SummaryIn 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.TakeawaysMarketers 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."Chapters00:00 Introduction to Customer Intelligence02:41 Understanding Insighta's Methodology05:34 When to Use Insighta08:19 What Makes Insighta Remarkable10:52 The Role of Data in Marketing Decisions13:32 Navigating the Measurement Space16:16 Onboarding and Support with Insighta18:33 The Impact of Predictive LTV21:12 Case Study: Obagi's Success24:00 Lifetime Value for New Brands26:20 Client Engagement and Analytics29:13 The Future of AI in Marketing31:39 Pricing Models and Considerations34:08 Final Thoughts on Measurement Strategies36:31 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 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.Chapters00:00 – Why content feels like oxygen (but teams can’t breathe)00:55 – Meet Cohley: Sean reads the Blurb01:12 – Why brands have never needed this much content02:46 – Who Cohley is really for (and who it isn’t)04:35 – From early UGC to building Cohley06:36 – Beyond point solutions: powering the whole journey07:17 – Cohley vs competitors: where they truly differ09:08 – Using AI to enforce creative “non-negotiables”11:16 – Why customer success is Cohley’s backbone13:41 – Diversity of content and creator matching at scale15:19 – Who gets into the creator network (and how it self-regulates)17:51 – Perpetual rights and killing usage-tracking headaches19:31 – Case Study: Zak Designs and content for every touchpoint22:55 – Which verticals Cohley wins in (and which are harder)24:17 – What working with Cohley actually looks like27:56 – How brands measure success with Cohley content31:31 – Inside Cohley Cognition: the AI brain34:33 – Distributing content across Amazon, TikTok, Yotpo & more36:18 – Pricing, pilots, and de-risking the decision37:50 – 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 Takeaways60–70% 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?”Chapters00:00 — Intro & Who Is Bold Metrics?02:15 — The Origin Story: FashionMetric06:40 — Master Tailoring Meets Machine Learning10:25 — How Bold Metrics Predicts Body Measurements12:30 — Why Fit Is the #1 Conversion Killer in Apparel14:15 — The Economics of Returns17:50 — Size Confidence as a Performance Lever21:05 — Why Static Size Charts Fail25:35 — The Future of Fit Intelligence (Multimodal + Context Aware)29:10 — Fit as a Core Layer of Personalized Commerce32:00 — Advice for Apparel Leaders35:00 — Closing Thoughts

Episode SummaryMost 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 TakeawaysThe 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.⏱️ Chapters00:00 – Intro: The styling gap in modern eCommerce01:33 – Michelle’s founder story: From window displays to AI styling04:23 – Why most shoppers can’t “see” the outfit (and why brands forget that)06:32 – Portland vs. New York: How geography and lifestyle shape style09:16 – Personalization beyond zip code: Trends, micro-niches, and culture11:10 – The Toy Story analogy: Giving every product a fair shot15:30 – Underperformers, sequined vests, and why discounting is a blunt tool16:08 – How FindMine works: Data, training, and plugging into your stack18:37 – Where styling shows up: Ads, PDPs, landing pages, email, chat, PIMs19:50 – Micro-trends, CAC busting, and the power of “small but specific” moments21:21 – Finding gaps in your marketing with niche themes and segments23:13 – Meta catalog ads: What Meta does vs. what FindMine actually changes25:35 – Why AI is “brilliant and stupid” — and why prompting matters for brand26:42 – Brand control spectrum: From luxury guardrails to fully automated styling29:53 – Working with big brands and navigating rebrands (Gap, Lulu, etc.)32:35 – Who FindMine is for: ICP, verticals, and where it works best34:42 – Case studies: AOV, conversion, repeat purchase, and an 8% landing page CVR36:16 – Unexpected insights: Bralettes, tops, and re-merchandising physical stores37:59 – Bridging online and in-store: Clienteling, touchscreens, and store associate tools40:18 – The future: Outcome-based search, AI chat, and being “AI ready” as a brand43:14 – Where to start: Don’t boil the ocean — pick your slice of the journey45:07 – Lightning round: Outcome obsession, the big mistake, and fraud tech46:38 – 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 ROASThe Better Stack: MER, CAC, and LTVMeasuring What MattersHow AI Is Changing the GameSpeed, Fit, and Impact: A New Way to Buy TechTakeawaysROAS 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.Chapters01:27 How the marketer’s job changed (real-time, cross-team)06:30 Brand’s rising importance & authenticity09:02 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 dashboards15:32 Overrated metrics: ROAS → shift to MER, CAC, LTV17:46 How to think about LTV at earlier-stage brands21:32 Buying tech: 30-day implementation mindset; time-to-value24:31 What vendors miss (research, economics, CFO proof)30:42 AI’s impact: compress data → creative → execution32:07 Acquisition vs retention (why retention wins next)35:10 Future skills: data fluency, AI literacy, brand authenticity38:23 Underrated channels: Affiliate & SEO (and AEO)40:53 BFCM tip: have backup copy/creative variants ready

Presented byThe MarTech Matrix — a Blurbs production helping marketers find clarity in the chaos of MarTech. No fluff. Just the truth.SummaryMarketers have been “data-driven” for years—but mostly by proxies. In this conversation, Attain President & COO Ben Kartzman joins host Sean Simon to show how connecting ad exposure to permissioned, first-party purchase data changes everything: measure real sales, optimize mid-flight, and build smarter audiences with competitive switching insights. If you’ve ever asked, “Did our ads actually sell anything?”—this one’s for you.Why this matters (fast)Reality check: Clicks don’t equal customers. Sales do.New signal: Real-time purchase data sourced from consumers (not retailers) closes the loop.Actionable now: Move from post-hoc reporting to in-campaign optimization.HighlightsOutcomes > Proxies — Ben explains how the industry defaulted to what was easy to measure (CTR, impressions) and how Attain puts sales outcomes at the center.Permissioned scale — Attain’s dataset is built from 10M+ opted-in consumers via a value-exchange in consumer apps (points → cash/gift cards).CTV that sells — Example: Amazon Prime Live Sports campaigns where Attain ties ad exposure to incremental purchases (e.g., Taco Bell).Mid-flight wins — Pilot Flying J and Sally Beauty optimized against real buyers during the campaign, unlocking millions in incremental revenue.Insights that build audiences — Identify likely buyers, basket size, Prime membership, pet ownership, region, and where shoppers switch from/to—then target precisely.What’s next — The path from real-time attribution to predictive and ultimately agentic media.Three Big TakeawaysStop grading on proxies. Measure what your media actually sold.Optimize while it runs. With real-time purchase data, you don’t have to wait for the post-mortem.Know the switchers. Competitive switching intel is fuel for smarter conquesting and retention.Favorite Quotes“Clicks don’t pay the bills. Sales do.”“When teams see the live purchase lift, the conversation shifts from measurement to optimization.”“Our data originates from consumers, so we avoid retailer blind spots like Amazon or Walmart.”Who should watchPerformance and growth marketers tired of dashboard vanityOmnichannel & offline-heavy brands wrestling with retail media attributionMedia buyers who want real-time signal to act on, not just report

Podcasting has never been more culturally relevant. Listeners are engaged, loyal, and attentive — yet the channel continues to punch below its weight when it comes to marketing budgets. Despite commanding millions of daily listeners and deep audience trust, podcast advertising still accounts for a small fraction of total media spend. So what’s holding it back? And what will it take for audio to claim its rightful share of the marketing pie?In this episode of The MarTech Matrix, host Sean Simon sits down with Pete Jimison, CEO and Founder of Frequency, to discuss the evolution of podcast advertising, the workflow barriers slowing its growth, and how Frequency’s new platform — Automated Direct — is reshaping the way brands plan, execute, and measure audio campaigns.Tune into this episode…Takeaways90% of podcast revenue is still direct, not programmatic.Host-read ads are key but challenging to scale.Frequency offers automated direct solutions for audio ads.AI is used for quality assurance in ad reads.Dynamic creative optimization is expanding in audio.Location-based data is crucial for ad personalization.Brands should test various audio strategies for best results.Listening to podcasts helps brands understand the space.Chapters00:00 Introduction to Pete Jimison03:00 The Rise of Podcasting09:00 Challenges in Audio Advertising15:00 Innovative Solutions by Frequency21:00 The Role of AI in Audio27:00 Future of Audio Advertising

Welcome to the Martek Matrix, where we delve into the dynamic world of Connected TV (CTV) advertising. Hosted by Sean Simon, alongside industry experts Dan Larkman, CEO of Keen’s Digital, and Rick Egan, owner of Riviera Marketing, we explore the challenges and opportunities in the rapidly evolving CTV landscape. From fragmented platforms to innovative solutions, our discussions aim to provide clarity and actionable insights for brands navigating this exciting channel. Join us as we cut through the noise and uncover the strategies that drive real results in CTV advertising.TakeawaysCTV is experiencing rapid growth, but it comes with challenges.Brands are increasingly investing in CTV as part of their marketing budget.Data alone is not enough; understanding infrastructure is key.Incrementality is essential for measuring the effectiveness of CTV campaigns.Brands often struggle to identify their return on investment in CTV.The CTV landscape is fragmented, leading to confusion for buyers.Expertise in CTV can help brands avoid pitfalls and optimize performance.Amazon’s role as a DSP raises concerns about data privacy and targeting.The future of CTV will see a focus on measurement and trust-building.Mid-market brands have significant opportunities in the CTV space.Chapters00:00 Introduction to CTV and Its Challenges03:08 The Current State of the CTV Ecosystem05:45 Understanding Data and Infrastructure in CTV08:45 Brand Frustrations and Challenges in CTV12:00 Identifying Reliable CTV Providers14:34 Incrementality and Proving Value in CTV17:42 Investment Strategies and Budget Allocation in CTV20:42 Unique Advantages of Keen’s in the CTV Space25:11 Navigating Audience Targeting in CTV31:35 The Role of Amazon in Digital Advertising37:18 Identifying Winners and Losers in CTV42:39 Unlocking Opportunities for Mid-Market Brands

In this episode of the MarTech Matrix, Sean Simon interviews Arthur Root, CEO of Nostra, discussing the critical importance of site speed for e-commerce brands. Arthur explains how Nostra enhances website performance through its Edge Delivery Engine, optimizing load times and improving personalization without compromising user experience. The conversation delves into the technical aspects of site speed, the role of cookies in personalization, and how Nostra integrates seamlessly with existing tech stacks. Arthur also shares insights on measuring success with Nostra and the future of site speed in the e-commerce landscape.TakeawaysNostra helps brands improve ROI by enhancing site speed.Site speed is crucial for conversion rates and user experience.Technical expertise is often required to address site speed issues.Cookies play a vital role in personalization and user tracking.Nostra’s Edge Delivery Engine caches website content for faster loading.Crawler optimization is essential for SEO and personalization.Nostra integrates easily with various e-commerce platforms.Success can be measured through A/B testing and site speed improvements.Nostra is positioned as a leading solution in the market.The future of e-commerce will increasingly rely on edge computing for performance.Chapters00:00 Introduction to Nostra and Site Speed Importance02:53 The Technical Aspects of Site Speed06:04 Overcoming Site Speed Challenges in E-commerce09:03 The Role of Cookies in Personalization11:57 Nostra’s Edge Delivery Engine Explained14:55 Crawler Optimization and SEO Benefits17:45 Integrating Nostra with Existing Tech Stacks20:53 Measuring Success with Nostra23:53 Nostra’s Unique Value Proposition26:53 Future of Site Speed and E-commerce29:44 Lightning Round and Conclusion

TakeawaysPixels are broken. Nearly half of online conversions (42%) go untracked due to blockers, privacy settings, and attribution window limitations.Sales aren’t just on your site. 40% of eCommerce sales happen off-site (Amazon, Walmart, marketplaces), invisible to pixel tracking.Ad spend leakage. 17% of spend goes into channels like audio and TV that pixels can’t measure, leaving major blind spots.Last click is lazy. It oversimplifies the customer journey, rewarding the final step and ignoring the true path to purchase.Duplication is rampant. Multiple platforms often claim credit for the same conversion, inflating perceived ROI.MMM is back. Once considered slow and outdated, modern MMM now runs weekly, giving marketers near real-time budget insights.Unified framework matters. Combining MMM, incrementality testing, and MTA gives the clearest picture of channel performance.Custom > generic. AI-powered custom models now outperform generic off-the-shelf ones—at similar cost—thanks to automation.36% of ad dollars are wasted. Bad data and poor measurement account for over a third of spend, creating a massive opportunity for optimization.Experimentation wins. The best way to break through opinion vs. opinion debates is to run structured tests and let the data speak.ChaptersIntro & Sponsor Shoutout (00:01–00:44) Sean introduces the theme: why marketers have been “flying blind” and how better measurement restores vision.Meet Zeke Camusio (00:44–02:10) Zeke shares his background as an entrepreneur and why he founded Data Speaks after seeing marketers’ frustration with measurement.The Problem with Pixels (02:10–03:47) Why current attribution methods fall short: off-site sales, blocked conversions, arbitrary attribution windows, and duplication.The Measurement Framework (04:19–05:38) Zeke explains the three pillars: MMM, incrementality testing, and MTA—and why they work best together.MMM Misconceptions (05:38–06:25) Why MMM is misunderstood, how it’s evolved, and why it’s valuable in a real-time, digital-first environment.The Trouble with Last Click (06:25–08:30) Why simplistic attribution models distort ROI and why causal measurement matters more.Applying MMM & Incrementality (08:30–12:23) How Data Speaks uses experiments and triangulation to reveal true performance and reallocate budgets.Onboarding & Custom Models (14:16–17:30) What implementation looks like, why labeling data is critical, and how custom models beat generic ones.Culture Shift: Gut vs. Data (18:25–19:34) The organizational challenge of moving from instinct-driven marketing to data-driven experimentation.AI, Trust, and the Future (21:39–24:41) Why brands need independent measurement in the age of AI-driven media buying—and how AI enhances, not replaces, human judgment.Case Study: From $2M to $22M (24:41–25:51) How reallocating spend and applying the framework drove double-digit ROAS gains and 10x revenue growth.Competitor Landscape & Differentiation (27:45–30:50) Where Data Speaks sits relative to Recast, Measured, Triple Whale, and others—bringing consolidation under one roof.Mindset Shift: Do You Even Know You Have a Problem? (30:50–32:06) Why most marketers don’t realize their measurement stack is broken until they see the alternative.Lightning Round (32:06–34:32) Quick-fire Q&A: myths, frameworks, tools, decompression habits, and Zeke’s rugby background.Closing Thoughts & Call to Action (34:32–end) Where to find Zeke, upcoming product enhancements, and a final reminder: let the data speak.