The Evolution of Creator Content

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

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  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

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

The Future of Retail with FindMine

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

Kill the ROAS Crutch: Build a Profit Stack

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