The 10 Best Ecommerce Analytics Tools for Multichannel Brands (2027)

The best ecommerce analytics tools for a multichannel brand are the ones that see every place your profit is made and spent: Shopify, Amazon, the ad platforms, email and product cost. In 2027 most brands use two kinds of tool together: the reports built into each platform, which every team already has, and a tool that joins the channels into one profit number by channel and campaign. This guide compares ten of them, grouped by what they do.

Querri publishes this guide, and Querri is one of the tools listed. We placed it in the category it belongs to, not at #1, and listed its limits the same way as everyone else's. No vendor paid to be included.

How we chose these tools

We looked at the tools that show up most for "ecommerce analytics tools", "Shopify profit analytics" and "Triple Whale alternatives" searches, and at what heads of ecommerce and growth at multichannel brands told us they use. For each one we read the vendor's public product, pricing and documentation pages, 2026 release notes, and published user reviews. We did not run a hands-on trial of every product, and we don't publish prices a vendor hasn't published. We judged each tool on eight things:

1. Channels covered: whether it brings in Shopify, Amazon (sales, fees and Amazon Ads), wholesale, Meta, Google and other ad platforms, and email.
2. Product cost: how cost of goods gets in, and whether it stays current.
3. How profit is counted: whether net sales and contribution margin follow a rule you can see and change.
4. Attribution approach: pixel and multi-touch, media mix modeling, platform-reported, or none.
5. Traceability: whether you can see the orders, spend and costs behind a number.
6. Who can answer a new question: anyone on the team, or an analyst or the vendor.
7. Alerts: what triggers a flag, and whether it compares a channel, campaign or SKU with its own history.
8. Pricing transparency: a public price, or quote-only.

The list is grouped by category, not ranked by score. The reports you already have come first, then the layer that joins every channel, then the specialist tools brands add: attribution, ecommerce data platforms and profit apps. A tool's number is its place in that order, not a grade.

Ecommerce analytics tools at a glance

What a multichannel ecommerce team needs to see

The questions are the same every week: what each channel and campaign really earned after product cost, fees and ad spend; which revenue number to report when every platform claims the sale; which SKUs are out on one channel while they sit in another; and where repeat customers come from. Every one of those answers is split across systems. Orders and refunds are in Shopify, Amazon sales and fees in Seller Central, spend in each ad platform, flows in Klaviyo and product cost in the books. Store and site analytics see one platform well. Attribution tools credit ad spend against store orders. Profit apps and data platforms bring costs in. The questions that need every channel at once, on one definition of profit, are what an analytics layer is for.

The 10 best ecommerce analytics tools for multichannel brands

Store and site analytics you already have

1. Shopify Analytics

Best for: every Shopify brand, because the core reports come with every plan (Shopify help).

What it measures: sales, orders, customers, products and marketing on Shopify orders, with custom reports in ShopifyQL. Profit reports use the cost per item you set on each product and variant (Shopify help). Amazon orders can be imported into Shopify through Marketplace Connect (Shopify help).

AI: ask Sidekick for a report in plain words and it drafts and runs the ShopifyQL for you (Shopify docs). There's also an average profit margin by market report, and the Winter '26 Edition added one analytics view across several stores on Plus (Shopify Editions).

Pricing model: included in Shopify's published plans (Shopify pricing).

Where it breaks: it reports on what lives in Shopify. Profit only counts items that had a cost recorded when they sold, and spend from Meta, Google and Amazon Ads, Amazon fees and wholesale margin sit in other systems.

Bottom line: the right place to start, and the source of truth for store orders; not a profit view across channels.

2. Google Analytics 4

Best for: traffic, funnels and conversion by channel on your site, for free.

What it measures: web and app events, ecommerce purchases and campaign performance. It imports campaign data from Meta, Pinterest, Reddit, Snap and TikTok (Google help), and exports raw events to BigQuery (Google help). Google also offers an official GA MCP server for AI assistants (Google developers).

How it decides: data-driven attribution, or last click on paid and organic or on Google paid channels (Google help).

Pricing model: the standard property is free. Analytics 360 is sold by contract with no public price.

Where it breaks: it sees sessions and purchases on your site, not Amazon, product cost or fees, so it can't tell you profit. Cost imports need care, too: Google tells you to delete any earlier Meta cost uploads before you switch on the direct Meta import, or the data overlaps (Google help).

Bottom line: the free baseline for site behavior; pair it with something that knows your costs.

Profit across every channel

3. Querri

Best for: multichannel brands whose profit crosses Shopify, Amazon, several ad platforms and the books, and who want one profit number by channel and campaign that ecommerce, finance and the founder all report.

What it measures: whatever you ask, in plain words, across your whole stack. Querri connects to Shopify, Amazon, Meta Ads, Google Ads, Amazon Ads, Klaviyo, Google Analytics, NetSuite and QuickBooks, among 125+ connectors and file uploads, and an agent sets up each connection with you, so you get going quickly. It joins orders, refunds, fees, ad spend and product cost by order and SKU, then shows each campaign's platform-reported revenue beside the net sales Shopify ties to it, and contribution after ads. You define net sales, product cost and contribution margin once in the Library, and every answer, dashboard and AI assistant uses them. Click any number to see the orders, spend and costs behind it. Monitors watch each channel's margin, each campaign's MER and each SKU's stock against its own history. The Channel profit and growth blueprint installs a channel P&L, campaign profit, MER against breakeven and email's share on sample data, so you have numbers right away and swap in your own when you connect (how it works).

How it decides: it doesn't add an attribution model of its own. It puts each platform's claim next to the orders in your store and the costs in your books, so you see where they disagree and report the number you chose.

Pricing model: free to start with no credit card, then usage-based (Querri pricing).

Where it breaks: Querri doesn't buy media, bid campaigns or push budgets back to the ad platforms. It has no tracking pixel and no media mix model; if you want modeled credit for view-throughs and upper-funnel spend, keep an attribution tool and let Querri read its numbers next to everything else. It works from a synced copy of your data on a schedule, not a live feed.

Bottom line: the layer that joins the store, Amazon, the ad platforms and product cost, with one definition of profit every team reports.

Attribution and marketing measurement

4. Triple Whale

Best for: Shopify DTC brands that spend heavily on paid social and want attribution, a profit summary and AI agents in one place.

What it measures: store sales, ad spend, and net profit after the costs you enter (COGS, shipping and custom expenses) (Triple Whale help). It connects Amazon Seller Central, Amazon Ads and Vendor Central (Triple Whale help).

How it decides: pixel-based multi-touch attribution, plus media mix modeling and incrementality testing.

AI: Moby 2 (May 2026) runs in Copilot mode, where you approve actions, or Autopilot, with Media Buyer, Creative Director and Conversion Optimizer agents that can act on Meta and Google campaigns (press release). A read-only MCP server is available (Triple Whale help). It bought Anteater, which tracks brand visibility in AI answers, in January 2026.

Pricing model: plans on its own pricing page; check it directly, because figures have changed through 2026 (Triple Whale pricing).

Where it breaks: its center of gravity is store orders and paid media. Triple Whale's own docs explain why Amazon product sales in Triple Whale can differ from Amazon's dashboard (Triple Whale docs), and wholesale margin sits outside it.

Bottom line: the most complete toolkit for a paid-social Shopify brand; check how it handles your Amazon and wholesale numbers before you make it the report of record.

5. Northbeam

Best for: brands with large ad budgets that want detailed multi-touch attribution and media mix modeling from one vendor.

What it measures: ad performance across platforms on store orders, with profit benchmarks once you add COGS (Northbeam).

How it decides: three attribution models (Clicks-Only, Clicks + Modeled Views, Clicks + Deterministic Views) (Northbeam docs), with MMM+ and incrementality as add-ons on higher plans.

AI: an MCP server for ChatGPT, Claude and other assistants (Northbeam docs).

Pricing model: set by annual ad spend, with published entry tiers and custom quotes above them (Northbeam pricing).

Where it breaks: Amazon comes in as total revenue and total Amazon Ads spend; its docs say "We're still working on Amazon attribution," and Vendor Central isn't supported yet (Northbeam docs). Onboarding takes real effort.

Bottom line: among the deepest attribution tools for big paid budgets; plan on another view for Amazon and true channel margin.

Ecommerce data platforms

6. Polar Analytics

Best for: multichannel brands that want a warehouse-backed data model, with Amazon covered in detail and AI on top.

What it measures: Shopify, ad platforms and Amazon Seller Central down to SKU, marketplace and fulfillment channel, including fees and refunds (Polar), plus Amazon Ads and DSP. It reports contribution margin in tiers from CM1 to CM4.

How it decides: its own first-party pixel for multi-touch attribution, and incrementality tests, run with a dedicated data scientist, that can measure the effect on Shopify, Amazon or retail sales (Polar pricing).

AI: Polar Operator, an AI Data Engineer and an AI Data Analyst, and an MCP server for Claude, ChatGPT, Gemini and others (Polar AI).

Pricing model: based on annual GMV, through a demo.

Where it breaks: the first Amazon backfill takes time (Polar says two to three weeks is common, with a separate connection per region), and one App Store reviewer noted that "before you can try - it requires a sales call" (Shopify App Store, 2024).

Bottom line: one of the strongest packaged data models for Shopify plus Amazon, for brands ready for a sales-led setup.

7. Daasity

Best for: larger omnichannel brands selling DTC, on Amazon and through retail and wholesale, that want a governed data model, often in their own warehouse.

What it measures: Shopify and Shopify B2B, Amazon Seller Central and Vendor Central, Amazon Ads, Walmart Marketplace, Meta, Google, Klaviyo, NetSuite and retail sources, landed in Snowflake, BigQuery or Redshift (Daasity integrations). Contribution margin is part of its data model.

AI: an MCP server for ChatGPT, Claude and other assistants (Daasity), and an AI Analyst add-on.

Pricing model: published flat monthly tiers, with Enterprise by quote (Daasity pricing).

Where it breaks: it is a data platform, so building your own dashboards takes analyst time, and implementation is a project. One merchant called theirs "possibly the worst I have ever experienced" (Shopify App Store, 2025).

Bottom line: the broadest omnichannel coverage here, best when you have the analyst capacity to use it.

Profit apps

8. Lifetimely by AMP

Best for: Shopify-first brands that want a daily P&L, LTV and cohort reporting, with Amazon as a second channel.

What it measures: a P&L from COGS, shipping, handling, refunds, fees, ad spend and operating costs, with connections to Meta, Google and TikTok ads, Klaviyo, QuickBooks Online, ShipStation and ShipBob (AMP integrations). Amazon sales come in through a paid add-on.

AI: a Profit Agent that drafts actions and runs them only with your approval, and an MCP server for Claude, ChatGPT and Copilot Studio (AMP).

Pricing model: by monthly order volume, with a free tier and an Amazon add-on (AMP pricing).

Where it breaks: profit is only as good as the costs you keep current; one reviewer wrote "Our COGS are not accurate anymore" (Shopify App Store, 2025). Amazon is an add-on rather than the core, and wholesale isn't covered.

Bottom line: a strong Shopify profit and LTV app; less suited when Amazon or wholesale is a big share of sales.

9. BeProfit

Best for: smaller Shopify, WooCommerce and Amazon sellers who want contribution profit and profit on ad spend in one low-cost view (BeProfit).

What it measures: orders, COGS, ad spend, shipping, fees (including Amazon's), returns and discounts. It connects Meta and Google Ads (Shopify App Store).

AI: a read-only MCP so you can ask Claude or ChatGPT about your numbers.

Pricing model: by monthly order volume, with a free tier and per-order overage (BeProfit pricing).

Where it breaks: ad spend has to sync completely for profit to be right. "A profit tool must always show total ad spend, even if attribution is imperfect," one reviewer wrote in January 2026 (Shopify App Store).

Bottom line: an affordable way to see profit across a store and Amazon; check ad spend totals against each platform.

10. Sellerboard

Best for: Amazon-first sellers who want profit after every Amazon fee, plus PPC and inventory tools.

What it measures: sales minus Amazon's fees, PPC, refunds and COGS, per product per day, across Amazon marketplaces, with PPC down to keyword and bid automation to a target ACOS (Sellerboard).

Pricing model: published flat tiers with a free trial. Its Shopify app is a separate product priced by order count (Shopify App Store).

Where it breaks: Amazon and Shopify are separate products, so a brand with both doesn't get one profit view by default. One Shopify-app reviewer wrote that ad spend "doesnt sync" (June 2026, same source).

Bottom line: the go-to for Amazon profit; add something else when the store and ads matter as much.

What about BI tools, other profit apps and general AI assistants?

Other names worth a look: Glew and Peel Insights (now part of Relay Commerce) for multichannel and retention reporting, and TrueProfit for Shopify net profit. Many brands also have a BI tool (Looker Studio, Power BI, Tableau) or a general AI assistant. BI tools can join anything once someone has piped each source into a warehouse and modeled it, so the limit is the analyst time to build and maintain that model. General AI assistants now have memory, dashboards, scheduled tasks and connectors, and they're very good at one-off analysis. What they don't keep on their own is one company-wide definition of net sales and margin: context lives with a person or a project, and an answer is worked out fresh each time. Querri connects to the warehouse your BI tool uses, and works inside the AI assistants your team already has (Querri MCP).

How to choose: match the tool to your stack

• Selling only on Shopify and buying on one or two platforms? Start with Shopify Analytics and GA4, and add a profit app such as Lifetimely or BeProfit when you want costs in the picture.
• If most of your sales are on Amazon, start with Sellerboard for fee-level profit, then join it to the store and the ad platforms as Shopify grows.
• Heavy paid social and a need for modeled credit points to Triple Whale or Northbeam. Treat their numbers as a diagnostic and report profit on the margin finance uses.
• When profit crosses Shopify, Amazon, several ad platforms and the books, an analytics layer such as Querri gives you one profit number by channel and campaign without a data build first.
• Retail, wholesale and an analyst team: an ecommerce data platform such as Daasity or Polar, or BI on your own warehouse.

Before you buy: questions to ask in the demo

1. How do Amazon sales, fees, settlements and Amazon Ads get in, and how far back?
2. Where does product cost come from, and who keeps it current?
3. How do you define net sales and contribution margin, and can we change the rule?
4. Can we see the orders, spend and costs behind any number on a dashboard?
5. When a question needs data the tool doesn't hold, such as wholesale or 3PL stock, what happens?
6. What triggers an alert, and does it compare a channel or SKU with its own history?
7. If we leave, what does an export of all our data look like?

Frequently asked questions

Next step

If the question on your desk this week is what each channel and campaign really earned, see how Querri answers it across Shopify, Amazon and your ad platforms: real profit by channel and campaign, not platform-reported revenue.

This comparison reflects publicly available information as of November 2026. Product features and pricing change often; check each vendor's site before you buy. Querri publishes this guide and is one of the tools listed.