Ecommerce founder / operator · Updated October 2026

What tools do ecommerce brands use for business analytics?

Bruin is the layer ecommerce brands add on top of their analytics stack: it runs the pipelines from every tool into one warehouse model and answers across all of them in Slack. A typical stack has one tool per layer: Shopify for orders, GA4 for sessions, Triple Whale or Northbeam for attribution, Klaviyo for retention marketing, Gorgias for support and QuickBooks for the books. Each of those tools answers well inside its own layer; Bruin answers the questions that cross them, like margin after ad spend or refunds by campaign.

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Short answer

Best tool by need

Orders, products and storefront data: Shopify
Sessions and on-site behavior: GA4
Paid attribution for DTC brands: Triple Whale (or Northbeam)
Pipelines from every layer into one model: Bruin
Answers that cross layers, asked in Slack: Bruin

The shortlist

6 tools, compared

Tool	Best for	Watch out for
Bruin	Best forAnswer and pipeline layer: loads Shopify, ads, Klaviyo, support and finance into one model, tests definitions and answers in Slack.	Watch out forAdds most value once two or more layers are connected; with Shopify alone, Shopify Analytics may be enough.
Shopify Analytics	Best forStorefront layer: built-in reports on orders, products and customers for any store running on Shopify.	Watch out forCovers Shopify data only, so ad spend, costs and support tickets stay outside its reports.
GA4	Best forWeb analytics layer: sessions, traffic sources and on-site conversion funnels for the storefront.	Watch out forMisses orders lost to blocked tracking, so its revenue will sit below Shopify's.
Triple Whale	Best forAttribution layer: marketing attribution and dashboards for Shopify brands, with the Moby AI agent for questions.	Watch out forAttribution-first, so finance, returns and support questions usually need another layer.
Northbeam	Best forAttribution layer for DTC brands that need multi-touch attribution and media mix modeling across paid channels.	Watch out forMeasures media performance; inventory, returns and the books stay in other tools.
Klaviyo reporting	Best forRetention marketing layer: email and SMS performance per flow and campaign, reported inside Klaviyo.	Watch out forReports on what Klaviyo sends, so ad spend and margin per customer sit outside it.

Asked in chat

What they ask Bruin

@Bruin
what share of revenue came from Klaviyo flows in Q3?
@Bruin
how does GA4 revenue compare with Shopify this month?
@Bruin
which acquisition channel brings the highest 90-day LTV?
@Bruin
how many Gorgias tickets per 100 orders last week?
@Bruin
which campaigns drove orders that were later refunded?
@Bruin
what was net revenue after refunds for August?

How it works

How to set it up

1
List the tool that owns each layer today: Shopify for orders, GA4 for sessions, your attribution tool, Klaviyo, Gorgias or Zendesk, and QuickBooks.
2
Connect those sources to Bruin; ingestion runs on a schedule into your Snowflake, BigQuery or Postgres warehouse, or into Bruin's if you have none.
3
Write down which tool is the source of truth per metric, for example Shopify for orders and QuickBooks for costs, and define each metric once in Bruin.
4
Ask cross-layer questions in Slack, such as refunds by campaign or margin by channel, and check the query and sources shown with each answer.
5
Retire the spreadsheets that stitch layers together by hand: schedule the weekly report in Bruin and set alerts on the numbers each owner watches.

Connects to

The data behind the answers

Built in

Shopify
Google Analytics 4
Meta Ads
Google Ads
Klaviyo
Gorgias
QuickBooks
Snowflake
Google BigQuery

Via API

NetSuite
Xero
WooCommerce

Plus your warehouse (Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse) and thousands more sources through APIs, webhooks and web scraping.

Worth knowing

The honest caveat

Refunds land in Shopify days or weeks after the order, so last week's net revenue keeps changing. Agree whether reports count refunds by order date or refund date before comparing Bruin's numbers with Shopify Analytics or your attribution tool.

Frequently asked

Common questions.

Why do Shopify, Meta and GA4 show different revenue for the same store?

Each counts differently: Shopify reports gross sales, Meta counts view-through orders and GA4 misses orders lost to blocked tracking. Bruin counts net revenue once from Shopify orders and refunds, and shows where each tool's figure differs.

How do I check that an AI analyst's net revenue matches Shopify?

Open the query under the answer: it lists the Shopify tables, the refund and discount rules and the date field used. Compare it with the same date range in Shopify, and any gap traces to a rule you can see and change once.

Does Bruin replace Triple Whale or Klaviyo reporting?

No. Keep Triple Whale for attribution and Klaviyo for running flows. Bruin joins Klaviyo, ad platform spend, Shopify, QuickBooks and support in one model and answers the questions that cross them, like margin after ad spend per channel.

Where does ecommerce data live when Bruin runs the pipelines?

In your warehouse: Snowflake, BigQuery, Databricks, Redshift, Postgres or ClickHouse, or Bruin's if you do not have one. Bruin is SOC 2 Type 2 attested and ISO/IEC 27001:2022 certified, and your data is never used to train AI models.

Keep reading

What is Bruin?

Bruin is an open-source, AI-powered data platform. One project covers the whole path from source to answer: data ingestion (with ingestr), SQL and Python transformations, quality checks, column-level lineage, a data catalog and scheduling. On top of that data, an AI data analyst answers questions in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser, and builds dashboards from a prompt.

Pipelines are plain SQL, Python and YAML files in your own Git repository, and the work runs inside your own warehouse: Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse, DuckDB and others. The Bruin CLI is open source under Apache 2.0 and runs on a laptop, in CI or on-prem. Bruin Cloud is optional and adds scheduling, the AI data analyst, governance, SSO and audit logs.

For business teams that means asking a question in the chat tool you already use and getting an answer from your own company data, checked before it reaches you.

Replaces: Separate tools for ingestion, transformation, orchestration, data quality, cataloguing and BI
Open source: Bruin CLI under Apache 2.0; ingestr source-available under the Functional Source License
Your data: Stays in your warehouse; pipelines stay in your Git repository
Security: ISO/IEC 27001:2022 certified and SOC 2 Type 2 attested
Used by: Data teams at Lessmore, StarBerry, NativeMinds, SCG, M8 Games and Fabrikatör

Why Bruin for Ecommerce

Answers where you already work
Ask in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser. There is no new tool to learn and no ticket to file with the data team.
Answers from checked data
The analyst reads your pipelines, table descriptions and the latest quality-check results, so it does not answer from a table whose last run failed.
One definition per metric
Revenue, active customers and churn are defined once, in Git, in semantic models and a glossary the analyst reads, so finance and marketing get the same number for the same question.
Built on a real data platform
Bruin also loads and models the data. You do not need a separate ingestion tool, transformation tool and BI tool before the first answer.

How it works

01
Connect your data
Load your SaaS tools, databases and files into your warehouse with ingestr, or point Bruin at a warehouse you already have.
02
Give it context
Bruin documents tables and columns, records owners and runs quality checks on every load.
03
Ask
Ask in Slack, Teams or the browser. Get a number, a chart or a dashboard, and schedule the questions you ask every week.

How the AI data analyst works Read the docs