Ecommerce Revenue Analytics: Track and Optimise Your Online Store Revenue

Revenue is the scoreboard—but revenue analytics is how you learn why the score changed and what to do about it. Strong revenue analytics goes beyond “total sales” and shows which customers, products, and channels actually create sustainable growth.

What is ecommerce revenue analytics?

Ecommerce revenue analytics focuses specifically on measuring and analysing the money your store makes—then breaking it down so you can improve it. It includes revenue trends, revenue drivers, and efficiency metrics like revenue per visitor/session.

Key revenue metrics to track

The most useful revenue metrics are the ones you can control with clear actions (pricing, offers, merchandising, traffic quality, checkout friction).

Total revenue and revenue growth

Track total revenue, then growth rates week-over-week and year-on-year. Growth is commonly calculated as (current − previous) ÷ previous × 100. This helps you separate seasonal peaks from genuine improvement.

Revenue by channel

Break revenue down by channel: organic search, paid ads, email, social, referral, direct. Shopify’s analytics reporting is designed to help merchants understand visitor insights and performance, which is the kind of “slice and compare” view you want for channel revenue analysis.

Practical use: if paid social revenue is flat but CAC is rising, you can shift budget towards a channel with stronger revenue per visitor, or improve landing pages for that audience instead of spending more.

Revenue by customer segment

Split revenue into new vs returning customers and into high-value vs low-value cohorts. This tells you whether growth is coming from “more customers” or “better customers,” and it helps you prioritise retention work when repeat buyers drive profit.

Revenue by product category

Analyse revenue by category, and then pressure-test it against returns, discounts, and stock-outs. Category reporting is how you avoid the trap of “topline growth” created by heavy discounting. Shopify also highlights merchandising tactics and products bought together as a use case for its analytics reporting, which is exactly what category-based analysis supports.

Revenue per visitor/session

Revenue per visitor is typically total revenue ÷ total visitors. It’s ideal for comparing traffic sources and landing pages because it tells you which visitors are worth more—not just which channel sends the most clicks.

How to analyse revenue trends

Start with time comparisons: month-over-month and year-on-year. Then look for “drivers”: did conversion rate change, AOV change, traffic change, or mix change (different products or channels)? If your site performance shifts, it can directly impact revenue metrics—research on mobile site speed found that improvements correlate with conversions and funnel progression, which is often why revenue changes even when traffic stays flat.

Revenue attribution: understanding what drives sales

Attribution is how you assign credit across marketing touchpoints. “Last click” credits the final touchpoint before conversion, while other models spread credit across multiple interactions. Google Ads documents multiple attribution models (including last click and data-driven approaches), and multi-touch attribution is commonly defined as assigning credit to multiple touchpoints along the journey.

A simple approach most stores can use early: track both first-touch (what brings people in) and last-touch (what closes) so you can see the difference between demand creation and demand capture. Then move towards multi-touch options when your tracking and data quality are mature.

Using revenue data to make decisions

Revenue analytics becomes useful when it changes operations: where you allocate ad budget, which products you invest in, how you plan inventory, and whether you adjust prices/offers. If you can’t answer these questions quickly, tools that reduce manual reporting and make analysis self-serve can shorten the decision cycle (Daymark’s “ask naturally → get results” approach is one example of that direction).

Conclusion

Ecommerce revenue analytics is how you stop treating revenue as a single number and start treating it as a system. Track revenue by channel, customer type, and product category; monitor revenue efficiency (RPV); and use attribution to understand what’s actually driving growth. That’s how revenue turns into strategy.