E-Commerce Analytics Guide

Analytics for an online store is useful when the numbers are defined, compared over time, and tied to a decision. It is not a wall of real-time widgets. This guide covers the metrics that actually describe store health, a simple reporting cadence, humility about attribution, and how to connect figures to actions.

Start with e-commerce metrics that matter. If you are still launching, how to start an online store covers the minimum instrumentation. Conversion work that uses these numbers is in the conversion optimization guide.

A small metric set

You can add more later. If these five are messy, extra dashboards will not help.

Metric	Meaning	Typical decision
Orders and net revenue	After refunds if material	Is the business moving?
Conversion rate	Orders ÷ sessions (define both)	Funnel vs traffic quality
AOV	Revenue ÷ orders	Mix, thresholds, bundling
Contribution	Revenue − COGS − variable shipping − payment fees − typical discounts	Can we afford ads and overhead?
Refund / return rate	Refunds ÷ orders or revenue	Product, sizing, COD, logistics

Then acquisition:

Metric	Meaning	Typical decision
CAC	Acquisition spend ÷ new customers	Channel mix, offer
ROAS	Ad-attributed revenue ÷ ad spend	Media efficiency, not profit
ROI	Net profit ÷ investment	Whether the campaign or project paid
LTV (contribution)	Expected contribution from a customer over a period	Ceiling on CAC

Definitions in depth:

• What is ROAS
• ROAS vs ROI
• What is customer acquisition cost
• Customer lifetime value explained
• Average order value explained

Run the math locally:

• ROAS calculator
• ROI calculator
• Conversion rate calculator
• Customer acquisition cost calculator
• Customer lifetime value calculator

Gross vs net, tax in vs tax out

Pick one revenue definition for the scorecard (usually net of refunds, tax treatment documented). Mixing tax-inclusive storefront totals with ex-tax ad revenue will create fake “channel” gaps. Same for marketplace payouts versus list price — use the marketplace fee logic when that channel matters.

Formulas you should be able to write from memory

Conversion rate = orders ÷ sessions
AOV = revenue ÷ orders
ROAS = ad revenue ÷ ad spend
Break-even ROAS (simple) ≈ 1 ÷ gross margin (then raise it for fees, shipping subsidy, returns)
CAC = spend ÷ new customers
Simple LTV = AOV × purchases per period × periods × gross margin

Write the date range and the currency on every screenshot. A 4x ROAS in a week of branded search is not the same as 4x on cold prospecting.

Reporting cadence

Daily (operations, not strategy)

• Can we fulfill? Payment and gateway errors
• Stock-outs on top SKUs
• Site down / checkout broken

Do not retarget the whole media plan because Tuesday was quiet.

Weekly (operating review)

Same weekday-to-weekday window when you can.

1. Orders, net revenue, conversion, AOV vs last week and vs last 4-week average
2. Split: device, new/returning, top 3 channels
3. Payment failure rate
4. Refunds started
5. Ad spend and platform-reported ROAS (labelled as such)
6. One sentence: what we will change next week

This is also where CRO experiments get a row in the changelog (conversion optimization guide).

Monthly (economics)

• Contribution after COGS, shipping, fees
• Blended CAC vs paid CAC (CAC article)
• Cohort repeat (even a simple 30/60/90-day repeat rate)
• Inventory and returns as a tax on last month’s “revenue”
• LTV:CAC as a range, not a single heroic number

If monthly contribution cannot cover people, software and a conservative ad plan, weekly ROAS theatre will not save it. Go back to unit economics in how to start an online store.

Quarterly

• Channel mix: what you would cut if spend had to drop 30%
• Catalog: what actually sold vs what you photographed
• Measurement: broken events, consent mode changes, new UTMs

Attribution humility

No common model tells you the “true” path:

• Last click over-credits branded search and email
• Platform pixel over-credits the network that scored the conversion
• First click over-credits whatever introduced the cookie
• Data-driven in a suite is still a model with your data gaps

Practical rules:

1. Reconcile to orders you fulfilled, not to the highest dashboard.
2. Use UTM standards so you do not split the same campaign into five names. The UTM builder is only as good as your naming doc.
3. Judge incrementality when the spend is large: holdouts, geo splits, or simply “what happens if we pause this for a week” on a non-brand campaign.
4. Do not fire a well-performing brand campaign because last-click looks expensive; brand is often the closer.

SEO and direct traffic will overlap with ads. That is not theft; it is how people shop. Credit arguments are cheaper than incrementality tests, and they are usually worse.

Instrumentation that is worth doing

• Purchase event with order id, value, currency, tax/shipping if you will use them
• A server-side or gateway-based check against “thank you page only” pixels when you can
• Consistent item-level data if you will ever do product-level ROAS
• Refund events or a weekly finance join — otherwise all ads look more profitable than they are
• Internal links and SEO landing pages in the same revenue join as paid, even if attribution is messy

You do not need a customer data platform on day one. You need a spreadsheet or BI view that finance would not laugh at.

Connecting metrics to decisions

If a number cannot change what you do, demote it from the weekly page.

Observation	Better next step	Worse next step
Conversion down, AOV flat, paid share up	Segment by channel; likely mix, not a broken PDP	Redesign homepage
Conversion down on mobile checkout only	Checkout/payment research	More Instagram ads
ROAS up, contribution down	Margin, discounts, returns	Scale spend
CAC up, repeat strong	Check LTV; maybe still fine	Cut all acquisition
Refunds up on one SKU	Product/shipping/COD diagnosis	Sitewide 20% off
Sessions up, orders flat	Landing page and offer	Buy more cheap traffic

Tools exist to keep the arithmetic honest while you debate. They do not decide for you. Pair a conversion question with the conversion optimization guide. Pair a “can we even sell this” question with how to start an online store.

Contribution is the bridge. A channel that looks efficient on ROAS can still fail ROI once COGS, shipping and refunds sit on the same row. If you cannot produce that row monthly, stop adding dashboards.

Inventory and product-level views

Storewide conversion hides SKU truth. A handful of products often carry revenue; a handful of products often carry refunds. Monthly, list:

• Top SKUs by contribution, not only by revenue
• SKUs with high views and low add-to-cart (offer or PDP)
• SKUs with high refunds (description, size, COD, damage)
• Stock-outs on those top SKUs (you cannot convert what you cannot ship)

You do not need a perfect SKU P&L on week one. You need to stop scaling ads to a product that is about to run out or that comes back as a return.

A sample weekly scorecard

Keep one table. Add columns only when a decision needs them.

Field	Example definition
Window	Mon–Sun, store timezone
Orders	Paid, not draft; exclude test gateways
Net revenue	Captured minus refunds posted in the window (know the lag)
Sessions	GA4 (or platform) sessions; do not mix with users in the same cell
Conversion	Orders ÷ sessions
AOV	Net revenue ÷ orders or gross ÷ orders — pick one and label it
Contribution (est.)	Net revenue − COGS − shipping subsidy − payment fees
Ad spend	Invoices, not only pixel-reported
Platform ROAS	Labelled with the ad UI’s model and window
Payment failures	Failed attempts ÷ checkout starts
New customers	First order in your customer table, not the ad platform’s

Worked contribution sketch (illustrative arithmetic, not a benchmark): 200 orders, 1,500 AOV, 300,000 revenue. COGS 55%, shipping subsidy 40 per order, payment fees 2%. Contribution ≈ 300,000 − 165,000 − 8,000 − 6,000 = 121,000 before overhead and ads. If ads were 80,000, this week’s media did not “print money” even if the ad UI showed 4x ROAS on a subset of revenue. Re-run with the ROAS calculator and profit margin calculator on the same cost basis.

Refund lag and “new customer”

Refunds often post after the weekly window. A campaign week can look profitable until size returns arrive. Track refunds started in the week as a leading indicator, and a trailing 30-day refund rate by SKU.

“New customer” must mean the same thing in CAC and in LTV. First-time in your database is not the same as “new” in an ads audience. If you use CAC and LTV together, write the definition on the scorecard.

Channel grouping

Collapse UTMs into a short list: brand search, non-brand search, paid social, email, organic, direct, marketplace. facebook / cpc vs Facebook / paid is not two strategies. Document lowercase sources and a finite medium list. The UTM builder does not enforce discipline; your sheet does.

Direct and email will absorb branded demand. That is expected. Do not zero out brand search because last-click “proves” email closed it.

What not to do

• Optimize for add-to-cart and call it growth
• Report ROAS without stating the window, the attribution model, and whether refunds are in
• Treat LTV from a calculator as a fact (LTV article is explicit that simple models are planning tools)
• Average everything into one conversion rate and then argue about “the industry benchmark”
• Guarantee that better reporting will increase revenue — it will only make the next decision less fictional

Analytics is a language for trade-offs: traffic quality versus volume, conversion versus AOV, media efficiency versus contribution, acquisition versus retention. Keep the language small, the cadence boring, and the attribution claims modest. That is enough to run a store.

When a number looks impossible, assume a definition mismatch before you assume a business miracle. Check timezone, refund lag, tax inclusion and whether the ad platform is counting sessions you would not call orders. Then look at the funnel. Curiosity beats a new dashboard every time.