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

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