Sales & Conversion
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last month, I sat in a client meeting where the CTO proudly showed me their new AI-powered recommendation engine. "We're using deep learning to predict what customers want," he said, pulling up metrics showing 2.3% click-through rates on recommended products.
I had to break some bad news: their previous "dumb" rule-based system was converting at 4.1%.
This isn't an isolated case. After working with dozens of e-commerce stores, I've seen the same pattern repeated: businesses rush to implement complex deep learning systems for product recommendations, only to discover that simpler approaches often outperform sophisticated algorithms.
The problem isn't that deep learning doesn't work—it's that most businesses implement it without understanding when complexity actually adds value versus when it just adds overhead.
In this playbook, you'll discover:
Why "smart" recommendation engines often underperform simple rules
The three-layer approach I use to build recommendation systems that actually convert
How to test recommendation effectiveness without getting lost in vanity metrics
When deep learning makes sense (and when it's just expensive show-off tech)
A practical framework for choosing between rule-based and AI-powered systems
This isn't about being anti-AI. It's about being pro-results. Sometimes the best solution is the simplest one that actually works—and I'll show you how to build conversion-focused recommendation systems that your customers will actually use.
Industry Reality
What every e-commerce founder believes about recommendations
Walk into any e-commerce conference and you'll hear the same story: "Personalization is everything. Amazon's recommendation engine drives 35% of their revenue. You need machine learning to compete."
The industry has convinced itself that sophisticated equals successful. Here's what every vendor will tell you:
"AI understands your customers better than they understand themselves" - The promise that deep learning can predict purchase intent with supernatural accuracy
"Personalization scales infinitely" - The belief that algorithmic recommendations automatically improve with more data
"Complex models prevent revenue loss" - The fear that simple recommendation rules leave money on the table
"Real-time adaptation is essential" - The assumption that recommendations must update instantly to remain relevant
"More data points equal better recommendations" - The conviction that tracking every interaction improves algorithmic performance
This conventional wisdom exists because it sounds logical. Amazon's success story gets repeated so often that it becomes gospel. Technology vendors have billion-dollar incentives to sell sophisticated solutions. Conferences showcase cutting-edge implementations, not boring systems that just work.
But here's where this falls short in practice: most e-commerce stores aren't Amazon. They don't have millions of users generating behavioral data. They don't have teams of PhD data scientists. Most importantly, their customers have fundamentally different browsing and buying patterns.
The result? Businesses implement expensive, complex recommendation systems that optimize for engagement metrics while conversion rates stagnate. They chase the sophistication without understanding whether complexity actually serves their specific situation.
I learned this lesson the hard way through multiple client projects where "smart" recommendations consistently underperformed simple, customer-centric approaches.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The reality hit me during a Shopify project last year. The client sold handmade jewelry—over 1,000 unique pieces across 50+ categories. They wanted to implement AI-powered product recommendations to "increase average order value like the big retailers."
Their existing setup was basic: "You might also like" sections showing random products from the same category. No algorithms, no machine learning, just simple category matching. But their data told an interesting story—customers were already discovering related products organically through their navigation and search.
Despite this, they insisted on upgrading to a sophisticated recommendation engine. We implemented a collaborative filtering system that analyzed purchase histories, browsing patterns, and user similarities. The setup took three weeks and required integrating multiple third-party services.
The results after two months were disappointing. The AI recommendations had higher click-through rates (people were curious about algorithmic suggestions), but conversion rates dropped significantly. Customers were clicking on recommendations but not buying.
Here's what I discovered through user session recordings: the AI was recommending products based on algorithmic similarity, but it was ignoring practical customer needs. For example, someone buying earrings for a wedding would get recommended other "algorithmically similar" earrings instead of complementary items like matching necklaces or bracelets.
The system was optimizing for data patterns instead of customer intent. It knew that people who bought Item A often looked at Item B, but it didn't understand why customers were actually making purchases.
This experience forced me to question everything I thought I knew about recommendation systems. The most sophisticated solution wasn't necessarily the most effective one.
Here's my playbook
What I ended up doing and the results.
After the jewelry store experiment failed, I developed a completely different approach. Instead of starting with algorithms, I started with customer psychology and worked backward to the technology.
My three-layer recommendation framework now looks like this:
Layer 1: Intent-Based Rules (Foundation)
I begin every recommendation system with simple, logic-based rules that mirror natural customer behavior. For the jewelry store, this meant:
Product viewing triggers recommendations for complementary items (earrings → necklaces)
Cart additions show matching sets or occasion-appropriate bundles
Category browsing surfaces best-sellers and trending items within that category
Layer 2: Behavioral Context (Enhancement)
Once the foundation rules are converting well, I add behavioral intelligence:
Time-based recommendations (weekend browsers see different products than weekday browsers)
Price-point matching (customers viewing $50 items see similar price ranges)
Purchase history influence (previous buyers see new arrivals in preferred categories)
Layer 3: Algorithmic Refinement (Optimization)
Only after layers 1 and 2 are performing do I consider machine learning:
A/B testing different recommendation approaches for specific user segments
Collaborative filtering for edge cases where rule-based systems lack data
Machine learning to optimize the timing and placement of recommendations
For the jewelry client, I rebuilt their system using this framework. Layer 1 alone improved their conversion rate by 34% compared to the AI system. We never needed to implement Layer 3—the simple rules were performing so well that adding complexity would have been counterproductive.
The key insight: start with customer psychology, not algorithms. Technology should amplify natural buying patterns, not replace human understanding with mathematical models.
This approach has now worked across multiple industries—from e-commerce optimization to SaaS feature recommendations. The framework scales because it's built on understanding customer intent rather than chasing technological sophistication.
Customer Psychology
Focus on intent before algorithms - understand what customers actually want to buy together
Conversion Testing
A/B test simple rules against complex systems - measure actual revenue impact not engagement metrics
Implementation Speed
Start with basic rules that work immediately rather than waiting months for AI training
Data Requirements
Simple systems work with limited data while AI needs substantial behavioral datasets to perform well
The results spoke louder than any algorithm could. After implementing the three-layer framework starting with simple intent-based rules:
Immediate Impact (First Month):
Conversion rate improved by 34% compared to the AI-powered system
Average order value increased by 23% through better complementary product matching
Customer satisfaction scores improved—people were finding what they actually wanted
Long-term Performance (6 Months):
System maintenance time dropped from 15 hours/week to 2 hours/week
Implementation cost was 80% lower than the machine learning solution
Revenue attribution to recommendations increased from 12% to 28% of total sales
The most surprising outcome wasn't the improved metrics—it was how much easier the system was to understand and optimize. When a rule-based recommendation wasn't performing, we could immediately see why and fix it. With the AI system, poor performance was a black box that required expensive consulting hours to diagnose.
This pattern has held across multiple client projects. Simple, well-designed recommendation rules consistently outperform complex AI systems for businesses with under 10,000 monthly transactions. The complexity only adds value when you have massive scale and dedicated data science resources.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this framework across dozens of e-commerce projects, here are the seven critical lessons that will save you months of frustration:
Customer psychology beats algorithmic sophistication every time. Understanding why people buy together matters more than mathematical similarity models.
Start with rules that mirror natural shopping behavior. If customers naturally look at matching accessories after viewing a dress, build that into your system first.
Measure conversion rate, not click-through rate. High engagement on recommendations means nothing if people aren't actually buying.
Simple systems are easier to optimize and maintain. When something isn't working, you can immediately understand why and fix it.
Scale determines complexity needs. AI makes sense for Amazon-scale operations, not for stores with hundreds of products and thousands of customers.
Context matters more than computational power. Knowing someone is shopping for a wedding gift is more valuable than knowing their browsing history.
Build incrementally—don't start with the most complex solution. Layer 1 rules often solve 80% of the problem without any AI needed.
The biggest pitfall to avoid: implementing complex recommendation systems because competitors have them or because they sound impressive. Technology should solve real customer problems, not showcase engineering capabilities.
This approach works best for small to medium e-commerce businesses with clear product relationships and customer purchase patterns. It doesn't work as well for marketplaces with millions of products or businesses where customer behavior is truly random.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS products, apply the same intent-based approach:
Recommend features based on user goals rather than usage patterns
Show relevant integrations when users are setting up specific workflows
Surface upgrade prompts when users hit logical feature limits
For your Ecommerce store
For online stores, focus on shopping psychology:
Match complementary products to customer purchase intent
Show trending items within relevant categories during browsing
Bundle related products at checkout for increased order value