# Ecommerce Analytics: Profit After Ads, Stock, Repeat Buyers > Ask store, marketplace, retail and ad data about profit after ads, MER, stock cover and repeat buyers, then get profit by channel in your inbox every Monday. HTML version: https://supaboard.ai/industry/ecommerce **What you made yesterday, after ads, shipping and fees.** Connect the warehouse where your store, marketplace, retail and ad data meet, then ask in plain English: profit by channel, MER, stock cover, repeat buyers and returns. For multi-channel consumer brands selling through their own store, marketplaces and retail, and their founders, finance and growth teams. ## Every question, by who asks it ### Profit and spend - Profit yesterday after ads, shipping and fees - MER this week against last week - Units, revenue and margin by channel this month: own store, marketplaces and retail ### Customers - What share of last quarter's first-time buyers have ordered again? - Which first product leads to the highest 90-day LTV? - Did the SPRING20 code bring in new customers, or discount existing ones? ### Stock and returns - SKUs that stock out within 30 days at the current sell rate - Return rate by product and size, last 90 days - Sell-through by retail account last month, against the units we shipped them ## Keep it running - **The Monday profit pack** (Monday 08:00). Last week's net sales, ads, shipping, fees and profit by channel, with MER against the week before, summarised and in the founders' inbox. Automation: Timed Start → Query Your Data → Summarize Insights → Send Email. - **Stock-cover watch** (Every 5 minutes). A SQL check finds any SKU under 30 days of cover with nothing on order, and emails the buyer while there's still time to reorder. Automation: Alerts → Send Email. - **The channel datasite** (Month end). Units, revenue and margin for your own store, marketplaces and retail, on a private datasite the founders and investors sign in to with Google or Microsoft. ## The numbers it keeps watch on | Measure | Definition | Source | In Supaboard | | --- | --- | --- | --- | | Profit after ads | Net sales − product cost − shipping − payment and marketplace fees − ad spend | Common practice | Monday profit pack | | MER | Net sales across every channel ÷ total ad spend, for the same period | Common practice | Monday profit pack | | Channel margin | Net sales − product cost − that channel's fees, for own store, marketplaces and retail | Common practice | Channel datasite | | Days of stock cover | Units on hand ÷ average daily units sold over the last 14 days, across every channel | Common practice | Alert at 30 days | | Repeat purchase rate | First-time buyers in a period who order again ÷ first-time buyers in that period | Common practice | Monthly question | | 90-day LTV | Net sales per customer in the 90 days from their first order, by first product | Common practice | Growth dashboard | | Return rate | Units returned ÷ units sold, by product and size | Common practice | Weekly report | | Code new-customer share | Orders with a discount code from first-time buyers ÷ all orders with that code | Common practice | Ask it after each promotion | ## Where your data lives - Redshift: connects live - [BigQuery](https://supaboard.ai/integrations/bigquery): connects live - [Snowflake](https://supaboard.ai/integrations/snowflake): connects live Or attach an export to a conversation, as CSV or Excel: Retail sell-through reports; Store and ad-platform exports. The tables these questions read: Orders and order lines by channel, with discounts and refunds; Products and SKUs, with unit cost; Inventory by location, and open purchase orders; Ad spend by platform, campaign and day; Returns, with reason and size; Retail sell-through by account and week. ## Teach it your definitions once Rules are read with every question, so a term means the same thing in Chat, in Slack and in the Monday report. - Rule: "Profit means net sales minus product cost, shipping, payment and marketplace fees, and ad spend." - Rule: "The current sell rate is average daily units over the last 14 days, across every channel." - Rule: "A first-time buyer is a customer with no earlier order in the online store." [How rules work](https://supaboard.ai/product/knowledge-and-rules) ## Already running on it A multi-channel consumer brand runs Supaboard on its Redshift warehouse, where its online store, marketplace, retail and media-spend data meet: orders, inventory, shipping and campaigns. (A Supaboard customer, described without its name.) ## Security - **Read-only by construction.** Database and warehouse queries are read-only. We give you the SQL for a read-only user too. - **Credentials stay in the workspace.** Scoped to the one workspace that connected them, never logged and never sent back to a browser. - **SOC 2 Type II.** An organisation-wide audit of the platform, covering every plan. ## FAQ ### Which store and ad platforms does it work with? Any whose data reaches your warehouse. Redshift, BigQuery and Snowflake connect live, so store orders, marketplace sales and ad spend that land there are queried in place, read-only. There's no direct store or ad-platform connector: for a one-off question, export from the platform as CSV or Excel and attach it to a conversation. ### How soon can we ask about our store data? As soon as your warehouse is connected and its tables are indexed. It's queried in place, so answers are as fresh as the warehouse. ### Can we try it before connecting anything? Yes. Onboarding offers a sample online-store database, so you can ask about its orders, products and returns first. ### How does it learn what we count as profit? Write it once as a rule, such as "profit is net sales minus product cost, shipping, fees and ad spend". The agent reads its rules with every question, so profit means the same thing in Chat, in Slack and in the Monday report. ### Can we get the numbers every Monday? Yes. An automation runs your questions at 08:00 on Monday in your timezone, summarises what changed and emails it. Dashboard reports can also go out as PDF, PowerPoint or Excel. ## More in Software & Commerce - [SaaS & Cloud Software](https://supaboard.ai/industry/saas): MRR movements, churn, expansion and activation, from billing and product data. - [Subscription Boxes](https://supaboard.ai/industry/subscription-boxes): Active subscribers, failed payments, gifting and churn by box, from billing data. - [Ed-Tech](https://supaboard.ai/industry/edtech): Enrolments, course completion, learner activity and renewals, from your platform's data. Supaboard is [the AI data analyst connected to your business data](https://supaboard.ai/). For the background on this industry, read [Ecommerce analytics: metrics that change decisions](https://supaboard.ai/blog/ecommerce-analytics). 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