Sales & Conversion
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Over the years, I've had the opportunity to work with about a dozen ecommerce projects. When I started, I was convinced that custom solutions and advanced analytics were the holy grail. I spent months building beautiful Webflow stores with headless Shopify backends, thinking I was giving clients the "best of both worlds."
Here's what actually happened: I'd wake up to urgent emails about checkout issues, inventory sync problems, and broken analytics. What I'd built wasn't a sustainable solution—it was a maintenance nightmare wrapped in beautiful design.
After migrating all those projects to native Shopify, I learned something crucial: the platform you choose fundamentally changes how you understand your business performance. Shopify's analytics aren't just reporting tools—they're designed around a commerce-first philosophy that treats your website as one sales channel among many.
In this playbook, you'll discover:
Why Shopify's "limited" analytics are actually a feature, not a bug
The hidden costs of "better" analytics platforms that nobody talks about
Real performance comparisons from my client migrations
When Shopify analytics fall short (and what to do about it)
My framework for choosing the right analytics setup for your store
If you're tired of drowning in data that doesn't drive decisions, or if you're considering a platform migration, this guide will save you months of trial and error.
Reality Check
What everyone says about Shopify analytics
The ecommerce world loves to bash Shopify's analytics. Every comparison article, every "expert" review, every agency pitch follows the same script: Shopify's built-in analytics are "basic," "limited," and "not suitable for serious businesses."
Here's what you'll typically hear:
"Shopify analytics lack depth" - Critics point to the simplified dashboard and limited segmentation options
"You need Google Analytics for real insights" - Everyone recommends layering on GA4, enhanced ecommerce tracking, and custom events
"Enterprise needs enterprise analytics" - The assumption that bigger business requires more complex reporting
"Custom tracking is essential" - The belief that you need to track every micro-interaction and custom conversion
"Real-time data is critical" - The obsession with minute-by-minute performance monitoring
This conventional wisdom exists because most people approach ecommerce analytics like they're running a content website or a SaaS platform. They want attribution models, funnel analysis, cohort reporting, and sophisticated segmentation.
But here's where this thinking falls short: ecommerce isn't about analyzing user behavior—it's about optimizing commercial performance. When you're selling products, the metrics that actually matter are revenue, profit margins, inventory turnover, and customer lifetime value. Everything else is just noise that distracts from making money.
The industry's obsession with complex analytics has created a generation of store owners who can tell you their bounce rate but can't tell you their profit margin.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I first started building ecommerce sites, I was completely bought into the "more data equals better decisions" philosophy. I'd spend hours setting up custom Google Analytics events, building elaborate dashboards, and creating complex attribution models for my clients.
The breaking point came with a particular fashion client who had over 1,000 SKUs. I'd built them a beautiful Webflow frontend with a headless Shopify backend, complete with custom analytics tracking every possible interaction. The setup looked impressive in client presentations.
But here's what actually happened: The client spent more time trying to understand their analytics dashboard than actually running their business. We'd have weekly calls where they'd ask me to explain why their Google Analytics numbers didn't match their Shopify numbers, why their attribution was showing different sources, and why their conversion rates varied depending on which tool they looked at.
Meanwhile, their actual business problems weren't being solved. They couldn't quickly see which products were profitable, which inventory was moving slowly, or how their email campaigns were affecting overall revenue. The "sophisticated" analytics I'd built were creating analysis paralysis instead of actionable insights.
The final straw came when the custom integration broke during their biggest sale of the year. While I was troubleshooting API connections and data sync issues, they were losing real money. The beautiful analytics dashboard was useless when the actual commerce functionality was down.
That's when I realized I was treating ecommerce like a tech product instead of what it actually is: a commercial operation that needs to focus on buying, selling, and profit optimization.
Here's my playbook
What I ended up doing and the results.
After that disaster, I completely restructured my approach to ecommerce analytics. Instead of starting with "what can we track," I started with "what decisions need to be made." This shift led me to migrate all my projects to native Shopify and embrace its commerce-first analytics philosophy.
Here's the framework I developed:
Step 1: Commercial Metrics First
I stopped tracking vanity metrics and focused on what actually drives profitability. In Shopify's dashboard, this means obsessing over revenue per visitor, average order value, and profit margins by product. These metrics directly translate to business decisions: which products to promote, what pricing strategies to test, and where to allocate marketing spend.
Step 2: Channel Performance Over Attribution
Instead of trying to track every touchpoint in a customer's journey, I focused on channel-level performance within Shopify's marketing reports. This approach acknowledges the reality of modern customer behavior: people research on multiple devices, get influenced by various touchpoints, and make decisions based on factors you can't track.
Step 3: Operational Intelligence
Shopify's inventory reports, customer behavior analysis, and sales performance data became the foundation for operational decisions. Unlike Google Analytics, which tells you about website traffic, Shopify analytics tell you about commercial performance: which products need restocking, which customers are worth retaining, and which marketing efforts actually drive revenue.
Step 4: Integration Over Isolation
Rather than viewing analytics as a separate system, I leveraged Shopify's integrated approach where analytics are built into every aspect of store management. Inventory management informs marketing decisions, customer data drives email campaigns, and sales performance guides product development.
The key insight was treating Shopify not just as an ecommerce platform, but as a complete commerce operating system where analytics serve commercial decisions rather than analytical curiosity.
Simplicity Wins
Analytics should drive action, not analysis
Clear Metrics
Focus on revenue per visitor, not page views
Commerce Context
Built for selling, not just tracking
Integration Power
Analytics connected to inventory, customers, sales
The results of this commercial-first approach were dramatic across multiple client projects. Store owners went from spending hours trying to interpret complex data to making quick, confident business decisions based on clear commercial metrics.
The most significant change was decision-making speed. With Shopify's integrated analytics, clients could quickly identify which products were profitable, which marketing channels were working, and which inventory needed attention—all from a single, coherent view of their business.
Perhaps more importantly, the maintenance burden disappeared completely. No more broken tracking codes, no more data discrepancies between platforms, and no more technical issues during crucial sales periods. The analytics just worked, allowing store owners to focus on what actually matters: buying, selling, and growing their business.
Client satisfaction improved because they finally had analytics that matched their commercial reality rather than abstract user behavior metrics that didn't translate to business value.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Commercial metrics beat vanity metrics every time - Revenue per visitor matters more than bounce rate
Integration trumps sophistication - Connected data is more valuable than detailed data
Maintenance costs are hidden costs - Complex analytics setups require ongoing technical support
Decision paralysis is real - Too much data often leads to delayed or poor decisions
Platform philosophy matters - Choose tools designed for your specific business model
Analytics should serve commerce, not curiosity - Every metric should drive a business decision
Simplicity scales better than complexity - Clear, consistent metrics grow with your business
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus on metrics that drive subscription decisions: trial conversion rates, churn analysis, and customer lifetime value calculations rather than complex user behavior tracking.
For your Ecommerce store
For ecommerce stores, prioritize commercial intelligence: inventory turnover, profit margins by product, customer acquisition costs, and channel performance over sophisticated attribution modeling.