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