Marketing Dashboard with BigQuery

Query real GA4 ecommerce data and build an interactive marketing dashboard.

Introduction

30 Second Summary

Every marketing team asks the same question after a campaign: which channels actually brought in revenue? Knowing whether to invest more in organic search or paid ads requires data, not gut feelings.

In this project, you will query real Google Merchandise Store ecommerce data in BigQuery and build an interactive dashboard in Looker Studio that shows exactly which marketing channels drive the most value.

What You'll Build

You'll have a live marketing dashboard where you filter by device type, compare channel revenue at a glance, and share a link with anyone to prove you can turn raw analytics data into actionable business recommendations.

By the end of this project, you'll have:

A BigQuery Sandbox project where you write SQL queries against real ecommerce data and instantly see which marketing channels generate the highest revenue per user.
An interactive Looker Studio dashboard with bar charts, scorecards, and filter controls that update every chart live when viewers click through the data.
A shareable link you can paste into job applications or your LinkedIn profile, showing hiring managers you can go from raw data to business recommendation.
Secret Mission: Add a time-series trend view showing month-over-month revenue by channel.

Are there any prerequisites?

You need a Google account and basic SQL knowledge (SELECT, WHERE, GROUP BY). No credit card or billing account is required.

Before We Start

Before we dive in, let's lock in what you're about to build and why it matters. This step is your moment to commit to the goal before any hands-on work begins.

Set Up BigQuery Sandbox and Explore the Dataset

Every marketing dashboard starts with data. Before you can calculate which channels drive revenue, you need a place to run SQL queries against real analytics data.

In this step, you will set up BigQuery Sandbox. This is Google's free data warehouse that lets you query terabytes of public data without a credit card or billing account.

In this step, get ready to:

Create a BigQuery Sandbox project with no billing required.
Navigate the Explorer pane to find the GA4 ecommerce dataset.
Run an exploratory SQL query to see what event data is available.

Open BigQuery Sandbox and create a project

BigQuery Sandbox gives you 1 TiB of free query processing per month. No credit card needed.

Open your browser and go to the BigQuery Console:
https://console.cloud.google.com/bigquery
Sign in with your Google account.

What if I see a welcome page?

If this is your first time using Google Cloud, you will see a welcome page asking you to select your country and agree to the Terms of Service. Select your country, check the Terms of Service box, and click Agree and continue.

Click Create project when prompted.
In the Project name field, enter Marketing Dashboard (or any name you like).
For Organization, select No organization if you are not part of one.
Click Create.

You will be redirected back to the BigQuery page. A sandbox notice confirms that BigQuery Sandbox is active on your project.

What is BigQuery Sandbox?

BigQuery Sandbox is a free tier that lets you query public datasets and store up to 10 GiB of data without attaching a billing account. The only limitation that matters for this project is that tables you create expire after 60 days. Since you are querying a public dataset (not creating your own tables), this does not affect you.

Not seeing the Create project option?

If you already have a Google Cloud project from a previous session, BigQuery may open directly into that project. That is fine. You can use your existing project for this tutorial. Look for your project name in the top-left of the console header to confirm you are in a project.

help me set up BigQuery Sandbox

Find the public GA4 ecommerce dataset

Google hosts a real ecommerce dataset from the Google Merchandise Store. It contains the same fields a marketing analyst works with daily: traffic sources, device categories, purchase events, and revenue.

In the left-hand Explorer pane, click Add data.
Select Public datasets.
In the Marketplace search field, type ga4 obfuscated and press Enter.
Click the Google Analytics Sample result.
Click View dataset. This opens the dataset in BigQuery and pins the bigquery-public-data project in your Explorer pane.

You should now see bigquery-public-data listed in the Explorer pane on the left side of your screen.

In the Explorer pane, expand bigquery-public-data by clicking the arrow next to it.
Scroll down (or use the search filter) to find ga4_obfuscated_sample_ecommerce.
Click the dataset name to expand it. You will see a list of tables named by date (like events_20210101).

What is this dataset?

This is real Google Analytics 4 data from the Google Merchandise Store. Each table represents one day of events. The fields include event_name, traffic_source.medium, device.category, and geo.country. These are the exact dimensions marketing analysts use to evaluate campaign performance.

Can't find the dataset in the Explorer pane?

Try typing ga4_obfuscated in the search box at the top of the Explorer pane to filter results. Make sure bigquery-public-data is visible and expanded. If it is not listed, go back to Add data and try the Public datasets route again.

help me find the GA4 ecommerce dataset in BigQuery

Run your first exploratory query

Before building a marketing analysis, you need to see what events this dataset tracks. A quick SELECT DISTINCT query reveals the event types available in a single day of data.

Click the + button (or Compose a new query) above the query editor to open a new query tab.
Paste the following SQL into the query editor:

SELECT DISTINCT event_name
FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20210101`
LIMIT 20

What does this query do?

This query looks at a single day of data (January 1, 2021) and returns up to 20 unique event names. It tells you what types of user interactions the dataset tracks, like page views, purchases, and session starts.

Before you run this, what event names do you expect to see in an ecommerce analytics dataset?

Click Run.

In the Query results section below the editor, you should see rows with event names like purchase, page_view, and session_start. These are the building blocks for your marketing analysis.

Query failing or no results?

Make sure you copied the entire query including the backticks around the table name. The backticks are required because the table name contains hyphens. Also confirm your project is selected in the top-left dropdown of the console.

help me debug my BigQuery query

Your BigQuery Sandbox is live and you have confirmed the GA4 dataset is accessible. Next up, you will write a query that turns these raw events into a marketing channel revenue breakdown.

Query Revenue by Marketing Channel

You've confirmed the GA4 ecommerce dataset is live and queryable in BigQuery. Now comes the first real analysis.

A marketing team's most pressing question is: which channels actually drive revenue? You need a per-user efficiency metric because channels with more traffic naturally produce bigger totals.

In this step, get ready to:

Write a SQL query that calculates total revenue by marketing channel.
Add a revenue-per-user metric to compare channel efficiency.
Identify which channel delivers the highest return per purchaser.

Write a channel revenue query

The traffic_source.medium field in the GA4 dataset records how each user arrived at the store. Values like organic, cpc, and referral map directly to marketing channels.

Clear the previous exploratory query from the BigQuery query editor.
Paste the following SQL query that filters for purchase events and aggregates revenue by channel:

SELECT
  traffic_source.medium AS channel,
  SUM(event_value_in_usd) AS total_revenue,
  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20210101`
WHERE
  event_name = 'purchase'
GROUP BY
  channel
ORDER BY
  total_revenue DESC

What does this query do?

WHERE event_name = 'purchase' filters down to only completed purchases, ignoring page views and other events.
traffic_source.medium extracts the marketing channel each purchaser came from (organic search, paid ads, referral links).
SUM(event_value_in_usd) totals all purchase revenue for each channel.
COUNT(DISTINCT user_pseudo_id) counts unique purchasers, so repeat purchases by the same user are not double-counted.

Click Run above the query editor.

You should see rows in the Query results pane below the editor, each showing a channel name, its total revenue, and the number of unique purchasers.

Seeing an error instead of results?

Check that the table name is wrapped in backticks (the ` character), not regular single quotes.
Make sure there are no extra spaces inside the backtick-wrapped table name.
Confirm you selected your Sandbox project in the project picker at the top of the console.

Help me debug my BigQuery channel revenue query

Add revenue per user to compare efficiency

Total revenue favours channels that simply have more visitors. A channel with 10,000 users and $5,000 in revenue looks bigger than one with 100 users and $1,000. But the second channel converts at $10 per user versus $0.50.

In the query editor, find the line that says:

  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers

Add a comma at the end of that line, then add this new line directly below it:

  SUM(event_value_in_usd) / COUNT(DISTINCT user_pseudo_id) AS revenue_per_user

Find the last line of the query:

ORDER BY
  total_revenue DESC

Replace total_revenue with revenue_per_user so the line reads:

ORDER BY
  revenue_per_user DESC

Why revenue per user?

Dividing total revenue by unique purchasers gives you a normalized metric. This lets you compare a niche referral channel against a high-traffic organic channel on equal footing. Marketing teams use this to decide where to increase spend.

Run the query and identify the top channel

Before you click Run, think about this: do you expect the channel with the highest total revenue to also have the highest revenue per user?

Click Run to execute your updated query.

Your results now show a revenue_per_user column alongside each channel. The rows are sorted by efficiency, not volume. You should see different values across channels like organic, referral, and cpc.

All revenue_per_user values showing NULL?

This happens if no rows match the purchase filter. Double-check that event_name = 'purchase' uses single quotes around the word purchase, not backticks.
Confirm the table name ends in events_20210101 (January 1, 2021). Other dates may have fewer or no purchase events.

Help me fix NULL values in my BigQuery revenue query

✔️ Awesome, I've got everything!

Great. Confirm you have saved your query mentally or in a text file. You will build on this query in the next step.

ⓧ I'd like to double check the full code

Here is the complete query you should have in the BigQuery editor:

SELECT
  traffic_source.medium AS channel,
  SUM(event_value_in_usd) AS total_revenue,
  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers,
  SUM(event_value_in_usd) / COUNT(DISTINCT user_pseudo_id) AS revenue_per_user
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20210101`
WHERE
  event_name = 'purchase'
GROUP BY
  channel
ORDER BY
  revenue_per_user DESC

You now know which channels drive the most efficient revenue. Next up, you will break this down further by device and country to see where and how customers convert differently.

Add Device and Country Breakdown

Your revenue-by-channel query revealed which marketing channels are most efficient. Now you need to dig deeper into WHERE and HOW those conversions happen.

A marketing team allocating budget needs to know if mobile users from specific countries convert differently than desktop users. Without device and geography data, your channel recommendations could miss critical differences that change spending decisions.

In this step, get ready to:

Add device and country dimensions to reveal conversion patterns.
Expand your query to a full year of data using wildcard tables.
Save the final SQL for your Looker Studio dashboard.

Add device and country to your channel query

Your current query groups purchases by channel only. Adding device.category and geo.country as extra dimensions reveals whether the same channel performs differently on mobile versus desktop, or in different markets.

In the BigQuery query editor, select all the existing SQL and replace it with this updated query:

-- Revenue breakdown by channel, device, and country
SELECT
  traffic_source.medium AS channel,
  device.category AS device_type,
  geo.country AS country,
  SUM(event_value_in_usd) AS total_revenue,
  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers,
  -- Calculate efficiency: revenue divided by unique buyers
  SUM(event_value_in_usd) / COUNT(DISTINCT user_pseudo_id) AS revenue_per_user
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20210101`
WHERE
  event_name = 'purchase'
GROUP BY
  channel, device_type, country
ORDER BY
  total_revenue DESC

What changed from the previous query?

device.category AS device_type pulls the device type (desktop, mobile, or tablet) from the nested GA4 event data.
geo.country AS country pulls the visitor's country.
Both new fields are added to the GROUP BY clause so the revenue calculation splits across every channel-device-country combination.

Click Run in the query editor toolbar.

You should see rows in the Query results pane with three grouping columns: channel, device_type, and country. The same channel now shows different revenue_per_user values depending on device and location.

Seeing zero rows or an error?

Check that device.category and geo.country are spelled exactly as shown. These are nested fields and BigQuery is case-sensitive.
Make sure you added both new fields to the GROUP BY line. Missing one causes an aggregation error.
If you see a "not a valid table" error, confirm the backticks around the full table name are present.

Help me debug my BigQuery device and country query.

Expand to a full year with wildcard tables

Right now you are querying a single day of data. One day of purchases is too small a sample to draw budget conclusions from.

BigQuery wildcard tables let you query many daily tables at once by replacing the date suffix with * and filtering with _TABLE_SUFFIX. This scans a full year of purchase events in a single query.

Replace the query in the editor with this updated version that uses wildcard tables:

-- Full year revenue by channel, device, and country
SELECT
  traffic_source.medium AS channel,
  device.category AS device_type,
  geo.country AS country,
  SUM(event_value_in_usd) AS total_revenue,
  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers,
  -- Calculate efficiency: revenue divided by unique buyers
  SUM(event_value_in_usd) / COUNT(DISTINCT user_pseudo_id) AS revenue_per_user
FROM
  -- Wildcard table scans all daily tables at once
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  -- Filter to the full year 2021
  _TABLE_SUFFIX BETWEEN '20210101' AND '20211231'
  AND event_name = 'purchase'
GROUP BY
  channel, device_type, country
ORDER BY
  total_revenue DESC

What do wildcard tables do?

The GA4 dataset stores each day as a separate table (events_20210101, events_20210102, and so on). Replacing the date with * tells BigQuery to scan all matching tables.

_TABLE_SUFFIX BETWEEN '20210101' AND '20211231' limits the scan to only tables within the 2021 calendar year. Without this filter, BigQuery would scan every table in the dataset.

Before you run this, predict: do you think desktop or mobile users will show a higher revenue_per_user?

Click Run to execute the full-year query.

You should see many more rows than before, broken down by channel, device, AND country. Compare the revenue_per_user values for desktop versus mobile. They should be meaningfully different, confirming that device type matters for budget allocation.

Query taking a long time or timing out?

Wildcard queries scan more data. This query may take 10-20 seconds. Wait for the green checkmark in the results pane.
If you see a permissions error, confirm you are querying bigquery-public-data (not your own project's dataset). Public datasets are accessible to all BigQuery users.
If results show null for some channels, that is expected. Some events have no traffic source recorded.

Help me troubleshoot my BigQuery wildcard table query.

Copy the full SQL query from the editor and save it somewhere accessible (a text file or your clipboard). You will paste it into Looker Studio in the next step.

Keep this tab open

Leave your BigQuery tab open with the query results visible. In the next step, you will use a button in the results pane to send this data directly to Looker Studio.

✔️ Awesome, I've got everything!

Great. Confirm you can see rows in your results with channel, device_type, and country columns, and that desktop and mobile show different revenue_per_user values.

ⓧ I'd like to double check the full query

Here is the complete query you should have in your BigQuery editor:

-- Full year revenue by channel, device, and country
SELECT
  traffic_source.medium AS channel,
  device.category AS device_type,
  geo.country AS country,
  SUM(event_value_in_usd) AS total_revenue,
  COUNT(DISTINCT user_pseudo_id) AS unique_purchasers,
  -- Calculate efficiency: revenue divided by unique buyers
  SUM(event_value_in_usd) / COUNT(DISTINCT user_pseudo_id) AS revenue_per_user
FROM
  -- Wildcard table scans all daily tables at once
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  -- Filter to the full year 2021
  _TABLE_SUFFIX BETWEEN '20210101' AND '20211231'
  AND event_name = 'purchase'
GROUP BY
  channel, device_type, country
ORDER BY
  total_revenue DESC

Your data now tells a complete story: which channels drive revenue, on which devices, and in which countries. Next up, you will turn these numbers into an interactive dashboard in Looker Studio that stakeholders can explore themselves.

Build the Looker Studio Dashboard

Numbers sitting in a SQL results table do not get budget decisions made. Stakeholders need interactive visuals they can explore themselves.

Connecting BigQuery to Looker Studio is the last mile that makes your analysis actionable. In this step, you will turn your query results into a dashboard with charts, scorecards, and an interactive filter.

In this step, get ready to:

Open your BigQuery query results directly in Looker Studio.
Add a bar chart, scorecard, and table to your dashboard.
Add an interactive drop-down filter that updates every chart on the page.

Open your query results in Looker Studio

Looker Studio can read data directly from a BigQuery query. Instead of exporting a CSV and importing it separately, you will send your results straight to a new report.

Return to the BigQuery console where your full-year channel/device/country query is still loaded.
Run the query again if the results have cleared (click Run).
In the Query results section below the editor, click Open in and select Looker Studio.

What happens when you click this?

BigQuery sends your query and its schema to Looker Studio as a live data source. Looker Studio opens in a new browser tab with a blank report canvas connected to your query.

If Looker Studio asks you to authorize access to your BigQuery data, click Allow.

You should see a new Looker Studio tab open with an empty report canvas (or a default table chart) and your BigQuery data source listed on the right.

Don't see the Looker Studio tab?

Check that your browser is not blocking pop-ups for console.cloud.google.com. Allow pop-ups and try clicking Open in > Looker Studio again.

help me open my BigQuery results in Looker Studio

Add a bar chart showing revenue per user by channel

The bar chart is the centerpiece of this dashboard. It answers the key question: which marketing channel drives the most revenue per user?

If Looker Studio added a default table chart to the canvas, click it and press Delete on your keyboard to remove it.
In the toolbar at the top, click Add a chart.
Select Bar chart (the standard vertical bar option, also called Column chart).
Click the canvas to place the chart.

In the Properties panel on the right, select the SETUP tab.
Set the Dimension to medium (this is traffic_source.medium from your query).
Set the Metric to revenue_per_user.
Remove any other default metrics that Looker Studio may have added (click the X next to them).

Your bar chart should now display bars for each channel (organic, cpc, referral) with heights reflecting their revenue per user value.

Why revenue per user instead of total revenue?

Total revenue favours high-traffic channels regardless of efficiency. Revenue per user normalizes by audience size, so a small but highly-converting channel stands out.

This is the metric marketing managers use to decide where to increase spend.

Chart showing No data?

Make sure the Dimension field uses the exact column name from your query results. Click the dimension chip and look for medium in the list. If your query aliased the column differently, use the alias that appears in your results.

help me fix my Looker Studio bar chart that shows no data

Add a scorecard and a table

A scorecard gives a single headline number at a glance. A table lets viewers scan the detailed breakdown without switching to BigQuery.

In the toolbar, click Add a chart and select Scorecard.
Click the canvas to place it above your bar chart.
In the SETUP tab of the Properties panel, set the Metric to total_revenue.

You should see a large number appear on the scorecard representing the total revenue across all channels and devices.

Click Add a chart again and select Table.
Click the canvas to place the table below or beside your bar chart.
In the SETUP tab, set the Dimension to country.
Add a second dimension by clicking + Add a dimension and selecting category (this is device.category from your query).
Set the Metric to total_revenue.
Under Sort, sort by total_revenue descending so the highest-revenue combinations appear first.
Set Rows per page to 10 to show only the top 10.

Your table now shows the top 10 country/device combinations ranked by revenue.

What does this table tell a marketing team?

It highlights where both geography and device type align with high revenue. A marketing manager might see that desktop users in the United States generate far more revenue than mobile users in the same country.

That insight drives decisions about mobile UX investment and country-specific ad targeting.

Add a drop-down filter for device type

An interactive filter lets anyone viewing the dashboard slice the data themselves without knowing SQL. You will add a drop-down that filters every chart by device category (desktop, mobile, tablet).

In the toolbar, click Add a control.
Select Drop-down list from the menu.
Click the canvas to place the control at the top of your dashboard (above your charts).
In the SETUP tab of the Properties panel, set the Control field to category (the device category dimension).

How does the filter affect all charts?

By default, a Looker Studio drop-down control filters every chart on the same page that shares the same data source. Since your bar chart, scorecard, and table all pull from the same BigQuery query, selecting a device type in the drop-down updates all of them at once.

Before you test, do you think selecting mobile will increase or decrease the total revenue scorecard compared to the unfiltered view?

Click the View button in the top-right of Looker Studio to switch from edit mode to viewer mode.
Click the device drop-down filter you just added.
Select mobile.

All charts on the dashboard update to show only mobile data. The bar chart values change from the unfiltered view, and the scorecard shows a lower total revenue figure (mobile typically converts at a lower rate than desktop for this dataset).

Filter not updating the charts?

Make sure you are in View mode (not Edit mode). Controls do not filter interactively while you are editing the report.

Also confirm the Control field dimension in the filter matches the dimension name used by your charts. If the drop-down is set to a different field than what the charts use, filtering will not apply.

help me fix my Looker Studio drop-down filter

Your dashboard is now interactive and telling a story. Next up, you will add business recommendations and share the dashboard with a public link ready for job applications.

Add Insights and Share Your Dashboard

Your dashboard already shows which channels perform best and how devices compare. But numbers alone do not get budget decisions made.

Marketing analysts get hired because they tell stakeholders what to do with the data. In this step, you will add business recommendations to your dashboard and share it as a live link anyone can view.

In this step, get ready to:

Add a text box with bullet-point recommendations referencing your revenue data.
Rename the report with your name for portfolio use.
Share the dashboard with a public link and verify it works.

Add business recommendations to the dashboard

A text box with short recommendations tells viewers exactly what action to take based on the data. This is what separates a school project from a portfolio piece.

In your Looker Studio report, make sure you are in edit mode (click Edit in the top right if you see a View layout).
Click the Text icon in the toolbar at the top.
Click and drag on the canvas below your charts to draw a text box.
Click inside the text box and type 2-3 bullet-point recommendations based on what your query results showed.

What should my recommendations say?

Look at your bar chart and query results. Reference the actual channel names and their revenue_per_user values. For example:

"Referral traffic generates the highest revenue per user. Consider increasing referral program investment."
"Mobile users convert at a lower rate than desktop. Investigate mobile checkout friction."
"Organic search drives the most total revenue. Maintain SEO investment to protect this channel."

Use whatever your data actually shows. The specific numbers make your recommendations credible.

You should now see a text box on your canvas with your typed recommendations visible below the charts.

Text box not appearing?

Make sure you are in edit mode. If you only see View in the top right, you are already editing. If you see Edit, click it first.
The Text icon looks like a capital T in the toolbar. It is separate from the Add a chart button.

Still stuck? Help me find the Text tool in Looker Studio

Rename and share your dashboard

A professional report name and a shareable link turn this into something you can paste into job applications or your LinkedIn profile.

Click the report title in the top left corner (it likely says "Untitled Report" or a default name).
Replace it with Marketing Channel Performance Dashboard - followed by your name.

Your title should now display in the top left corner of the report.

Click Share in the upper right of the report.
In the dialog that appears, find Link settings.
Use the drop-down menu to select Anyone with the link can view.
Click Copy link to copy the shareable URL to your clipboard.

Why anyone with the link?

This setting means anyone who has the URL can view your dashboard without needing a Google account to edit it. Hiring managers and LinkedIn connections can open it instantly. They can interact with filters but cannot change your report.

Verify the shared link works

Before you paste this link anywhere public, you need to confirm it actually works for someone who is not signed into your account.

Open an incognito or private browser window (Cmd+Shift+N on macOS, Ctrl+Shift+N on Windows in Chrome).
Paste the URL you just copied into the address bar and press Enter.

Before the page loads, what do you expect to see? Will it show your charts, or will it ask for a sign-in?

The dashboard loads fully with all your charts, the device filter, and your recommendation text visible. No sign-in is required to view it.

Click the device filter drop-down and select a different device to confirm interactivity works for viewers too.

Seeing a permissions error in incognito?

Go back to your main browser window and confirm the Link settings drop-down is set to Anyone with the link can view (not "Off - Only specific people can access").
If you changed the setting, click Copy link again and paste the fresh URL into your incognito window.

Still stuck? Help me fix my Looker Studio sharing permissions

Secret mission

Add Month-over-Month Revenue Trends

Your dashboard shows a static snapshot of channel performance. Marketing teams need to see how revenue trends change over time to spot seasonal patterns and measure campaign impact. In this secret mission, you will write a more complex SQL query using DATE_TRUNC and build a time-series chart that shows monthly revenue by channel on a second dashboard page.

Clean Up Your Resources

This project uses BigQuery Sandbox and Looker Studio, both of which are completely free. There are no ongoing costs regardless of whether you keep your resources or delete them.

Decide whether to keep your resources running, pause them to come back later, or delete them entirely.

Resources you used:

BigQuery Sandbox project (with saved queries).
Looker Studio report (two-page dashboard with public sharing link).

Keep everything running

No action needed. Choose this if you want to keep your dashboard live and shareable.

Your Looker Studio report stays accessible at the shared link you created.
Your BigQuery Sandbox project remains available for future queries against the public dataset.
Neither service incurs any cost.

Pause - I'll come back to this later

Revoke public access so your dashboard is private while you're away. You can re-share it anytime.

Open your Looker Studio report.
Click Share in the top right corner.
Under General access, change Anyone with the link back to Restricted.
Click Done.
Your report and BigQuery project remain intact for when you return.

Delete - I don't want to use this again

Remove all project resources completely. This cannot be undone after 30 days.

Delete your Looker Studio report:

Go to lookerstudio.google.com.
Locate your report in the list.
Hover over the report name, then click the three-dot menu (More options).
Click Remove.
On the left sidebar, click Trash.
Hover over the report, click the three-dot menu, then click Delete forever.

Delete your BigQuery Sandbox project:

Go to the Google Cloud console Manage resources page at console.cloud.google.com/cloud-resource-manager.
Select the project you created for this dashboard.
Click Delete.
Enter the project ID in the confirmation dialog, then click Shut down.

What happens after shutdown?

Google Cloud soft-deletes the project for 30 days before permanently removing it. During that window you can restore it from the same Manage resources page if you change your mind.

Nice Work!

You just built a shareable marketing campaign performance dashboard powered by real Google Analytics ecommerce data. From raw event rows to interactive charts and business recommendations, you completed the exact workflow marketing analyst interviews test for.

You've learned how to:

Query a real GA4 ecommerce dataset in BigQuery Sandbox using wildcard tables, GROUP BY, and calculated metrics like revenue per user to compare marketing channel efficiency.
Build an interactive Looker Studio dashboard with bar charts, scorecards, filter controls, and business recommendations connected live to BigQuery.
Share a public dashboard link ready for job applications and your LinkedIn profile, demonstrating the data to insight to action workflow hiring managers look for.
Secret Mission: Added a time-series trend view showing month-over-month channel revenue performance on a second dashboard page.

Ready to quiz yourself?