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.

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CHAPTERS

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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

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KEY TAKEAWAYS

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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.

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.

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CHAPTERS

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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

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KEY TAKEAWAYS

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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

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.

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

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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Customer success is a strategic function, not just support.

   Dedicated CSMs provide channel-specific content strategy, quarterly check-ins, and in-person relationship building.

7. 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

   1. 00:00 – Why content feels like oxygen (but teams can’t breathe)

   2. 00:55 – Meet Cohley: Sean reads the Blurb

   3. 01:12 – Why brands have never needed this much content

   4. 02:46 – Who Cohley is really for (and who it isn’t)

   5. 04:35 – From early UGC to building Cohley

   6. 06:36 – Beyond point solutions: powering the whole journey

   7. 07:17 – Cohley vs competitors: where they truly differ

   8. 09:08 – Using AI to enforce creative “non-negotiables”

   9. 11:16 – Why customer success is Cohley’s backbone

   10. 13:41 – Diversity of content and creator matching at scale

   11. 15:19 – Who gets into the creator network (and how it self-regulates)

   12. 17:51 – Perpetual rights and killing usage-tracking headaches

   13. 19:31 – Case Study: Zak Designs and content for every touchpoint

   14. 22:55 – Which verticals Cohley wins in (and which are harder)

   15. 24:17 – What working with Cohley actually looks like

   16. 27:56 – How brands measure success with Cohley content

   17. 31:31 – Inside Cohley Cognition: the AI brain

   18. 34:33 – Distributing content across Amazon, TikTok, Yotpo & more

   19. 36:18 – Pricing, pilots, and de-risking the decision

   20. 37:50 – How to explore Cohley on Blurbs & what’s next

   
   

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

• 
  

Episode Summary
Most retailers still sell like it’s 1999: flat product photos, isolated PDPs, and generic campaigns that ignore how people actually use what they buy. In this episode of The MarTech Matrix, Sean sits down with Michelle Bacharach, CEO & Co-founder of FindMine, to talk about how AI-powered styling can finally connect merchandising and marketing — turning single products into full looks, routines, and room setups that are on-brand, on-trend, and in-stock.

From IKEA showrooms and TikTok micro-trends to Meta catalog ads and in-store experiences, Michelle breaks down how outcome-oriented styling boosts conversion, AOV, and customer loyalty — without burning out your creative and merchandising teams.

🔑 Key Takeaways

• The real problem isn’t product discovery — it’s outcome discovery. Most shoppers don’t know how to wear or use what they’re buying. Styling and context are what unlock confidence and conversion.

• Most consumers don’t have the “stylist gene.” Brand teams do — which is why they often underestimate how much help regular shoppers need to visualize outfits, rooms, or routines.

• Retailers still over-optimize for single products. SEO and PDPs are built around individual SKUs, but buying decisions are made around moments (holiday party, barn wedding, marathon, spooky season, etc.).

• AI styling can save “forgotten” products from the clearance rack. When you put underperforming items into the right story or trend, they often sell — without automatic discounting.

• Creative + inventory + performance need to be connected. FindMine ties together product feeds, brand rules, inventory, and media platforms to keep looks on-brand and in-stock across ads, PDPs, landing pages, email, and stores.

• Micro-trends beat monolithic audiences. It’s more powerful (and often cheaper) to lean into “spooky season,” “barn wedding,” or “almond mom summer” than just “holiday” or “wedding season.”

• Future search is outcome-first, not product-first. As AI search replaces traditional search, brands that structure their data around outcomes (e.g., “perimenopausal acne routine”) will win more share of wallet.

  
  

  ⏱️ Chapters

  00:00 – Intro: The styling gap in modern eCommerce
  01:33 – Michelle’s founder story: From window displays to AI styling
  04:23 – Why most shoppers can’t “see” the outfit (and why brands forget that)
  06:32 – Portland vs. New York: How geography and lifestyle shape style
  09:16 – Personalization beyond zip code: Trends, micro-niches, and culture
  11:10 – The Toy Story analogy: Giving every product a fair shot
  15:30 – Underperformers, sequined vests, and why discounting is a blunt tool
  16:08 – How FindMine works: Data, training, and plugging into your stack
  18:37 – Where styling shows up: Ads, PDPs, landing pages, email, chat, PIMs
  19:50 – Micro-trends, CAC busting, and the power of “small but specific” moments
  21:21 – Finding gaps in your marketing with niche themes and segments
  23:13 – Meta catalog ads: What Meta does vs. what FindMine actually changes
  25:35 – Why AI is “brilliant and stupid” — and why prompting matters for brand
  26:42 – Brand control spectrum: From luxury guardrails to fully automated styling
  29:53 – Working with big brands and navigating rebrands (Gap, Lulu, etc.)
  32:35 – Who FindMine is for: ICP, verticals, and where it works best
  34:42 – Case studies: AOV, conversion, repeat purchase, and an 8% landing page CVR
  36:16 – Unexpected insights: Bralettes, tops, and re-merchandising physical stores
  37:59 – Bridging online and in-store: Clienteling, touchscreens, and store associate tools
  40:18 – The future: Outcome-based search, AI chat, and being “AI ready” as a brand
  43:14 – Where to start: Don’t boil the ocean — pick your slice of the journey
  45:07 – Lightning round: Outcome obsession, the big mistake, and fraud tech
  46:38 – Wrap-up: How to learn more and where to find FindMine

  
  

  
  

  
  

For years, ROAS (Return on Ad Spend) was the go-to metric for performance marketers. It was simple, clear, and instantly gratifying — the higher, the better. But as Mark Deruyter points out in our latest episode of The MarTech Matrix, that once-reliable metric has quietly become one of the most misleading KPIs in modern marketing.

We cover:

• The Problem with ROAS
• The Better Stack: MER, CAC, and LTV
• Measuring What Matters
• How AI Is Changing the Game
• Speed, Fit, and Impact: A New Way to Buy Tech

Takeaways

• ROAS is overrated. It relies on platform data and third-party cookies, which makes it unreliable in today’s privacy-first world. It measures spend efficiency, not profitability, and can create a false sense of success.
• Shift to MER, CAC, and LTV.
• Trust first-party data. Platform dashboards are directional only. Real insight comes from CRM and transaction data that connect spend directly to sales and retention.
• Retention is AI’s next frontier. AI can now identify inactive customers, predict churn, and trigger personalized outreach automatically. Retention automation is becoming the biggest growth lever for established brands.
• The modern marketer’s must-have skills:
• Buy technology based on speed, fit, and impact. If a tool can’t be implemented and delivering results within 30 days, it’s probably not the right one. Focus on solutions that make your team faster and smarter, not bloated with features.
• Collaboration beats silos. The best marketing teams align brand, creative, and performance to connect storytelling with measurable growth.
• Bottom line: Move beyond vanity metrics. Build a profit stack grounded in first-party data, AI, and metrics that matter — MER, CAC, and LTV. The future belongs to marketers who measure what actually drives profit, not just performance.

Chapters

01:27 How the marketer’s job changed (real-time, cross-team)

06:30 Brand’s rising importance & authenticity

09:02 Gut vs data (keep the art, validate the inputs)

12:36 Tools that accelerate marketplace performance (Stackline, Helium 10)

14:21 First-party truth over platform dashboards

15:32 Overrated metrics: ROAS → shift to MER, CAC, LTV

17:46 How to think about LTV at earlier-stage brands

21:32 Buying tech: 30-day implementation mindset; time-to-value

24:31 What vendors miss (research, economics, CFO proof)

30:42 AI’s impact: compress data → creative → execution

32:07 Acquisition vs retention (why retention wins next)

35:10 Future skills: data fluency, AI literacy, brand authenticity

38:23 Underrated channels: Affiliate & SEO (and AEO)

40:53 BFCM tip: have backup copy/creative variants ready