DTC Podcast

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Braydon Germain from Pilothouse is back, and we go straight into the most practical question in DTC right now: how do you use AI to create more winning ads and systems without torching brand trust or wasting time prompting? We talk AI “employees,” creative production in the Andromeda era, and why concept volume matters more than ever.

Role-Based Hook: For DTC founders + performance marketers scaling Meta spend while creative fatigue (and CAC) keep creeping up.

In this episode, we get tactical on:
“Clawbot/Maltbot” style AI agents that can operate a computer (and why security is the real bottleneck)
How to use AI for Black Friday creative that stops the scroll but doesn’t scream “fake”
Why Andromeda pushes you toward new concepts (not tiny headline/CTA tweaks)
How to set ChatGPT custom instructions so it stops being a yes-man and starts pressure-testing your ideas
Motion’s AI tagging + “chat with your ad data” workflows for faster creative strategy loops

Who this is for:
Media buyers, creative strategists, and founders who need more creative output, faster learnings, and fewer “we tested 30 ads and learned nothing” weeks.

What to steal (quick wins):
Use AI to generate weird-but-believable statics (organic-looking, scroll-stopping) instead of obvious AI art
Build a “mentor mode” prompt profile that actively calls out weak angles before you waste spend
Audit your account for creative diversity and gaps (formats, audiences, hooks) before you brief your next batch

Timestamps
0:00 Using AI in ads without looking obviously AI
2:00 Malt Bot and autonomous AI that can run a computer
4:05 Bot social network drama and why “scary AI posts” go viral
6:10 Black Friday AI creative workflow with Photoshop and subtle edits
8:20 AI video trick: first frame + last frame for organic-looking shots
10:20 Static ad creator tools for fast concept volume
12:30 Andromeda creative testing: the 70% different rule and bucketing
14:45 Custom instructions to make ChatGPT less of a people pleaser
17:05 Building an AI-assisted newsletter system with Claude and podcast “brains”
19:15 Staying plugged into AI communities and new ecommerce tools
21:25 Motion app AI tagging and creative analysis for Meta ads
23:30 Agent mode, security, and letting AI work while you sleep

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Kareem Raslan (co-founder of BrainGain) breaks down how a “25 dumbbells in a garage” COVID side-hustle turned into a home gym brand with 100,000+ customers across 30 countries. We talk heavy-product logistics, why “just run Meta” isn’t the whole story, and what it really takes to expand across Europe without margin leakage.

For DTC operators selling high-AOV, physical products who want to expand beyond one market without getting crushed by fulfillment and localization.

In this episode, we cover:
Why BrainGain skipped dropshipping and went product-in-hand from day one
The Europe expansion reality: VAT, language, regulations, and market-by-market nuance
Why Germany can be the “logical” move… and still the hardest operationally
Their channel strategy today: ~50% Amazon / ~50% Shopify, with Google doing the heavy lifting
How YouTube affiliates drive trust for high-consideration purchases

Who this is for:
Founders and marketers selling heavy, high-AOV products (fitness, home goods, equipment) who need a real playbook for scaling across regions.

What to steal:
Build SKU-by-SKU unit economics so you know your true ceiling CAC (by market + channel)
Use YouTube affiliates for “proof” when the purchase isn’t impulsive
Audit 3PL invoices line-by-line (surcharges hide everywhere)

Timestamps
0:00 BrainGain’s growth from garage sales to 100,000 customers
2:00 How BrainGain started during COVID with Facebook Marketplace sales
5:00 Post-lockdown demand, competing in “big and heavy” products
7:00 Switching to Shopify and Amazon, building the brand online
14:40 Expanding across Europe: VAT, regulations, and localization realities
22:00 Channel mix breakdown: Amazon vs Shopify, Google vs Meta
24:00 Why BrainGain is saying no to TikTok influencers and leaning into YouTube affiliates
27:30 Picking the right 3PL in Europe and avoiding hidden surcharges
31:00 Fulfillment cost levers: packaging thresholds, pallet rules, invoice audits
34:10 SKU-level unit economics audit and setting a real CAC ceiling
37:20 Pricing strategy: Shopify vs Amazon and controlling channel mix
39:30 What US expansion could look like for heavy, bulky products

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If you’re a $5M+ brand owner or growth lead and Meta’s performance feels “fine but fragile,” this is probably why. Abby from Pilothouse breaks down how Andromeda changed the rules: targeting moved upstream into creative strategy, and the old “tiny tweaks + CTR tests” approach doesn’t survive in an AI-ranked feed.

Role-Based Hook: For DTC brand owners and growth leads at $5M+ who need a real Meta strategy for creative testing, not more random volume.

What you’ll learn (tactical + skimmable):
Why “go broad” without segmentation is a strategy fail (and a fast way to spike frequency)
The audience setup mistake that makes Meta hammer warm users instead of prospecting
Why micro-variants (same static, new headline) now read as boring to the algorithm
The new testing model: persona → concept → multiple formats (same message, different executions)
How to use competitive pressure to sharpen your differentiation strategy (not copy angles)

Who this is for: Owners + growth leads managing Meta as a core growth channel and trying to scale without wasting spend on over-frequency.

What to steal:
Ask your team for frequency split by new vs engaged vs existing over the last 30 days
Build creative tests around a persona hypothesis, then ship 5 distinct formats (carousel, static, UGC, iPhone selfie, trend)
Pressure-test messaging against competitors: “If we line up 5 brands, what do we say that only we can say?”

Timestamps
00:00 Andromeda changes how creative testing works
02:00 Why broad targeting breaks without proper audience segments
04:00 Frequency benchmarks and the prospecting mistake to watch
06:00 Why the old “pilot test” headline testing stopped working
08:00 Persona testing: turning pain points into ad angles
10:00 Differentiators vs competitors when everyone sells the same USP
12:00 The new testing stack: one concept, five creative formats
15:00 Building personas with the four Cs plus competitor ad library
18:00 Audit questions to ask your media buyer and creative strategist

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Laura Cantor, VP of Marketing & E-commerce at New York & Company, shares the reality of transforming a legacy retail brand in the age of AI - and why nobody can do it alone.

https://zenyt.ai/grow

In this episode:
🚀 Leading digital transformation at a legacy retail brand
🤖 Using to find 500+ site issues (and the problems AI creates) zenyt.ai/start
💻 Moving from Shopify Hydrogen to Liquid right before Black Friday
🤝 Why partnerships replaced competition in retail
🧠 AQ (Adaptability Quotient) - the skill that matters most
📱 Building in public and learning together
⚡ The reality of doing more with less in 2025
🎯 Why every problem now has 10 AI solutions (and nobody knows which works)

Key topics:
- The shift from headless back to Liquid (and why simplicity won)
- AI-powered site QA and the translation gap (zenyt.ai/start)
- Why collaboration beats secrecy in the AI era
- Post-COVID conference culture and community building
- Adaptability and unlearning in real-time
- NRF insights and retail trends for 2025
- The calculus class we're all failing together

Timestamps:
0:00 - Intro
1:30 - Laura's retail background (Ralph Lauren, J.Crew)
2:10 - New York & Company's digital transformation journey
4:40 - The biggest challenges: doing more with less
8:15 - AI creates problems AND solutions (the QA challenge)
15:50 - Why we moved from Hydrogen to Liquid
21:10 - The post that resonated across the industry
28:30 - Why partnerships matter more than competition now
33:00 - How agile teams adopt AI tools
36:35 - We're all failing calculus together
39:00 - Adaptability Quotient (AQ) - the new essential skill
44:50 - The power of clarity: translating robot to human
52:30 - Why retail is fundamentally a people business
55:45 - Upcoming events (eTail West, ShopTalk, Commerce Next)

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Amazon Marketing Cloud (AMC) used to feel “enterprise-only.” Not anymore. Tyler Masur (Head of Amazon at Pilothouse) breaks down what AMC actually does, how to use the no-code templates without being a SQL wizard, and the audience overlays that make broad keywords finally make sense.

Role-based hook: For Amazon operators and DTC teams spending real money on Sponsored Ads who want lower ACOS without sacrificing scale.

In this episode, we get tactical on:
What AMC is (and isn’t): audience building + deeper measurement layered on top of your existing console
How to start with the no-code audience + analytics templates (and when AI-generated SQL helps)
Why you should test AMC audiences in net-new campaigns (so you don’t accidentally choke your winners)
The “broad keyword + qualified audience” play (example: bidding on “cooler” but only for outdoors browsers)
Measuring DSP impact: what happens after someone sees DSP, then hits Sponsored Brands/Products

Who this is for: Amazon managers, DTC founders, and growth teams trying to scale Sponsored Ads past the “set it and forget it” phase.

What to steal:
Build a “generic keyword” campaign, then overlay an in-market audience (nodes/categories) so you can bid higher without paying for junk clicks.
Keep audience tests isolated in new campaigns; don’t jam audiences into legacy structures and hope.
Run the AMC overlap reporting to spot the campaigns that actually increase conversion when paired together (then fund those).

Timestamps:
0:00 Amazon Marketing Cloud is now open to all sellers
2:05 What AMC actually does: audiences and analytics
4:10 No-code templates vs custom SQL queries (and the built-in AI helper)
6:10 Audience targeting strategy to improve ACoS without over-narrowing
8:55 Using prebuilt analytics to see which campaigns lift conversion together
11:10 When AMC becomes worth it based on ad spend and effort required
13:15 How Pilothouse uses AI day-to-day for Amazon work (including Rufus content)
15:20 Measuring DSP incrementality and overlap with sponsored ads using AMC reports
17:30 Platform notes: Amazon layoffs and the OpenAI + Amazon speculation

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