The work moved slowly. It required budget discipline and uncomfortable trade-offs. It rebuilt trust in data and freed ...
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    1 h y 2 m
  • 210: Ronald Gaines: 6 Things the next generation of marketing ops leaders must learn

    210: Ronald Gaines: 6 Things the next generation of marketing ops leaders must learn
    Mar 10 2026
    What’s up folks, today we have the pleasure of sitting down with Ronald Gaines, Digital Transformation & Marketing Ops Leader at Sunbelt Rentals, Inc.(00:00) - Intro (01:12) - In This Episode (06:18) - 1. Learning to Operate Without Formal Authority (13:59) - 2. Stop Waiting for the Org to Define Your Marketing Ops Role (22:53) - 3. The Hidden Cost of Self Taught Ops and Minimum Viable Discipline (31:46) - 4. Thinking in Products Instead of Tasks (39:15) - 5. Data Discipline Outlasts Any Platform (48:38) - 6. How to Design a Marketing Ops Intake Process That Protects Team Capacity (52:18) - Personal Energy Allocation Framework For Marketing Ops LeadersSummary: Ronald shares a framework for marketing operations leaders to move from reactive support into proactive systems authority by building influence through measurable credibility, structured intake processes, and disciplined governance. It argues that operational work should be managed like a product with clear boundaries, documented standards, and strong data discipline, which protects team capacity, prevents burnout, and makes impact visible to the business. By defining their own role and communicating value in commercial terms, operators convert technical execution into durable strategic leverage.About RonaldRonald Gaines is a Digital Transformation and Marketing Operations leader who builds scalable revenue engines across complex enterprise environments. He combines strategic direction with hands-on expertise in marketing automation, data architecture, analytics, and customer experience optimization.As Senior Manager of MarTech and Data Analytics at Sunbelt Rentals, he leads the enterprise martech roadmap, governs lead management and data integrity, and aligns marketing technology with measurable revenue outcomes. His experience across Cisco, Dell, and global consulting engagements reflects a consistent focus on operational rigor, system design, and performance-driven growth.Outside of work, Ronald is a dedicated fan of comic books and graphic novels, with a particular appreciation for mech stories and towering kaiju battles. He is also launching a nonprofit focused on building youth leaders and strengthening communities, speaks at career days to introduce young people to digital marketing, and is committed to serving families and helping the next generation build a path toward a thriving, stable quality of life.1. Learning to Operate Without Formal AuthorityMarketing ops leaders operate at the center of execution. Campaigns depend on them for tracking, lifecycle depends on them for clean product data, and growth teams depend on them for accurate reporting. Work flows through their systems every day. Authority often sits somewhere else.We describe this tension as an authority paradox. You touch everything. You own very little. Influence becomes the mechanism that moves work forward.Ronald believes influence grows from operational credibility. Ops leaders who become indispensable demonstrate rigor and produce dependable outcomes with quantifiable business impact. They can show how their work reduces launch time, decreases system incidents, improves data accuracy, or drives measurable revenue lift. When the numbers are visible, stakeholders treat the function differently.“If you cannot quantify the work that you’re doing for the business and the impact that it is making, it becomes very hard to have the influence and authority you need to push back and protect your bandwidth.”That perspective shifts the conversation from personality to proof. Relational influence still matters. Cross functional trust smooths collaboration. Operational influence carries more weight because it compounds. When a team consistently delivers outcomes that are measured and shared, credibility grows with each cycle.Ronald points to structure as the starting point. A centralized intake process creates visibility and discipline. A mature intake process includes:A required business outcome for every request.An estimated level of effort based on real sizing.A defined metric tied to revenue, cost savings, risk reduction, or speed.A transparent prioritization rubric that stakeholders can review.When every request moves through this filter, conversations become sharper. Trade offs move from hallway debates to documented decisions. You protect capacity because the impact is visible. You prioritize high value work because the math supports it.He also encourages ops leaders to create formal deliverables that showcase impact. Publish a quarterly ops impact report. Share a dashboard that tracks launch velocity. Track incident reduction over time. Circulate a capability roadmap tied to revenue targets. These artifacts signal accountability. Accountability grants the authority to set priorities and allocate resources.Influence grows when stakeholders associate your involvement with consistent business gains. Teams start asking for your perspective earlier in the planning process...
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    59 m
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