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Your e-commerce dashboard, reconciled to the storefront.

Orders, GMV, average order value, net revenue, and refund rate in one view, reconciled from your storefront, payments, and ad spend, so the revenue number and the store number agree.

See how to build one in Definite

What’s in a e-commerce dashboard?

An e-commerce dashboard is the single governed view of store performance: how many orders are coming in, gross merchandise volume, average order value, net revenue after refunds, and what the ad spend is returning. The version worth optimizing on reconciles the storefront, the payment processor, and the ad platforms, so the revenue the store reports is the revenue that settled.

Shopify reports one revenue number, Stripe reports another, and Google Ads claims credit for a third. When orders, GMV, and net revenue come from one set of definitions modeled on the actual systems, you optimize on a number that ties to the bank, and you catch the refund spike or the ROAS drop before it eats the margin.

Who it’s forE-commerce operators, CFOs, and founders who own the store P&L.

CadenceRefreshed daily; reviewed in the weekly store review and before seasonal planning.

Built fromShopify, Stripe, Google Ads

§ How it works

Describe your dashboard. Fi builds it.

Fi is the AI agent inside Definite. Tell it what you’re trying to understand, and it connects your sources, defines the metrics, and builds the dashboard. One conversation, not a project.

You

I need one view of the store: orders, GMV, AOV, and net revenue, reconciled between Shopify, Stripe, and what we are spending on ads.

✦ Fi

Here's your e-commerce dashboard, on your Shopify, Stripe and Google Ads data.

Here’s what’s in it

The top row leads with the 4 numbers that matter most: Orders, GMV, Avg order value, Net revenue. Each shows a delta versus the prior period so you can see direction at a glance. Below that, 2 trend charts (GMV over time, Order volume trend) show how the headline numbers have moved over time. A breakdown (Orders by product) splits the metric by dimension so you can see what's driving the total. A detail table (Store health) rounds it out with the secondary metrics and their deltas. Every number is computed from the exact formulas shown in the metric table below. Composites are derived from their components, not pasted in, so the KPI tiles, breakdowns, and totals all reconcile to each other.

Illustrative data

Orders

8.5K▲ 1.7%

Data ▾

Period	Orders
Jan	6.1K
Feb	5.3K
Mar	5.4K
Apr	5.9K
May	5.9K
Jun	6.0K
Jul	5.1K
Aug	6.0K
Sep	6.3K
Oct	8.2K
Nov	8.3K
Dec	8.5K

GMV

$1.63M▲ 9.7%

Data ▾

Period	GMV
Jan	$766K
Feb	$828K
Mar	$912K
Apr	$787K
May	$864K
Jun	$791K
Jul	$933K
Aug	$886K
Sep	$1.00M
Oct	$1.23M
Nov	$1.49M
Dec	$1.63M

Avg order value

$192▲ 7.9%

Data ▾

Period	Avg Order Value
Jan	$126
Feb	$156
Mar	$167
Apr	$134
May	$147
Jun	$132
Jul	$184
Aug	$148
Sep	$160
Oct	$151
Nov	$178
Dec	$192

Net revenue

$1.09M▲ 9.6%

Data ▾

Period	Net Revenue
Jan	$607K
Feb	$604K
Mar	$687K
Apr	$727K
May	$844K
Jun	$779K
Jul	$855K
Aug	$1.02M
Sep	$912K
Oct	$1.04M
Nov	$993K
Dec	$1.09M

GMV over time

Data ▾

Period	GMV
Jan	$766K
Feb	$828K
Mar	$912K
Apr	$787K
May	$864K
Jun	$791K
Jul	$933K
Aug	$886K
Sep	$1.00M
Oct	$1.23M
Nov	$1.49M
Dec	$1.63M

Orders by product

Data ▾

Product	Orders
Platform	3.1K
Add-ons	1.9K
Services	3.5K

Order volume trend

Data ▾

Period	Orders
Jan	6.1K
Feb	5.3K
Mar	5.4K
Apr	5.9K
May	5.9K
Jun	6.0K
Jul	5.1K
Aug	6.0K
Sep	6.3K
Oct	8.2K
Nov	8.3K
Dec	8.5K

Store health

Avg Order Value$192▲ 7.9%

Refund Rate1.0%▼ 21.5%

Gross Margin79.7%▲ 1.7%

✦ Fi

Anything else I can do for you?

You

Why did AOV drop last month?Which product category is driving the refund rate up?Show me the orders behind the revenue dip in March.How has net revenue trended as the refund rate has changed quarter over quarter?Break GMV out by channel instead of product.Trace this month's net revenue back to the Stripe settlements.Break net revenue by product line so I can see the revenue mix and where margin is tightest.Add refund rate by channel and flag any channel above 5%.Show me GMV by channel so I can see which acquisition path drives the most volume.

Schedule a demo →Start this conversation with your data →

First version in a dayFree to start, flat pricingNo infrastructure to set up

§ Why the numbers tie out

Every metric traces back to your systems

This is the part a BI tool can’t fake. Each metric is defined once, in your warehouse, from a specific object in a specific source. Change the definition in one place and every tile, report, and answer moves with it. So the number on the screen is the number in the source.

Shopify

◆ OrderOrdersGMVAvg Order Value

Stripe

◆ Balance Transaction (Ledger)GMVAvg Order ValueNet RevenueRefund RateGross Margin

◆ InvoiceNet RevenueRefund RateGross Margin

◆ PaymentNet RevenueRefund RateGross Margin

Metric	What it measures	How it's calculated	Sources
Avg Order Value	—	GMV ÷ Orders	Shopify, Stripe
Net Revenue	Revenue you actually keep after refunds and credits, not what you originally billed.	Gross Revenue − Refunds & Credits	Stripe
Refund Rate	—	Refunds & Credits ÷ Gross Revenue	Stripe
Gross Margin	The share of net revenue left after the direct cost of delivering the product, the ceiling on how efficiently the business can grow.	(Net Revenue − COGS) ÷ Net Revenue	Stripe

§ Then do something about it

Have our agent watch for you

A e-commerce dashboard tells you what happened, and Fi tells you why. The last step is not having to remember to check. Point Definite at the one number you can’t afford to miss, and it watches that number for you off the same definitions as your dashboard. When it moves, you hear about it before the next review instead of during it. One metric, one action, always reversible.

See how autonomous agents work →

Agents for this stack

Shopify agent →

Stripe agent →

Google Ads agent →

Autonomous agent · watch churn

Watch

A metric you choose

net revenue churn

→

Judge

One condition

> 5% week-over-week

→

Act

One action

alert #revenue + open doc

◄──── then waits · cooldown 24h before it can act again ────

Scoped to a single metric and a single action. You arm it; you can disarm it anytime.

§ The data that powers it

Built from whatever you already run on

Connect the systems you already use. Any source of these types works, and you don’t move data into a warehouse, because Definite is the warehouse.

Billing & Subscriptions7

Advertising16

No warehouse to stand up or connect. See how the platform models your data →

§ Get started

Build your e-commerce dashboard

From signup to a working dashboard in one sitting. No data team required.

Sign up

Free to start. No credit card, no infrastructure to set up.

Create your account →

Connect your sources

Stripe, your CRM, accounting. Definite syncs and models them automatically.

Decide your metrics

Pick the numbers that matter or let Fi propose them from your data. Every metric gets one definition, governed in one place.

Ask Fi to build it

Describe what you need in plain language. Fi builds the dashboard, and you refine by asking follow-ups.

§ FAQ

Common questions

Usually because Shopify reports gross revenue including refunds in progress, Stripe reports settled payments on a different date, and Google Ads claims attributed revenue from its own model. The reconciliation map above shows which object each metric comes from, so there is one definition of revenue, reconciled between the storefront and the payment processor.

Your storefront (Shopify) for orders, products, and customers, your payment processor (Stripe) for settlements and refunds, and your ad platforms (Google Ads) for spend and attributed revenue. Definite syncs and models all three.

GMV is the total value of orders placed. Net revenue is what remains after refunds, credits, and discounts. When both come from governed definitions, the gap between them tells you exactly where the money went.

It is a live ECharts dashboard running on a deterministic synthetic dataset, labeled illustrative. AOV is computed as GMV over orders, refund rate from the formula in the metric table, not pasted in. Connect your storefront and Fi builds the same view from your data.

Type a prompt like the one above. Fi connects Shopify, Stripe, and your ad platforms, proposes the store metrics, and you refine by asking follow-ups. The first version reconciles to the storefront without a spreadsheet.