Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
When I started working with a Shopify client who had 200+ collection pages, they were doing what most ecommerce stores do with email: sending the same generic "Get 10% off" blast to everyone on their list. Their open rates were mediocre, click rates were terrible, and they couldn't figure out why their 20,000+ subscriber list wasn't generating meaningful revenue.
That's when I discovered something most marketers completely overlook: collection pages are goldmines for email segmentation. Instead of treating all subscribers the same, we created 200+ micro-targeted email campaigns based on exactly what people were browsing.
The transformation was immediate. We went from generic blasts that everyone ignored to hyper-personalized emails that felt like they were reading subscribers' minds. Email revenue doubled in the first month, and we built a system that could scale to any size catalog.
Here's what you'll learn from my approach:
Why generic email blasts are killing your ecommerce revenue
How to use collection page behavior for email segmentation
My step-by-step system for creating 200+ personalized email sequences
The AI automation workflow that makes this scalable
Why this approach works better than traditional abandoned cart emails
If you're tired of sending emails that feel like shouting into the void, this playbook will show you exactly how to turn your email list into a revenue-generating machine.
Reality Check
What most ecommerce stores get wrong about email
Walk into any ecommerce conference and you'll hear the same tired advice about email marketing. The "experts" will tell you to:
Segment by demographics - Age, location, gender
Use purchase history - What they bought before
Send abandoned cart emails - The holy grail of ecommerce
Create seasonal campaigns - Holiday, summer sales, etc.
A/B test subject lines - The ultimate optimization
This advice isn't wrong, but it's missing the biggest opportunity sitting right under your nose: behavioral intent signals from your website.
Most stores treat email like a broadcasting channel. They build one message and blast it to their entire list, maybe with some basic segmentation. The problem? Someone browsing vintage leather bags has completely different interests than someone looking at minimalist wallets. Generic emails ignore this context completely.
The conventional wisdom exists because it's easy to implement. Demographics are simple to collect, purchase history is already in your system, and seasonal campaigns require minimal setup. But easy doesn't mean effective.
Here's where this approach falls short: it treats your email list like a homogeneous group when it's actually hundreds of micro-audiences with different needs, interests, and purchase intent. You end up with decent open rates but terrible engagement, because most people on your list receive emails that aren't relevant to their current interests.
The breakthrough comes when you stop thinking about email blasts and start thinking about personalized conversation starters based on what people are actually interested in right now.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client came to me with a classic ecommerce problem: a beautiful Shopify store with over 1,000 products, solid traffic, but email campaigns that barely moved the needle. They had a 20,000+ subscriber list but were seeing typical industry metrics - 18% open rates, 2% click rates, and most importantly, disappointing revenue per email.
During my SEO audit, I discovered they had over 200 collection pages, each getting organic traffic but serving only one purpose: displaying products. That's when it clicked - every visitor who landed on a collection page was telling us exactly what they were interested in.
Someone browsing "Vintage Leather Bags" has different needs than someone looking at "Minimalist Tech Accessories." Yet both were receiving the same generic "20% off everything" emails. We were essentially ignoring hundreds of intent signals our visitors were giving us for free.
The traditional approach would have been to segment by purchase history or demographics. But here's the problem: purchase history only tells you what someone bought before, not what they're interested in right now. And demographics? Someone's age tells you nothing about whether they prefer vintage or modern styles.
I realized we were sitting on a goldmine of behavioral data. Each collection page visit was a micro-intent signal that we could use to create hyper-relevant email experiences. Instead of one generic email list, we could have 200+ micro-audiences, each receiving content perfectly aligned with their demonstrated interests.
The challenge was scale. Creating 200+ unique email sequences manually would take months and require constant maintenance. That's when I knew we needed an automated system that could turn website behavior into personalized email content.
Here's my playbook
What I ended up doing and the results.
Here's exactly how I built a system that turned 200+ collection pages into personalized email revenue streams:
Step 1: Intent Mapping
First, I mapped every collection page to specific customer intents. "Vintage Leather Bags" attracted customers interested in craftsmanship and timeless style. "Tech Accessories" drew minimalists and professionals. Each collection represented a different customer persona with unique pain points and desires.
Step 2: Lead Magnet Creation
Instead of generic "10% off" popups, I created collection-specific lead magnets. Visitors to the vintage collection got "The Complete Guide to Caring for Leather Goods." Tech accessory browsers received "The Minimalist's Equipment Checklist." Each lead magnet was laser-focused on that audience's specific interests.
Step 3: AI-Powered Content Generation
Here's where it gets interesting. I built an AI workflow that automatically generated email sequences based on collection characteristics. The system analyzed product attributes, customer reviews, and collection themes to create contextually relevant email content for each micro-audience.
Step 4: Automated Email Sequences
Each collection got its own email sequence:
Welcome Email: Delivered the promised lead magnet with collection-specific tips
Story Email: Shared the story behind products in that collection
Social Proof Email: Customer stories specific to that product category
Education Email: How-to content relevant to that audience
Gentle Offer: Discount specifically for that collection
Step 5: Dynamic Segmentation
I set up dynamic tagging so subscribers were automatically segmented based on which collection pages they visited. Someone who browsed multiple collections got tagged for all relevant sequences, creating even more personalized experiences.
Step 6: Performance Optimization
The system tracked which collection-based emails generated the highest engagement and revenue. High-performing sequences became templates for similar collections, while underperforming ones were optimized or retired.
The beauty of this system? It scaled automatically. New collections got their own email sequences without manual work, and the AI continuously improved email content based on performance data.
Personalized Magnets
Each collection page got its own targeted lead magnet based on visitor intent rather than generic discounts.
AI Content Engine
Built automated workflows to generate collection-specific email sequences that matched audience interests perfectly.
Dynamic Segmentation
Visitors were automatically tagged based on browsing behavior, creating hyper-targeted email audiences.
Performance Loop
The system continuously optimized email content based on engagement data from each collection.
The results spoke for themselves. Within the first month of implementing collection-based email targeting:
Email Revenue Growth: Revenue from email campaigns doubled, going from contributing 15% of total revenue to 30%. More importantly, this growth was sustainable because we weren't burning out our list with irrelevant content.
Engagement Improvements: Open rates increased from 18% to 28% across targeted sequences. Click rates jumped from 2% to 7%. But the real win was in conversion - email-to-purchase conversion rates increased by 180%.
List Quality Enhancement: Instead of one generic list that gradually became less engaged, we had 200+ micro-lists with subscribers who were genuinely interested in specific product categories. Unsubscribe rates actually decreased because people were getting relevant content.
Operational Efficiency: The AI-powered system reduced email creation time by 90%. What used to take weeks of manual work now happened automatically, and the quality was consistently high across all sequences.
The approach proved that email marketing success isn't about having the biggest list - it's about having the most relevant conversations with the right people at the right time.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the 7 key lessons I learned from transforming generic email blasts into revenue-generating targeted campaigns:
Website behavior beats demographics - What someone browses tells you more about their intent than their age or location ever will.
Micro-audiences outperform mass marketing - 200 engaged subscribers interested in vintage leather goods are worth more than 2,000 generic subscribers.
Lead magnets should match intent - Generic "10% off" offers pale in comparison to specific, valuable content that solves real problems.
Automation enables personalization at scale - AI workflows make it possible to create hundreds of personalized sequences without manual work.
Context matters more than frequency - Sending fewer, more relevant emails generates better results than daily blasts.
Collection pages are underutilized goldmines - Most stores use them only for product display when they should be email segmentation tools.
Performance data drives improvement - The system gets better over time as it learns which content resonates with each micro-audience.
The biggest mistake I see stores make is treating email like a broadcasting channel instead of a conversation starter. When you match email content to demonstrated interest, everything changes.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Use trial behavior and feature usage to create targeted onboarding sequences
Segment users based on which product features they explore most
Create use-case specific email sequences that match different user workflows
For your Ecommerce store
Map collection pages to customer intent and create targeted lead magnets
Build automated email sequences for each product category
Use browsing behavior to dynamically segment your email list