Everything EOS does today.

Leadership questions arrive faster than reports can be built. Revenue for today, this week and this month usually live in different exports, and by the time the numbers are assembled they are already stale.

A single overview with today, yesterday, week-to-date, month-to-date and year-to-date side by side — each with order counts, average order value and a trend line — plus a written summary of what changed in the selected period and why.

You open one page and know where the business stands, without asking anyone to prepare anything.

Store analytics answer "what were my sales" but rarely "which products actually drive profit", "which cities buy most", or "what hours convert".

Revenue and order trends over any date range, product performance with units, revenue, cost, profit and margin per product, revenue by city, by channel, by hour of day, and a live recent-orders feed.

Merchandising and promotion decisions get made on evidence instead of instinct.

Ad platforms grade their own homework. Meta reports its conversions, Google reports its own, and neither knows what your store actually banked — so spend decisions are made on partial evidence.

Synced campaign data from both platforms with spend, ROAS, CTR, CPC, CPM and conversions; best-performing campaigns ranked by return; Google keywords, search terms, landing pages and budget utilisation; Meta age, gender and region breakdowns; plus your own UTM-attributed orders shown alongside each campaign. A provider filter switches between Meta, Google and combined.

You can see platform-reported performance and your own store-side attribution together, and shift budget with both in view.

Customer lists are easy to export and hard to interpret. Repeat behaviour and customer value usually require a spreadsheet exercise nobody has time for.

New versus returning customers, retention rate, best customers by lifetime value, newest customers, and customer geography — all computed from synced store data across any date range.

Retention and loyalty work gets aimed at the customers who actually move the numbers.

Stock-outs are discovered after the fact, and slow-moving stock quietly ties up capital nobody is measuring.

Product and variant counts, low-stock and out-of-stock alerts with SKUs and quantities, stock value, vendor breakdowns, and overstock detection for items with no recent sales.

Restocking becomes proactive, and tied-up capital becomes visible.

Order status is scattered between the store admin and whatever spreadsheet operations keeps.

Order flow and fulfilment status with searchable, sortable, exportable tables, plus recent activity across the workspace.

Operations works from the same data leadership sees, so there is nothing to reconcile.

Delivery outcomes vary enormously by city and by courier, but that pattern is invisible without joining shipment data to orders.

City-level delivery and revenue analysis built from your synced order data, including where paid traffic actually converts.

Shipping and cash-on-delivery decisions get made with geography in view.

Revenue is easy. Profit is not — it needs product costs, refunds, discounts, shipping, taxes and advertising spend brought together consistently.

Gross sales, discounts, refunds, net sales, shipping, taxes, COGS from your Shopify unit costs, gross profit, margin, marketing expense per ad platform, and profit after marketing. Every profit figure states how much of your catalogue actually has cost data behind it.

You get a P&L you can trust precisely because it tells you what it does not know.

Dashboards show what happened. Deciding what to do about it is still manual — and "AI" features that generate confident prose from thin data make that worse.

A rule-based analysis engine that produces alerts, opportunities, risks, benchmarks, forecasts, goals, a health score and a daily digest. Every statement is computed from your synced data, states its method, and is suppressed when there is not enough volume to support it.

You get prioritised, explainable findings — not generated text.

Integrations break quietly. A token expires, a sync stops, and nobody notices until the numbers look wrong.

An integration centre showing every connection with its real state — connected, expired, needs reconnection or error — with account selection, one-click reconnect, and automatic token refresh where the platform supports it.

A broken connection announces itself instead of silently corrupting your reporting.

Keeping data current by hand is the single largest recurring cost of any reporting setup.

A full historical import that runs once and resumes if interrupted, incremental syncs on a schedule, a job queue with retries and backoff, and a Sync Center showing live progress, speed, estimated completion, history, errors and a record-count verification against the source platform.

Data stays current on its own, and you can prove it is complete.

Board and client updates get rebuilt from scratch every period, usually by copying dashboard screenshots into slides.

A presentation-grade executive report — cover, KPI summary with trend chart, marketing, products and geography, P&L and intelligence highlights — that saves as a PDF deck, plus a CSV export of the same underlying data. Tables throughout the app export to CSV individually.

The reporting pack is one click, and it always matches what the dashboard says.