Build a Shopify Executive Dashboard in Data Studio (Looker Studio)

A Shopify store is a complicated machine, and the data underneath it is too. But a Shopify executive dashboard your leadership team can read in about ten seconds is something you can build in one sitting, if you do the steps in the right order. We build these for retail brands every week, and in the video below we build one from a blank page in Google Data Studio (the tool spent a few years named Looker Studio, and as of April 2026 it is Data Studio again). This post follows the video section by section, with timestamps, so you can jump straight to the step you need.

How to Build a Shopify Executive Dashboard, Step by Step

Seven sections, in the order we actually build them for clients. Not the order that looks good in a tutorial. The order that works.

Section 1: Open Google Data Studio and Create the Report (0:49)

Data Studio lives at datastudio.google.com, it is free, and if you have a Google account you already have access. From the home screen, click Create to start your first report. You will see options to pre-connect a data source here; we skip ahead to Reports in the video and handle sources in the next step, because the connection deserves its own section. It is the whole ballgame.

Section 2: Connect Your Data the Honest Way (2:44)

Here is what other tutorials will not tell you: there is no native Shopify connector in Data Studio. Google has not built one. So getting Shopify data into your report takes one of three source types:

1. Google connectors (Google Analytics 4, Google Ads) that come standard with the tool. If GA4 is running on your storefront, it already carries your sessions, conversions, and channel-attributed revenue.
2. Raw data, like Google Sheets or a CSV.
3. A third-party connector, like Supermetrics or funnel.io, which is what we use in the video to pull true Shopify-side fields.

Why wire up both GA4 and a Shopify-side source? Because they will not match, and that is normal (we break down why Shopify and Google data differ at 4:37). GA4 is our pick for channel-attributed revenue (its data-driven attribution model is the fairest way we know to credit marketing efforts, and we covered why in our piece on attribution models). But Shopify reports revenue its own way, with its own windows for returns, exchanges, and cancellations. So we wire GA4 for channel truth, the connector for order truth, and the report shows its work on both. There are also fields neither source holds cleanly, like product image URLs, which is exactly why the multi-source setup pays off later in Section 6.

Section 3: Set Up the Report (3:24)

Fast housekeeping that decides how the report gets consumed. Name the report first. Then pick a layout, and this choice depends on your executive team, not your taste. If they print to legal pages, format for legal. If the report lives in a weekly slide deck, build to a slide format so a screen grab drops straight into the presentation. If they open a link ad hoc, choose responsive layout so it fits whatever device they open it on. We use responsive in the video, because it also sets up the self-serve features we add in Section 7. Add your data sources through the resource panel (both the GA4 and funnel.io sources), and skip pages entirely. This is a ten-second read for a leadership team, not a workbook.

Section 4: Build the Top-Line Scorecards With a Real Comparison (5:59)

The top row is the few numbers a decision hangs on: net sales, gross sales, returns, and gross profit margin if you keep item costs in Shopify. Each scorecard gets a comparison date range (8:01) set to the previous period, because a flat number tells an executive nothing. Up or down against last period is the actual signal. (All figures in the video are demo data, and we say so on camera.) If you track sales goals in a sheet, you can join those in here too, and the scorecards compare performance against target instead of just against last period.

Section 5: Build the Channel Report (9:30)

Every ad platform grades its own homework. Facebook claims the sale, Google claims the same sale. So the channel panel puts traffic source, paid versus organic, gross sales, cost, and a calculated ROAS field side by side against one reconciled source. Add comparison deltas and you can see at a glance when spend is down but return on ad spend is up, which reads very differently than “sales dropped.”

Filter it to the comparison your team actually argues about (13:11), in the video Google versus Facebook, and keep the trail back to the raw source data intact. When finance asks you to show your work, remove the filter and the table ladders back up to the Shopify-reported totals. This is the panel that ends the “whose number is right” meeting, and it is the same reason we push measurement beyond any single platform in our multi-channel SEO work: no one system sees the whole picture.

Section 6: Build Product & Funnel Panels (15:06)

Most product reports are a wall of SKU codes, so the merchandiser tunes them out. Data Studio can render your product image URL as an actual image in the table (16:20). Top ten products by net sales, with returns as a second sort, category and subcategory alongside, and the photo right in the row. That one change is usually what gets the retail side of the business to open the report on their own.

Then the funnel (18:28). Across the industry, roughly seven in ten carts are abandoned (Baymard Institute keeps the running research), and a single conversion-rate number tells you that you have a problem, not where it is. So the funnel panel tracks add to cart, begin checkout, payment info, and purchases, with calculated drop-off percentages between each step (19:37). We build it as a table in the video, because the built-in funnel chart styles do not read as cleanly at a glance.

One honest caveat from building these on real stores: Shopify only fires the payment-info event for net-new payment entry, so Shop Pay and Apple Pay customers skip it, and that step can read lower than transactions (we hit this exact anomaly on screen at 22:38). That is not broken tracking, it is how Shopify reports. Depending on your checkout mix, a three-step funnel (add to cart, begin checkout, purchase) may tell the truer story.

Section 7: Make the Dashboard Self-Serve (23:39)

The last step is what turns a chart into a reporting layer: controls. A date-range control and a category drop-down mean the channel lead, the merchandiser, and the CMO can each answer their own question without booking a meeting with you. A summary row on the filtered product table shows the revenue for whatever category they pick. Publish to view mode and share the link (25:30), public unlisted, named individuals, or groups in your workspace, and every person who opens it sees the same numbers from the same source, with a trail back to the raw data.

That is the paradigm shift. The conversation stops being “where did these numbers come from” and starts being “here is how we act on them.”

The Part a Dashboard Cannot Check

Everything above is repeatable. You can rebuild every panel on your own store this week, and it will already beat the spreadsheet you have been updating by hand.

But the report only tells the truth if the Shopify data feeding it is correct, and on most stores it is not, and nothing on screen warns you. Events fire wrong, match rates drop in Meta or Google Ads, the product feed is malformed. You cannot eyeball that from a chart.

That is the audit we do. We check the stack, work with your dev team to fix what is off, and scope the framework so your reporting holds up to the people you answer to.

FAQs

Sources and Further Reading

• Watch the full build on YouTube: Build a Shopify Executive Dashboard in Data Studio
• Google Cloud: Looker Studio is Data Studio
• Baymard Institute: Cart Abandonment Rate Statistics
• GA4 Attribution Models (Google documentation)
• funnel.io
• zamartz: Digital Marketing Channel Interaction Attribution Models