Getting Started With eCommerce Product Analytics: A Stormly Setup Guide

Most Shopify store owners have at least two analytics tools installed already. They have Shopify Analytics, they have GA4 (maybe), and if they’re ambitious, a heatmap tool and a Facebook pixel. And yet the core question that drives every product decision, which of my 200 products is actually converting and which one is silently killing my cart abandonment rate, stays unanswered.

That’s not a data problem. You have plenty of data. It’s a tool fit problem.

Session analytics (GA4, Shopify’s native reports) were built to answer marketing questions: how many people came, where from, and what was the total conversion rate. They were not built to answer product questions: which specific SKUs are in 80% of abandoned carts, which products drive a second purchase within 60 days, or which items appear in high-AOV orders versus low-margin one-and-done transactions.

If you’re not yet clear on why these are fundamentally different, what eCommerce product analytics actually means explains the distinction. This guide assumes you’ve made that call and you’re ready to set it up.

Before You Touch Any Tool: Define Your Three Starting Questions

The most common mistake in setting up product analytics is connecting your data first and then trying to figure out what to look at. You end up with a dashboard full of numbers, no priority, and the same problem you started with.

Instead, start with the three product questions your team argues about most but can’t currently answer with data. For most eCommerce operators, they’re variations of:

1. Which products have the highest conversion rate at the product-detail-page level, not just the store level?
2. Which products are in the most abandoned carts, and is it the same customers who abandon again and again?
3. Which products cause a customer to come back and buy a second time, versus which ones are one-time purchases?

If you don’t know which questions apply to you, running a quick eCommerce analytics audit helps you identify what your current setup can and can’t answer before you start adding tools.

Write down your three questions. Everything you build from here should answer those.

Step 1: Connect Your eCommerce Data Source

Stormly connects to Shopify and WooCommerce directly, and also accepts event data via Segment or MParticle for stores with custom stacks.

For a Shopify store, the connection flow is:

• Install the Stormly Shopify integration from your Shopify admin
• Stormly pulls order history, product data, customer IDs, and session-to-order links automatically
• Historical data (typically 12–24 months) loads in the first sync, depending on store size

For WooCommerce, the setup uses the Stormly JavaScript snippet plus an order webhook that fires on purchase completion. If your store processes subscription orders or has a headless architecture, you’ll want to map product events (add-to-cart, checkout-started, purchase-completed) to the Stormly event schema before your first sync.

One thing worth doing on day one: verify that your product IDs in Stormly match your SKU naming convention in your store backend. A store with variant-level tracking (color, size, bundle) needs variant IDs mapped correctly, or your cart abandonment data will be aggregated to the parent product instead of the specific variant. That distinction matters. A jacket with a 4% cart-add-to-purchase rate overall might have a 28% rate for size M in black and a 1% rate for size XS in white, and those two products need different interventions.

Step 2: Run These Five Reports in Your First Session

You have data connected. Now run these five reports, in this order. They answer your three starting questions and surface four or five more problems you didn’t know you had.

1. Product Conversion Rate by SKU

This shows conversion rate (product page view to purchase) broken down by individual product. Not store-level conversion. Product-level. In a Stormly implementation for a fashion retailer with ~300 SKUs, the typical distribution looks like: top 20 SKUs at 6–14% product conversion, the middle 200 at 1–4%, and a long tail of 60–80 products below 0.5%. That bottom tail is burning ad spend and cluttering your catalog without returning customers.

For a deeper guide on reading this report and acting on it, finding your best-converting products with product analytics walks through the decision logic after you see the numbers.

2. Cart Abandonment by Product

Stormly’s cart abandonment report breaks abandonment rate by product, not session. You’ll see which individual SKUs appear most often in carts that were never completed. In most stores, 3–5 products account for a disproportionate share of total cart abandonment value. A product with 900 cart adds and 820 abandonments (91% abandonment rate) is a different problem than a product with a high price point and a 60% abandonment rate, and the solutions are different too.

3. Repeat Purchase Rate by Product

This is the report most analytics tools can’t give you at the SKU level. Stormly calculates the percentage of customers who purchased a specific product and then returned to buy again within 30, 60, and 90 days. A product with 800 sales and a 58% 90-day repeat rate is a loyalty anchor. A product with 12,000 sales and a 6% repeat rate is a one-time-buyer magnet. If you’re building a subscription funnel or trying to improve LTV, you want to know which products to put at the top of your acquisition funnel before you spend on them.

This is one of the most powerful starting points in eCommerce customer retention analytics: the product-level repeat purchase data changes which products you promote and to whom.

4. Cohort Analysis by First Purchase Product

Which product a new customer buys first predicts how likely they are to return. Stormly’s cohort report lets you group new customers by their first purchased product and then track their 30/60/90-day return rate. You’ll often find that certain entry-point products (low price, high discovery) generate very different long-term value than others. A $15 accessory might have a 44% 60-day return rate. A $150 hero product might have an 11% return rate. That changes how you think about your acquisition strategy.

5. Revenue by Product Category With AOV Overlay

Before you do anything else: check whether your category architecture in Stormly matches your store navigation. If category assignments are off, this report is noise. If they’re clean, it shows you which categories are growing in revenue, which are shrinking, and which have high revenue but low average order value (suggesting margin compression or heavy discounting).

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You’ve now got answers to most of your starting questions, plus four or five new ones. This is normal. The goal at this stage is not to act on everything. It’s to know what you’re dealing with.

Set up Stormly for your store and run these reports today → Start your free trial

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Step 3: Set Up the Weekly Review Routine

Product analytics data goes stale fast. A product that was converting at 8% last week might drop to 3% after a competitor starts discounting. An anomaly in cart abandonment that appears on Tuesday and isn’t caught until Friday means four days of wasted ad spend driving traffic to a broken product page.

Build a weekly review cadence. Every Monday, open Stormly and check three things:

• Product conversion rate changes week-over-week: any product that dropped more than 2 percentage points
• Cart abandonment spikes: any product with abandonment rate increase of 10%+ week-over-week
• New cohort behavior: how customers who purchased in the last 14 days are trending on repeat purchase velocity

If you want a structured template for this review, the weekly Shopify analytics action plan gives you a repeatable Monday routine that takes about 20 minutes and generates specific weekly actions, not just a bunch of charts to stare at.

The other habit worth building in week two: set up Stormly’s anomaly alerts. You configure a threshold (for example, cart abandonment on your top-10 SKUs exceeds 85%), and Stormly sends an alert automatically. You stop catching problems in the weekly review and start catching them the day they happen.

What the First 30 Days Actually Look Like

Week 1 is mostly data validation. You connect your store, run the initial reports, and spend time verifying that the numbers make sense. Compare Stormly’s order count for a given week to your Shopify admin export for the same period. If they’re within 2–3%, you’re good. If there’s a larger discrepancy, check whether your variant mapping is correct and whether historical order data for cancelled or refunded orders is being excluded or included.

Week 2 is where the first real decisions start. You’ll have enough product-level conversion data to identify the bottom 10–15% of your catalog that’s consuming marketing budget without generating repeat buyers. You don’t need to act yet. Just flag it.

By week 3, you’ll have enough cohort data to see the beginning of 30-day repeat purchase behavior for customers who bought in week 1. This is your first signal on which entry-point products are building loyalty versus which are attracting one-time buyers.

Week 4 is when most teams make their first product-level decision based on the data: pulling a low-conversion product from paid traffic, repositioning a high-repeat-rate product as the lead item in a welcome email sequence, or adjusting pricing on a high-abandonment SKU.

For teams that want to track the full 7 eCommerce KPIs that actually drive decisions alongside this setup, building a Stormly dashboard with those metrics alongside the product-level data makes the weekly review faster and easier to share with your team.

The Setup Is Not the Hard Part

The technical setup of eCommerce product analytics takes a few hours, not weeks. What takes longer is building the habit of using product-level data to make decisions instead of reverting to gut feel or top-level revenue numbers.

The operators who get the most out of Stormly are the ones who run the five starter reports on day one, identify their first three product-level problems, and make at least one product change in the first 30 days based on the data. Not because the tool is magic, but because that first decision creates proof, and proof creates a team that trusts the data enough to use it consistently.

If you’re ready to set up eCommerce product analytics for your store, Stormly connects to Shopify and WooCommerce in a single session. The first month of data will tell you more about your catalog than the last two years of session-level reporting.

Start your free Stormly trial. Connect your store and run your first product-level reports today.