It’s not always easy to get a clear picture. Your store's order report shows one revenue figure, while Google Analytics 4 (GA4) shows another. Your advertising accounts may tell a different story again. Each system measures something different, so effective ecommerce website analytics starts with understanding where the figures come from and what they mean.

In this guide, we'll explain where ecommerce data comes from and how GA4 measures each step of the buying journey. You'll learn how to check whether your numbers are accurate and use the results to make better decisions. We'll also cover the differences between B2B and consumer ecommerce.

What ecommerce analytics is

Regular web analytics tells you how visitors find your website and what they do once they arrive. Ecommerce analytics adds the store's transaction records to the mix, so you can cross-reference browsing behavior with actual purchases. Your store records which products customers buy and how much money changes hands. You can see how much customers spend, as well as what they return on the back end. Combined with your website activity, this information helps you understand how people move through the buying journey and where their actions lead to sales.

You may also hear this called ecommerce performance analytics. It's the same practice: measuring how well your online store performs using data from the website and connected systems. Ecommerce analysis covers individual product performance, as well as the results of marketing campaigns. Teams use analytics for ecommerce to compare their store's performance over time and make better decisions for the future.

The importance of analytics in ecommerce becomes clear when you start investigating parts of the buying journey that aren't working so well. For example, if a product attracts plenty of views but generates very few orders, you have a reason to examine its page more closely. Other useful capabilities include seeing where shoppers drop off and which marketing channels bring customers who buy. When you change something on the website, you can compare the results to see whether your intervention worked. You’re now basing decisions about product pages and marketing spend on customer behavior and purchases rather than gut feeling.

Where ecommerce data comes from

Ecommerce data analytics draws information from several sources, each capturing a different part of the buying journey. Your store tracks sales, while analytics and marketing systems record how shoppers interact with your website and campaigns. Good data analytics for ecommerce depends on knowing which source can answer each question.

This is particularly important for agencies, where data analytics in ecommerce often involves comparing figures across several systems. Ecommerce and data analytics teams need access to these sources to understand how website activity relates to orders.

The different types of ecommerce analytics also help you distinguish between sales performance and customer behavior. 

Your store platform holds the official record of orders and revenue, while GA4 records website behavior. You can also use Google Search Console to see which searches bring people to your store, or Microsoft Clarity to examine session recordings and heatmaps. For a comparison of the available products, see our guide to website analytics tools. 

GA4 ecommerce events: the measurement layer

GA4 provides ecommerce web analytics by recording events as shoppers move through the buying journey. Google recommends 14 ecommerce events, covering everything from viewing a product to completing a purchase and receiving a refund.

The 14 events, step by step through the buying journey 

Each GA4 event records a particular action, such as viewing a product or adding it to a cart. Together, they let marketers track the customer's progress through the store and see where they move forward or lose interest.

Web analytics for ecommerce depends on tracking what happens throughout the buying journey. These events provide that information and form the basis of ecommerce customer journey analytics. The table below shows the actions each event records.

Source: Google for Developers, Measure ecommerce.

You can use these events to build a funnel showing how shoppers progress toward a purchase. For example, compare the number of shoppers who add something to their cart with those who begin checkout. A large drop gives you a useful place to investigate. Keep in mind that raw event counts won't always decrease at each stage. Someone might view several products or add multiple items to their cart before buying. Compare the same measurement across stages to get a meaningful picture of where people leave.

Check which events your store currently sends to GA4. Any missing events leave gaps in your ecommerce website analytics, making it harder to see what's happening at that point in the buying journey. 

The parameters that make the numbers usable

 GA4 events tell you what shoppers do, but the parameters attached to them provide the detail you need to make sense of the results. The “currency” parameter identifies the currency associated with an event's value, such as USD or EUR. Google requires it whenever an event sends a “value” parameter. Without the correct currency information, GA4 can calculate revenue incorrectly, making it harder to compare sales across different markets.

The “items” parameter records the products involved in an event. It can contain up to 200 items, each with information such as item_id, item_name, price, quantity, and item_category. This lets you see which products shoppers interact with and how different product categories perform. It also lets you compare product activity across the buying journey, giving you more detail than an overall purchase total.

“transaction_id” identifies the order associated with a purchase or refund event. Google requires this parameter for both events. Each order needs its own ID, allowing you to match the purchases GA4 records with the corresponding orders in your store. This becomes particularly useful when checking whether your revenue figures are accurate.

For more on setting up website measurement, see our guide on how to monitor website traffic.

The metrics this data gives you

Once you've connected your website activity with store records, you can measure how well your store converts visitors into customers and how much those customers spend. Here are the main metrics and where to find the data for each:

• Conversion rate: GA4 sessions and purchases show how often visits result in a sale.

• Average order value: Your store's order records show how much customers typically spend in a single transaction.

• Cart abandonment rate: GA4 cart and checkout events help you measure how often shoppers leave before completing a purchase.

• Customer lifetime value: Your store's customer and order history shows how much customers spend over the course of their relationship with you.

• Customer acquisition cost: Advertising and email spending, combined with new customer records from your store, show how much you spend to acquire customers.

• Return on ad spend: Advertising costs and revenue figures from your store or GA4 help you assess what your campaigns bring in.

• Repeat purchase rate: Your store's customer history shows how often customers come back to buy again.

For the formulas, benchmarks, and help choosing the most important figures to track, see our guide to ecommerce metrics and kpis.

How to check the data is right

 Your GA4 revenue and store revenue figures will not match exactly. These ecommerce analytics best practices will help you measure the difference and understand what's causing it.

1. Compare a week of GA4 purchases with your store’s orders

Choose the same seven-day period and time zone for both systems. GA4 will generally record fewer purchases because some visitors decline consent, use ad blockers, or complete orders without the purchase event firing. Under basic consent mode, Google tags send no data until consent is granted. Advanced consent mode allows tags to send consent information and measurements without cookies when visitors decline. Our guide to cookieless website analytics explains how these choices affect measurement. 

2. Check transaction_id

Every purchase event should have a unique transaction ID that matches an order in your store. Missing or duplicated IDs can make it harder to reconcile GA4 purchases with your actual sales records. 

3. Check currency

Every event that sends a value must also send the correct currency. Check that the currency matches the transaction, particularly if your store sells internationally. Incorrect information can distort the revenue GA4 reports. 

4. Check the items

Compare the product IDs and names in GA4 with those in your store's catalog. They need to match so you can identify the same products across both systems and trust your product-level reports. 

5. Check refunds

Confirm that refunded orders generate a refund event in GA4. If these events are missing, GA4 won't account for the money returned to customers, overstating the revenue associated with its recorded purchases.

6. Check campaign tagging

Make sure links in your email campaigns and paid ads include the correct utm parameters. Without these tags, GA4 may record visits as direct or unassigned traffic, making it harder to establish which campaigns brought customers to your store. See our guide to utm parameters for more detail.

If you're an agency analyst checking a new client's store, record the difference between GA4 revenue and store revenue as a percentage. Include that gap alongside the revenue figures in your reports, so your client can see exactly how the numbers compare. 

From data to decisions

Ecommerce performance analytics becomes useful when you start with a question about your store and find the data that will help you answer it. You can then focus on the changes most likely to boost sales.

The following ecommerce analytics use cases show how teams can use analytics in ecommerce to make better decisions.

The trick is to tackle one question at a time. For example, if you notice shoppers regularly add products to their carts but leave before completing checkout, investigate where they drop off and decide which part of the process needs attention. Make one change, then check the results a few weeks later. 

Compare your findings against your store's own historical performance. That gives you a more useful picture of whether things are improving. Be careful about jumping to conclusions. A drop in purchases tells you where to investigate, but you'll need to examine the relevant part of the buying journey before deciding what to change.

You can use a web analytics dashboard to keep the questions your team asks every week on one screen. Then use a web analytics report to document your monthly findings and the changes you've made. This approach to ecommerce business analytics gives you a way to test changes against your store's own results.

B2B ecommerce analytics

Most ecommerce guides assume a consumer makes a purchase through an online checkout. In B2B, several employees may research products before their company buys anything. Customers can also order through existing accounts or place orders directly with sales representatives, so some transactions don't appear in the website's records.

How B2B stores buy differently

In B2B ecommerce, the customer is usually a company. Several employees might research products before anyone places an order. They could visit the store repeatedly over several weeks, sometimes using different devices. Standard website analytics records these visits as separate interactions, making it difficult to see how much interest comes from the same business. For a B2B seller, understanding activity at the company level is particularly important. 

The checkout process can also look very different. Wholesale stores and trade portals often let customers order through existing accounts, sometimes at prices agreed with individual businesses. Other buyers request a quote before committing to a purchase. If you only measure completed online transactions, you'll miss these expressions of buying interest. A quote request could represent a valuable sales opportunity, even though no money has changed hands yet.

Some customers place repeat orders directly with sales representatives or over the phone. The business records these transactions in its CRM or ERP, so they never appear as purchases in GA4. This creates a gap in the store's online reporting, especially when you're trying to understand the value of existing customers. B2B measures such as revenue per account and reorder rate can help here, and we cover those in our separate guide to ecommerce metrics and KPIs.

Measure the account, not the shopper

To get a more accurate picture of B2B ecommerce analytics, measure activity at the company level. Connect your website data with the account records in your CRM or ERP. That way, you can assign purchases made through the online store, a sales representative, or over the phone to the same company. You can then see how much business each account generates across its different ordering channels, including purchases that GA4 doesn't record. 

You'll also need to track quote requests. If customers request a price before ordering, record that action as a website event. Google's recommended lead generation events include generate_lead, which lets you count inquiries from potential customers even when they haven't placed an order.

You can use website intelligence to understand which companies research your store before anyone logs in or buys. Leadfeeder uses reverse ip lookup to identify visiting businesses at the company level, without identifying individual shoppers. Its Web Visitors tool lets you identify visiting companies and see which pages they've viewed, including wholesale pricing or trade account pages. Leadfeeder Web Insights gives you a broader picture through ready-made reports showing how many companies come through each channel and page. You can also see how many of those companies fit your ideal customer profile (ICP), and the percentage they represent. These company-level figures add another perspective alongside GA4's session and user reports. Getting started takes one tracking script, ready in minutes. Install it on your store, then check Web Visitors after a week to see which companies have been looking at your wholesale pricing or trade account pages.