The MarTech Matrix

Hosted by Sean Simon

Business Marketing

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Aug 2026

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The MarTech Matrix Podcast is dedicated to helping brands and agencies discover technology without the hassles and time commitment of lengthy sales calls. There are over 17k MarTech solution on the market, in dozens of categories. Finding the right, best solution can take months from the beginning of the search until selection. This podcast, it’s content, and our platform are designed to help expedite the entire process because time is money and neither is more precious than the other.

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August 16, 2026Episode 5250 min

Why Your Marketing Attribution Numbers Never Agree

Last-click says one thing. Your CFO doesn't believe it. Your incrementality test says something else entirely. None of those numbers are wrong — they're just not measuring the same thing. In this episode of Inside the Blurb, I sit down with Madan Bharadwaj, founder and CEO of M-Squared. Madan has built four companies inside the attribution and measurement space, including Visual IQ (one of the first multi-touch attribution platforms) and Measured (the industry's first pure-play incrementality company). We cover: -Why nearly everything brands use to measure marketing is "correlational" and carries built-in bias . -Why incrementality testing is deceptively hard to run well -Triangulation — turning conflicting attribution, MMM, and incrementality numbers into one multiplier and one decision -A real story about an AI model recommending a brand cut $25M in Amazon ad spend, and why it was wrong -Why causal data has to be built, not mined -Who actually needs this level of measurement (and who doesn't) Read the full breakdown + key takeaways: www.themartechatrix.com Check out M-Squared on Blurbs: https://www.trustblurbs.com TIMESTAMPS 00:00 — Introduction: welcome to the Matrix 01:00 — Madan's origin story: from computer science to a $10M/month PPC bidder 07:01 — 2005 vs. 2026: what's changed in measurement, and what hasn't 10:55 — How GDPR, CCPA, and walled gardens shrank trackable behavior 13:07 — Why brands still lean on last-click even though it's incomplete 16:10 — Why incrementality testing sounds simple and is actually hard 18:30 — Designing a "feasible test" before you run it 20:07 — Do marketers use data to confirm what they already believe? 24:42 — Why the best marketers combine data with business instinct 26:45 — Causal vs. correlational, defined 30:08 — What makes M-Squared different 33:51 — Triangulation, explained 38:38 — The causal insights program 41:38 — Who actually needs advanced measurement 42:58 — Why AI can't manufacture causal data 44:07 — The $25M Amazon ad spend story 46:56 — Why domain expertise still matters 47:10 — Squared Collective: next workshop September 10, Boston 49:10 — Closing thoughts ABOUT INSIDE THE BLURB Inside the Blurb is a podcast by The MarTech Matrix where Sean Simon interviews founders and leaders from the vendor side of marketing technology. Website: https://www.themartechmatrix.com Blurbs: https://www.trustblurbs.com LinkedIn: https://www.linkedin.com/in/madanbharadwaj/

July 27, 2026Episode 5135 min

Why Ecommerce Re-platforming Fails

If you've been part of an ecommerce replatforming project, you already know the feeling. Six months of your life disappear. Your team can't think about anything else. And at the end of it, you're not even sure the new platform is meaningfully better than the one you left. Steve Deller has watched this story play out for 25 years. As CEO of Virid (https://www.virid.com) — an ecommerce platform and agency that has been building and running enterprise retail sites since the late 1990s — he's seen what works, what fails, and why brands keep making the same mistakes when they replatform. In this episode of Inside the Blurb, Steve breaks down unified commerce, explains why integration debt is the real enemy, and shares the questions every brand should be asking — but usually isn't — before they sign with a new platform vendor. In this episode, we get into: - What unified commerce actually means — and why it's different from headless and composable - Why 60-70% of Virid's clients came from failed implementations on other platforms - How Journeys (the shoe retailer) manages real-time inventory across hundreds of stores every 5 minutes - The 6 questions every brand should ask before signing with a new ecommerce platform - What to do before you even start shopping for a new platform - How AI is reshaping ecommerce — and why Steve calls AI shopping "just another channel" Whether you're evaluating platforms, knee-deep in a replatforming project, or trying to understand what unified commerce means in practice, this conversation will help. TIMESTAMPS 00:00 — Introduction: Steve Deller and Virid's origin story 01:13 — From monolithic to headless to composable — the ecommerce evolution 01:33 — What unified commerce actually means 03:11 — Why ecommerce replatforming feels like a 6-month nightmare 03:49 — The data migration problem explained 05:35 — Virid vs. Shopify — knowing which fits your business 07:32 — Real-world example: Journeys and real-time omnichannel inventory 08:53 — Who is Virid's ideal client? 10:34 — The agency + platform model explained 12:21 — What the team looks like during a migration 13:50 — The "one check" model and why it works 15:18 — How AI is already integrated into Virid's platform 17:28 — The future of SaaS: will AI replace admin dashboards? 19:52 — The compute cost and data center conversation 21:14 — What post-launch partnership looks like (weekly calls, QBRs, annual visits) 23:22 — AI shopping: will consumers buy through Claude and ChatGPT? 26:27 — What triggers a brand to start looking for a new platform 30:00 — The 6 questions brands forget to ask platform vendors 33:31 — What to do before you start shopping for a new platform 35:27 — What's ahead for Virid in the next 12-18 months 37:17 — Final thoughts and wrap-up

July 27, 2026Episode 5042 min

Your Marketing Data is Lying to You!

The problem isn't that your marketing isn't working. The problem is you can't prove it. Two years before Pranav Piyush — CEO of Paramark — ever bought anything from True Classic, he was seeing their ads everywhere. YouTube. Instagram. The mall. He laughed at their videos, moved on, and kept shopping at Uniqlo. Then Uniqlo lost him as a customer, and True Classic was the first brand that came to mind. No last-click attribution model captured that. No multi-touch dashboard told True Classic that their patience had paid off. The click that triggered the purchase looked like it came from an Instagram ad — but the real conversion happened over two years of brand building. "If you're the growth marketing manager at True Classic," Pranav told us on Inside the Blurb, "you're optimizing to the wrong signal." That's the problem Paramark is built to solve. What you'll learn: → The difference between marketing "spend" and marketing "investment" — and why it matters → What incrementality testing actually is (it's not just holdout tests) → How Marketing Mix Modeling (MMM) works alongside experimentation → Why a great dashboard is only 20% of the measurement problem → What AI can and can't do for measurement right now → What to look for when evaluating measurement vendors → Where measurement and media buying are headed in the next 2 years --- Guest: Pranav Piyush, CEO & Co-Founder of Paramark Paramark on Blurbs: https://trustblurbs.com Paramark website: https://paramark.com --- TIMESTAMPS 0:00 — Introduction: Who is Pranav Piyush? 0:41 — Paramark's Blurb and the Growth Advisory Services differentiator 1:58 — Why Growth Advisory is the 80% that most vendors skip 4:33 — Marketing spend vs. marketing investment — the mindset shift 5:48 — How Pranav's career at PayPal, Dropbox, and Magento led to Paramark 9:33 — Why marketers can't make decisions in a vacuum 10:29 — The True Classic story: 2 years of ads, no click, then loyalty 12:56 — The CeraVe / Michael Cera Super Bowl example 14:17 — Did multi-touch attribution ever actually work? 16:17 — How Paramark would explain measurement to a CMO at CeraVe 19:13 — How AI is changing consumer behavior and ad measurement 21:20 — Will AI replace the math in MMM and incrementality testing? 23:50 — Live walkthrough of Paramark's dashboard and experimentation view 31:10 — Why garbage data = garbage insights (and how to fix it) 33:36 — How to design and analyze a geo-pulsed TV incrementality test 36:13 — The virtuous cycle: experiment → MMM → scenario planning → repeat 37:39 — What good data architecture looks like (with a Shopify example) 38:33 — How Pranav would evaluate vendors if he were a buyer today 39:17 — The 3 things Pranav doesn't love about buying MarTech 41:48 — How Blurbs is trying to fix vendor-buyer communication 43:52 — The future: measurement + media buying convergence 45:12 — Is autonomous media buying coming in 2 years? 46:28 — Why humans still need to be in the loop 47:00 — What Pranav wishes he knew at the start of his career

July 5, 202642 min

What Vendors Get Wrong — A Fractional CMO’s Honest Take on MarTech Sales

Most vendor outreach fails before it's even opened. And most demos lose the deal before the second slide. In this episode of The Human Side of MarTech, Sean Simon sits down with Chris Seahorn, Co-founder of Boundless Digital — a fractional CMO agency he started two years ago with a longtime colleague after 25+ years in digital marketing at companies like eBags, MapQuest, Naked Wines, and others. Chris evaluates MarTech stacks across multiple clients simultaneously. He has an unusual vantage point: he sees what vendors do well, what kills deals, and what separates implementations that stick from ones that quietly die at renewal time. In this conversation: → The StoryBrand framework: why "your customer is the hero" changes everything about how you sell → What makes Chris immediately delete a cold email (and what gets a meeting) → The "kitchen sink demo" problem — and how to fix it before you walk in the room → How a fractional CMO evaluates a client's tech stack from day one → Why conversion rate optimization is always the first quick win (and how 5% improvements stack to 25–30% total lift) → The most common checkout conversion killers: coupon confusion, account creation walls, and traffic mismatches → Why SaaS implementations fail — and it's almost never the product → AI customer service agents: why they went from "worse than useless" to genuinely impressive → The self-checkout analogy and what it tells us about AI adoption timelines → What Chris would tell his 1999 self: "Progress over perfection — trust your gut, but come with the receipts" 0:00 — Introduction: Chris Seahorn and Boundless Digital 1:30 — Chris's 25-year journey: eBags, MapQuest, Naked Wines, startups 3:30 — Why Chris left the CMO seat to go fractional 5:30 — What signals to Chris that a vendor actually understands his world 8:00 — The StoryBrand framework: hero, guide, problem, plan 11:00 — The "creep factor" — when vendor research goes too far 13:30 — Getting from inbox to meeting: breaking through the clutter 16:00 — What makes a good vs. bad vendor demo 19:30 — The kitchen sink problem: analysis paralysis in presentations 22:00 — Why use cases and follow-ups close more than the deck does 24:30 — How a fractional CMO walks into a new client 27:00 — The first assessment: looking at data before charging anything 30:00 — Evaluating a tech stack: measurement first, then fundamentals 33:00 — Email/SMS, CRO, and A/B testing as the foundation 36:00 — Conversion rate: the tide that lifts all ships 39:30 — Why 5% improvements on key pages beat swinging for 50% 42:00 — The most common conversion killers (coupon confusion, account walls) 45:30 — Seasonal behavior: why November cart abandonment is actually a good sign 48:00 — The tire store checkout story — friction that kills conversions 51:00 — Are consumers actually changing their behavior with AI? 54:00 — AI recommendation engines and what winning e-commerce brands do 57:00 — AI customer service agents: from worse-than-useless to genuinely impressive 60:00 — The self-checkout analogy and AI adoption timelines 63:00 — Why SaaS implementations fail: no champion, no success definition 66:00 — Chris's advice to vendors: step out of your own shoes 68:00 — The case for trials and reduced-rate pilots 70:00 — Advice to his early-career self: progress over perfection

June 25, 202630 min

How a DTC Operator Evaluates MarTech

Most brand marketers talk about what tools they use. Sean Agatep talks about why everything has to earn its place on the P&L. Sean is the co-founder and operator of Vincero Watches — a brand he launched in China with his college friends in 2014. From Kickstarter experiments to Facebook mastery to navigating the AI era, he's built one of the most disciplined approaches to marketing technology in the DTC space. In this episode, we go deep on: → Why Vincero used Kickstarter as a marketing testing platform — not a fundraiser → How mastering one channel (Facebook) built a sustainable competitive advantage → The real framework for evaluating vendors when you own the P&L → Why aspirational case studies are the fastest way to lose a deal → His "holes in the boat" approach to vendor relationships → Where AI is actually adding value — and where it's still noise If you sell to brands or buy MarTech for a living, this one is required listening. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 00:00 The Origin Story of Vincero 04:03 Kickstarter as a Marketing Platform 06:40 Scaling the Business: Lessons Learned 09:37 Evaluating Marketing Technologies 12:50 Vendor Relationships and Evaluation 15:52 The Role of AI in Business 18:37 Staying Ahead in a Competitive Market 21:55 Future Trends and Innovations ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ KEY TAKEAWAYS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. Evaluate tech against your most pressing needs — not what's trending 2. Deep channel expertise creates a defensible edge. Go wide too early and you lose both. 3. Vendors: know the brand's weight class. Stop pitching logos they can't relate to. 4. Lean tech stacks move faster. Over-investment in fixed solutions kills flexibility. 5. AI adoption is a habit problem. Focus on getting your team using it — not finding the perfect tool. 6. The best vendor relationships are built long before the deal. Get on the shortlist.

June 25, 202650 min

Dear Mr. Vendor: Lead With the Problem You Solve

Most people in MarTech have sat on one side of the table. Thomas Mercier has sat on all of them. Vendor. Agency. Brand. Publisher. Consultant. From Quantcast to OMD to Activision to WildBrain — Thomas has seen how technology gets bought, sold, evaluated, and ignored from every angle. That makes his take on vendor pitches, ethics, and sustainability unlike almost anyone else's. In this episode, we go deep on: → The #1 thing vendors still get wrong (and have always gotten wrong) → Why credibility is a two-way street — buyers have work to do too → How Thomas built a sustainability framework at Activision that cut carbon emissions 30% without hurting ROAS → What ethics in advertising actually means — beyond compliance → The microphone activation story every vendor should hear → How Gen Z is rewiring brand loyalty around ethics, not punch cards → His single best piece of advice for every sales rep If you sell to brands or evaluate vendors for a living, this one is required watching. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 00:00 The Unintentional Career Path 02:12 Mind Shifts in Role Transitions 04:17 Understanding Client Complexities 06:51 The Importance of Relevancy in Sales 08:43 Ethics in Children's Media 12:43 Navigating Ethical Challenges in Advertising 16:49 The Process of Vendor Evaluation 19:51 Managing Vendor Relationships 23:35 Establishing Credibility with Vendors 26:53 Mutual Respect in the Industry 28:10 The Importance of Transparency and Trust 29:40 Shifting Consumer Loyalty and Brand Ethics 36:20 Understanding Sustainability in Marketing 44:17 Leveraging AI for Efficiency 46:54 Advice for Sales Reps: Building Relationships ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ KEY TAKEAWAYS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. Lead with the problem you solve — not the product you can sell 2. Credibility goes both ways. Buyers need to show up prepared too. 3. Buyers are more organizationally complex than they appear from the outside 4. Ethics is a stricter filter than legal compliance 5. Sustainability is now a real tiebreaker in vendor selection — at equal benefits 6. Be transparent about what your product DOESN'T do 7. Treat vendor relationships like partnerships, not transactions 8. Block calendar time for vendor discovery — it's part of the job 9. Consumer loyalty is shifting. Ethics and sustainability are the new moat. 10. Dare to ask the question — most people in the room have the same one

June 25, 20261 hr 3 min

The Keys to Enterprise AI Data Integration

The data warehouse model is 60 years old — and AI is about to expose every flaw in it. In this episode of Inside the Blurb, Sean Simon sits down with Kyle Csik, CEO and co-founder of Adaly, to get into what's actually broken about how enterprises handle data, why marketing has been set up to fail from the start, and what a genuinely different model looks like. Kyle spent 15 years on every side of adtech — exchange, DSP, publisher, agency — watching marketers fight with one hand tied behind their back. Adaly is his answer to a problem he's been watching compound for over a decade: you can't run modern AI on infrastructure that was designed for human analysts in the 1960s. In this conversation: → The Napster vs. Spotify analogy that explains why data warehouses can't support AI → Why marketing has been "operating with both arms tied behind its back" — and who's really at fault → How Adaly eliminates data copies (90% of all data is copies of other data) and inherits existing RBAC security → The crawl-walk-run adoption strategy for getting enterprise IT buy-in without a day-one rip-and-replace → How real-time supply chain data let one client plan media 12 months ahead of the market → The RFP team that went from "not confident in what we sent" to "making more money and standing behind our work" → "Adaly Terminal" — the system that tells you what questions you haven't thought to ask → The one question that defined the company: "Do you want to keep preparing to work or do you want to just get to the work?" TIMESTAMPS 0:00 — Introduction: Kyle Csik and Adaly 2:00 — Kyle's origin story: biological computers, physics, and adtech 6:00 — The moment adtech clicked — walking into one of the first biddable exchanges 9:00 — The Napster problem: why data warehouses were built for a world that no longer exists 14:00 — IBM → Oracle → Teradata → Snowflake: 60 years of the same broken model 17:00 — Marketing's dirty secret: both arms tied behind its back 22:00 — The 64% shelf-space stat that shows what marketing is missing 25:00 — Adaly's architecture: connecting to source systems instead of copying data 29:00 — RBAC inheritance: why CIOs love the security model 32:00 — 90% of all data is copies — and every copy is a new security risk 35:00 — The crawl-walk-run adoption strategy 38:00 — Real-time data use case: crop yields, media planning, and 12-month advantage 42:00 — Connector depth: 81 Salesforce APIs vs. first-page search results 45:00 — The RFP team that started standing behind their work 49:00 — Adaly Terminal: the system that asks what you haven't thought to ask 52:00 — The $1M/month "Netflix subscription" a client didn't know they had 55:00 — The Tesla Optimus robot that couldn't find the Coke 58:00 — Model-agnostic portability: your data estate travels with you 61:00 — The one question that built the company 64:00 — How to get started with Adaly 🎙️ Inside the Blurb is part of The MarTech Matrix — a podcast network for people who live and breathe marketing technology.

February 5, 2026Episode 1036 min

Inside the Blurb with Insighta

Summary In this conversation, Sean Simon and Matthew Liu delve into the intricacies of customer intelligence and how brands can leverage behavioral data to make informed marketing decisions. They discuss the methodology behind Insighta, a platform designed to help marketers understand their data, optimize ad spend, and drive growth. Matthew shares insights on the importance of predictive lifetime value, the challenges of multi-touch attribution, and the role of AI in marketing. The discussion also highlights the onboarding process for Insighta and the impact of data-driven strategies on brand success, illustrated through a case study with Obagi. Takeaways Marketers have access to vast amounts of customer data, but much of it remains underutilized. Insighta focuses on understanding the cost of acquiring customers over time, rather than just immediate returns. The platform is particularly beneficial for brands in growth phases with significant ad spend across multiple channels. Insighta's methodology combines various marketing measurement techniques into a unified approach. Actionability of data is crucial for marketers to make informed decisions. The predictive lifetime value feature helps brands identify long-term growth opportunities. Case studies, like that of Obagi, demonstrate the effectiveness of Insighta's strategies in driving new customer acquisition. Understanding customer journeys can extend back hundreds of days, providing valuable insights into purchasing behavior. Brands should seek transparent partnerships in measurement to ensure accurate data interpretation. AI is increasingly integrated into marketing tools, but its application is still evolving. Sound bites "What did it cost me to get that?" "It's like activity-based costing." "Actionability is a key component." Chapters 00:00 Introduction to Customer Intelligence 02:41 Understanding Insighta's Methodology 05:34 When to Use Insighta 08:19 What Makes Insighta Remarkable 10:52 The Role of Data in Marketing Decisions 13:32 Navigating the Measurement Space 16:16 Onboarding and Support with Insighta 18:33 The Impact of Predictive LTV 21:12 Case Study: Obagi's Success 24:00 Lifetime Value for New Brands 26:20 Client Engagement and Analytics 29:13 The Future of AI in Marketing 31:39 Pricing Models and Considerations 34:08 Final Thoughts on Measurement Strategies 36:31 The New MarTech Matrix Outro ‑ Made with FlexClip.mp4

December 11, 2025Episode 933 min

The Evolution of Creator Content

Marketers talk about content like it’s oxygen, but most teams are still short of breath. Budgets are tighter, channels keep multiplying, and the demand for high-performing creative never slows down. That’s the backdrop for my conversation with Tom Logan, CEO of Cohley , on Inside the Blurb . Cohley sits at the intersection of creators, AI, and operations, helping mid-market and enterprise brands turn user-generated content into a real, repeatable advantage. Key Takeaways Brands don’t just need more content—they need a content engine. Cohley is built to power content across the entire consumer journey, not just one-off campaigns. Cohley is built for mid-market and enterprise consumer brands. Below ~$10M in revenue, most brands don’t yet feel the full intensity of the content problem Cohley solves. Creator matching is data-driven, not just a marketplace free-for-all. Cohley uses deep creator data and workflows to prioritize fit and quality over volume. AI is embedded in the workflow, not bolted on. Tools like AI Asset Analysis and Cohley Cognition learn brand preferences, flag off-brief content, and guide briefs over time. Perpetual content rights remove a massive operational headache. Brands own their assets forever, avoiding complex usage windows and “this ad is working but we’re out of rights” moments. Customer success is a strategic function, not just support. Dedicated CSMs provide channel-specific content strategy, quarterly check-ins, and in-person relationship building. Pilots de-risk adoption for the right brands. 90-day pilots with flexible brief structures let Cohley prove value before a long-term commitment. Chapters 00:00 – Why content feels like oxygen (but teams can’t breathe) 00:55 – Meet Cohley: Sean reads the Blurb 01:12 – Why brands have never needed this much content 02:46 – Who Cohley is really for (and who it isn’t) 04:35 – From early UGC to building Cohley 06:36 – Beyond point solutions: powering the whole journey 07:17 – Cohley vs competitors: where they truly differ 09:08 – Using AI to enforce creative “non-negotiables” 11:16 – Why customer success is Cohley’s backbone 13:41 – Diversity of content and creator matching at scale 15:19 – Who gets into the creator network (and how it self-regulates) 17:51 – Perpetual rights and killing usage-tracking headaches 19:31 – Case Study: Zak Designs and content for every touchpoint 22:55 – Which verticals Cohley wins in (and which are harder) 24:17 – What working with Cohley actually looks like 27:56 – How brands measure success with Cohley content 31:31 – Inside Cohley Cognition: the AI brain 34:33 – Distributing content across Amazon, TikTok, Yotpo & more 36:18 – Pricing, pilots, and de-risking the decision 37:50 – How to explore Cohley on Blurbs & what’s next

December 5, 2025Episode 832 min

The Apparel Industry’s $100 Billion Fit Problem

In this episode of The MarTech Matrix , Sean Simon sits down with Daina Burnes , CEO & Co-Founder of Bold Metrics , to explore how AI-driven fit intelligence is transforming apparel commerce. Daina shares the origin story of Bold Metrics, how the company predicts over 50 body measurements using simple customer inputs, and why fit uncertainty remains the biggest reason shoppers fail to convert — and the biggest driver of apparel returns. We dive into the economics of returns, the limitations of static size charts, and why size confidence should be considered a performance lever, not a UX enhancement. Daina also looks ahead to the next 3–5 years, where fit technology evolves into a multimodal, context-aware personalization layer that blends body data, climate, lifestyle, and purchase behavior. If you lead eCommerce, merchandising, or personalization for an apparel brand, this episode is essential listening. Top Takeaways 60–70% of apparel returns are caused by fit — the #1 margin leak in the industry. Bold Metrics predicts 50+ body measurements without photos, scanners, or measuring tapes. Fit intelligence is a conversion driver, not a UX enhancement. Static size charts underperform compared to intelligent size guidance. The next era of fit tech will merge personalization, digital identity, and predictive merchandising. Fit systems will become multimodal: climate, lifestyle, body data, and style preferences. Apparel brands can significantly reduce returns by arming shoppers with pre-purchase fit clarity. The industry’s shift will move from “What size?” to “What fits me?” Chapters 00:00 — Intro & Who Is Bold Metrics? 02:15 — The Origin Story: FashionMetric 06:40 — Master Tailoring Meets Machine Learning 10:25 — How Bold Metrics Predicts Body Measurements 12:30 — Why Fit Is the #1 Conversion Killer in Apparel 14:15 — The Economics of Returns 17:50 — Size Confidence as a Performance Lever 21:05 — Why Static Size Charts Fail 25:35 — The Future of Fit Intelligence (Multimodal + Context Aware) 29:10 — Fit as a Core Layer of Personalized Commerce 32:00 — Advice for Apparel Leaders 35:00 — Closing Thoughts