Ecommerce Analytics: What It Is and Why Your Online Store Needs It

Most online stores aren’t short on data—they’re short on clear answers. A 2025 global study found that 58% of leaders said key business decisions are often based on inaccurate or inconsistent data, and 65% said nobody in their organisation fully understands all the data being collected or how to access it. That’s what ecommerce analytics is supposed to fix: turning messy signals into decisions you can act on.

What is ecommerce analytics?

In simple terms, ecommerce analytics is the process of collecting and analysing data from your online store so you can understand what’s happening, why it’s happening, and what to do next. It helps you answer practical questions like: “Where are customers dropping off?”, “Which products drive profit?”, and “Which marketing channels are actually worth the spend?”.

What ecommerce data gets collected (and where it comes from)

Ecommerce analytics pulls from multiple “streams” of data. Your store platform typically tracks orders, revenue, products, refunds, and customer details. Website analytics measures behaviour like page views, product clicks, add-to-cart actions, and purchases. Marketing platforms track impressions, clicks, and conversions. When these are connected, you can see performance across the full journey—from first visit to repeat purchase.

Analytics vs reporting: what’s the difference?

Reporting is usually about showing what happened (for example, a dashboard that says revenue is down 8% this week). Analytics is about understanding the story and deciding what to do (for example, finding that mobile checkout drop-offs rose after a payment change, then prioritising a fix). In other words: reporting gives you structured visibility; analytics gives you insight and direction.

Why ecommerce analytics matters for online stores

Because ecommerce is measurable, it’s also optimisable. Good analytics lets you improve the parts of the funnel that make the biggest difference: product discovery, add-to-cart, checkout completion, and repeat purchases. It also helps you reduce waste—like spending on campaigns that generate clicks but not profit.

Types of ecommerce analytics (and what each helps you do)

Most ecommerce teams use four “levels” of analytics. These levels build on each other—so you don’t need to jump to advanced forecasting before you can trust your foundations.

Descriptive analytics (what happened)

This is the starting point: totals, trends, and summaries. Examples: weekly revenue, conversion rate by channel, top products, cart abandonment rate. Descriptive analytics is what most dashboards and weekly reports deliver.

Diagnostic analytics (why it happened)

Diagnostic analytics looks for causes. Examples: conversion rate fell—was it traffic quality, page speed, pricing, stock-outs, or a checkout bug? This is where segmentation, comparisons, and “drill-downs” matter.

Predictive analytics (what will happen)

Predictive analytics uses past patterns to forecast results. Examples: predicting demand for a product category, or forecasting revenue next month based on current traffic and conversion trends. This becomes more useful as you collect consistent, clean data over time.

Prescriptive analytics (what to do)

Prescriptive analytics goes one step further: it recommends actions. Example: “If you increase free shipping thresholds by £5 and bundle product A with product B, you can lift average order value without hurting conversion.” Prescriptive work often blends analytics with experimentation and operational constraints.

Why ecommerce analytics is critical for your business

When ecommerce analytics works, it changes how you run the business—less guessing, more controlled improvement.

First, it lets you make data-driven decisions instead of guessing. If your team can trust the numbers, you can move faster and reduce “opinion-led” debates. The opposite is also true: if you don’t trust the data, decisions slow down and you end up defaulting to gut feel.

Second, ecommerce analytics helps you understand customers more clearly—what influences them, what they buy together, and what causes them to abandon. Platforms like Shopify explicitly position their analytics dashboards and reports around helping merchants review activity, understand visitors, analyse performance, and improve sales and merchandising tactics.

Third, it helps you optimise marketing spend. When you can connect traffic sources to revenue (and ideally margin), you can reduce spend where it’s not paying back and double down where it is. Even basic channel reporting becomes far more useful when it’s tied to conversion and order data.

Finally, it helps you increase sales and revenue by improving the conversion funnel. Small improvements add up. For example, research on mobile site speed found that a 0.1-second change in load time can influence the user journey and that retail conversions increased (on average) when sites became faster. That’s a practical example of analytics leading to measurable growth: find friction → fix it → measure impact.

Popular ecommerce analytics tools

There isn’t one “best” tool—there’s the right tool for your store size, your team’s skills, and how fast you need answers. Here are four common options and where they fit.

Google Analytics 4

Good for understanding website behaviour, traffic quality, and conversion journeys across devices and platforms. GA4 supports ecommerce measurement through recommended events (for example, add_to_cart and purchase), which populate ecommerce reporting.

Shopify Analytics

Best if you run on Shopify and want store-native dashboards and reports tied directly to transactions. Shopify describes its analytics dashboards and reports as a unified experience to review store activity, understand visitors, analyse web performance, and analyse transactions—and use that insight to increase sales and improve merchandising decisions.

Daymark (natural language queries)

If your biggest problem is “I have dashboards, but I still can’t get quick answers,” Daymark is positioned around asking questions in plain language and getting charts, tables, and summaries that directly answer the question—without digging through dashboards or waiting for reports. That can be a big unlock for non-technical teams that need self-serve analytics.

Quick comparison

Tool	Best for	Strength	Watch-outs
Google Analytics 4	Website behaviour + funnels	Powerful behaviour and funnel exploration; ecommerce events	Setup quality matters; reporting can feel complex
Shopify Analytics	Store + transaction reporting	Native, unified dashboards tied to orders and visitors	Less flexible across non-Shopify data sources
Daymark	Fast answers for non-technical teams	Natural language questions → charts/tables/summaries	Still needs clean, connected data to be reliable

Getting started with ecommerce analytics

A practical “getting started” plan is simple: track the right actions, focus on a small set of KPIs, and tighten data quality before you add complexity. This matters because inaccurate or inconsistent data is a common failure mode—one that leaders already recognise as a real business risk.

Start by ensuring basic tracking is in place for your conversion funnel (product views → add to cart → begin checkout → purchase). GA4’s ecommerce guidance is structured around capturing recommended events like add_to_cart and purchase.

Next, pick 5–7 key metrics that match your current goals (profit, growth, efficiency, or retention). If you try to track everything at once, you’ll create dashboards—but not decisions.

Then connect your sources (store platform + web analytics + marketing + email/CRM) so you can answer “what caused this?” instead of just “what happened?”. Shopify’s analytics approach emphasises a unified dashboard and reporting experience, which is the direction most teams need to move in—even if you’re using multiple tools behind the scenes.

Conclusion

Ecommerce analytics is not about collecting more data—it’s about getting to confident decisions faster. When you track the right actions, focus on a small KPI set, and use tools that make analysis accessible, you can improve conversion, reduce wasted spend, and understand customers in a way that actually changes how you run the store. The biggest win is consistency: clean tracking, regular review, and a habit of turning insights into action.