Sales & Conversion
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
So here's the thing about Shopify personalization that nobody talks about - everyone's obsessing over expensive AI recommendation engines when the real money is in something way simpler and more scalable.
I was working with this Shopify client who had over 1,000 products and a solid customer base, but their repeat purchase rate was stuck at around 15%. The usual suspects were all there: abandoned cart emails, discount codes, loyalty points. But something was fundamentally broken.
The "aha" moment came when I realized we were treating every returning customer the same way. Someone who bought vintage leather bags was seeing the same homepage as someone who bought minimalist wallets. It was like having a personal shopper who completely forgot what you liked.
Instead of throwing money at complex personalization platforms, I built something different - a system that creates personalized collection pages for every customer segment, powered by their actual purchase behavior and browsing patterns.
Here's what you'll learn from this breakdown:
Why generic product recommendations kill repeat purchases
How to create 200+ personalized collection pages using AI automation
The email sequence that drives customers to their personal collections
Specific metrics that prove this approach works better than traditional recommendations
How to implement this without breaking your existing conversion optimization setup
This isn't about complicated AI or expensive platforms. It's about using what you already know about your customers to create experiences that feel genuinely personal.
Industry Reality
What Every Ecommerce Store Owner Has Been Told
Walk into any Shopify conference or browse any ecommerce blog, and you'll hear the same advice about personalization: "Install a recommendation engine!" "Use AI-powered product suggestions!" "Implement dynamic homepage content!"
The industry has convinced everyone that personalization means sophisticated algorithms analyzing micro-interactions to predict what someone might want to buy next. Platforms like Dynamic Yield, Yotpo, and Klaviyo Smart Recommendations promise to solve everything with their black-box AI.
Here's what the conventional wisdom looks like:
Product recommendation widgets - Those "Customers who bought this also bought" sections
Dynamic homepage content - Showing different hero banners based on past behavior
Email product recommendations - Automated suggestions in your email campaigns
Behavioral popups - Exit-intent offers based on browsing history
Personalized search results - Reordering products based on customer preferences
This approach exists because it feels logical - use data to show relevant products. The problem? Most of these solutions treat personalization like a math problem. They analyze purchase patterns and browsing behavior to surface "relevant" products.
But here's where it falls apart in practice: personalization isn't just about showing the right product - it's about creating the right shopping experience.
When you only focus on product recommendations, you're still forcing customers to navigate your generic site structure. They're still browsing through your standard collections, still seeing your homepage designed for everyone and no one.
The real issue isn't that customers can't find relevant products - it's that they don't feel like your store understands their specific needs and shopping style.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
This client came to me with what looked like a pretty solid setup. Over 1,000 products, decent traffic, and they'd already invested in some basic personalization through Klaviyo's Smart Recommendations. Their numbers weren't terrible - 15% repeat purchase rate, average order value around €45.
But when I dug into their analytics, I found something interesting. Their repeat customers were behaving like first-time visitors. They'd come back to the site, browse around for 3-4 minutes, maybe add something to cart, but then leave. The time between visits was getting longer, and when they did purchase again, it was usually after several sessions.
The client sold accessories - bags, wallets, jewelry, phone cases. The kind of products where personal style really matters. Someone who buys minimalist leather goods has completely different taste from someone who goes for vintage-style pieces.
I started watching session recordings and noticed a pattern: returning customers would land on the homepage, scroll through the featured products (which were the same for everyone), then start manually browsing through collections trying to find "their kind of stuff." It was like watching someone walk into a department store and having to search through every section to find their size and style.
The existing personalization wasn't working because it was product-focused, not experience-focused. Sure, the recommendations were technically relevant, but customers still had to work to find what they wanted.
That's when I realized we needed to flip the entire approach. Instead of personalizing individual product recommendations, what if we personalized entire shopping experiences?
Here's my playbook
What I ended up doing and the results.
OK, so here's what I actually built for this client, and why it worked better than any recommendation engine we tested.
Instead of trying to guess which specific products someone might want, I created personalized collection pages for different customer segments. But not just simple "Customers who bought X" collections - actual curated shopping experiences based on style preferences, purchase behavior, and browsing patterns.
The AI-Powered Collection Generator
First, I set up an AI workflow that analyzed customer purchase history and created distinct style profiles. For this accessories store, the main segments were:
Minimalist buyers (clean lines, neutral colors, functional design)
Vintage enthusiasts (classic styles, rich textures, heritage brands)
Tech-focused customers (functionality, durability, modern materials)
Fashion-forward shoppers (trendy, bold, statement pieces)
The AI didn't just look at what people bought - it analyzed product descriptions, materials, price points, and even color preferences to understand style DNA.
Dynamic Collection Creation
Here's where it gets interesting. Instead of manually creating collections for each segment, I built a system that automatically generates personalized collection pages. When someone with a "minimalist" purchase history visits the site, they see collections like:
"Essential Minimalist Pieces You'll Actually Use"
"Clean Lines: Functional Accessories"
"Timeless Pieces for the Modern Professional"
Each collection was populated with products that matched their style profile, but the magic was in the presentation. Instead of generic product grids, each personalized collection told a story about how these pieces fit into their lifestyle.
The Email-to-Collection Bridge
The real breakthrough was connecting this to email marketing. Instead of sending generic "Here are some products you might like" emails, I created campaigns that drove people to their personalized collections:
"Your Minimalist Collection Has New Arrivals"
"Complete Your Vintage Look"
"Tech Accessories That Actually Work"
Each email felt like it was coming from a personal shopper who really understood their style, not a generic marketing blast.
Smart Collection Updates
The system continuously learned and adapted. When someone's purchase behavior showed they were exploring new styles, their collections would gradually shift to include more diverse options. If a minimalist customer bought something with more color, the system would start including "Minimal with a Pop of Color" collections.
Style Profiling
AI analyzed purchase history, product descriptions, and materials to create detailed customer style profiles that went beyond simple "frequently bought together" data.
Dynamic Collections
Instead of static product recommendations, the system generated entire personalized shopping collections that told a story about how products fit the customer's lifestyle.
Email Integration
Personalized email campaigns drove customers directly to their curated collections rather than generic product recommendations or homepage visits.
Adaptive Learning
The system continuously updated customer profiles and collections based on new purchases and browsing behavior to keep experiences fresh and relevant.
The results were honestly better than I expected, especially considering this wasn't some expensive enterprise platform - it was a custom system built specifically for their needs.
Within three months of implementing the personalized collection system:
Repeat purchase rate jumped from 15% to 28% - customers were coming back more frequently and buying more consistently
Average time between purchases dropped from 4.2 months to 2.8 months - the personalized collections made it easier for customers to discover new products in their style
Email click-through rates increased by 67% - the personalized collection emails were way more engaging than generic product recommendations
Average order value for repeat customers went up 23% - when customers found collections that truly matched their style, they bought more items per order
But the most interesting result was qualitative - customer service reported that people were asking more questions about products and styles, which indicated they were more engaged with the shopping experience. We also saw customers sharing their personalized collections on social media, which never happened with the old generic approach.
The system automatically created over 200 different collection variations, each one feeling unique and personal without requiring manual curation.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from building this personalized collection system that apply way beyond just this one client:
Personalization is about experience, not just products. Showing the right products in the wrong context doesn't feel personal - it feels algorithmic. Creating entire curated experiences makes customers feel understood.
Style profiles work better than purchase history alone. Just because someone bought a black wallet doesn't mean they want more black products. Understanding their style DNA helps predict what they'll actually want.
Email integration is crucial. Personalized collections are powerful, but customers need a reason to visit them. Email campaigns that drive to specific collections convert way better than homepage visits.
Automation scales better than manual curation. Trying to manually create personalized experiences for every customer segment is impossible. AI workflows that generate collections automatically are the only way to scale this approach.
Storytelling matters in collections. A collection called "Essential Minimalist Pieces" performs better than "Products You Might Like" even if they contain the same items. The narrative makes the personalization feel intentional.
This works best for stores with 500+ products. You need enough inventory diversity to create meaningful segments. Smaller catalogs should focus on other personalization tactics.
Customer feedback loops are essential. The system needs to learn from customer behavior and adapt collections over time. Static personalization becomes stale quickly.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS applications, adapt this by creating personalized feature collections based on user behavior and company size, with targeted email sequences driving users to relevant use-case pages and integration guides.
For your Ecommerce store
For ecommerce stores, implement AI-generated customer style profiles that automatically create personalized collection pages, integrated with email campaigns that drive repeat customers to their curated shopping experiences.