Databox vs Looker Studio vs Supermetrics: We Tried All Three for GA4 Reporting
Over the past 18 months, we tried three different tools for automating our GA4 reporting. Not demos. Not free trials. Actual paid subscriptions with real data, real users, and real deadlines depending on the output. Databox for four months, Looker Studio for six months (the free version, then Looker Studio Pro), and Supermetrics for three months. We eventually landed on a fourth option that none of us had considered at the start.
This is not a feature comparison pulled from pricing pages. This is what happened when our marketing team of seven people tried to answer a simple question: how do we get GA4 data in front of the people who need it, every week, without someone spending an afternoon building the report manually?
Looker Studio: Free and Exactly What You Pay For
We started with Looker Studio because it was free and it was Google. The GA4 connector is native, so setup was fast. Kenji had a working dashboard in about two hours. It showed sessions, users, pageviews, traffic sources, and device breakdown. The charts looked professional. Everyone was excited for about a week.
The problems started when we tried to make the dashboard useful for weekly reporting. Looker Studio is a visualization tool, not a reporting tool. The distinction matters. A visualization tool lets you build interactive charts that someone can explore. A reporting tool delivers specific information to specific people at specific times. Looker Studio does the first thing well. It does the second thing poorly.
Scheduled email delivery exists but it sends a PDF snapshot of the entire dashboard. Our dashboard had 12 widgets. The PDF was 4 pages of tiny charts that nobody read on their phone. Priya set up a weekly send and after three weeks, Diana asked her to stop because "the PDF is useless and it clutters my inbox."
The bigger issue was data blending. We needed to combine GA4 data with Google Ads data in the same visualization. Looker Studio technically supports this through blended data sources, but the joins are unreliable with more than two sources and the calculated fields break in ways that are hard to debug. Kenji spent an entire afternoon trying to create a ROAS metric that divided Google Ads cost by GA4-tracked revenue. He got it working for one campaign. It broke for the others because of dimension mismatches.
Elena asked for a simple thing: "Show me this week's numbers compared to the same week last year." In Looker Studio, date comparison is a filter, not a built-in feature. The user has to manually adjust the comparison date range every time they view the dashboard. Automating that comparison for a scheduled delivery required a workaround involving a custom date dimension that Kenji built and then nobody understood how to modify after he moved to a different project.
Cost: free (Looker Studio), then $9/user/month (Looker Studio Pro) for scheduled delivery and better permissions. Total cost for our team: $63/month. Setup time: 2 hours for basic, 15+ hours for the dashboard we actually wanted. Ongoing maintenance: about 3 hours/month fixing broken blends, updating date ranges, rebuilding widgets that stopped working after GA4 property changes.
Databox: Beautiful Until You Need Something Custom
We tried Databox next because everyone raved about the mobile app and the pre-built dashboard templates. Setup was fast. The GA4 integration connected in minutes. We picked a template, customized the metrics, and had a dashboard running before lunch.
The mobile experience is genuinely good. Databox was built mobile-first and it shows. The CEO started checking the dashboard on her phone every morning. For the first time, leadership was actually looking at GA4 data without being forced to.
The ceiling appeared when we tried to go beyond the pre-built metrics. Databox uses a concept called "Databoards" with pre-defined metric tiles. You pick from a list of available GA4 metrics and they populate. But the available metrics are a subset of what the GA4 API actually offers. We wanted to track engaged sessions by landing page. Not available. We wanted conversion rate by traffic source filtered to mobile only. Not available as a pre-built metric.
Databox has a custom metric builder, and Tomás spent a morning trying to configure it. The builder uses a formula interface that feels like a simplified version of Excel. It works for basic math (divide metric A by metric B) but cannot handle dimensional filters or conditional logic. The specific metric we needed, conversion rate for mobile users from organic search, required a filtered dimension that the builder could not express.
The pricing also escalated. The free plan has three data sources and basic dashboards. The Professional plan, which we needed for scheduled snapshots and more than five users, was $169/month. With add-ons for additional data sources and priority support, our bill was $230/month.
Priya summarized it well: "Databox is great if you want to see the top ten GA4 metrics on your phone. The moment you want metric number eleven, you hit a wall."
Setup time: 1 hour. Monthly cost: $230. Ongoing maintenance: minimal, because we could not customize it enough to break anything. Limitation: could not produce the report our team actually needed.
Supermetrics: The Plumbing Without the House
Supermetrics takes a fundamentally different approach. It is not a dashboard tool. It is a data pipeline. You configure it to pull data from GA4 (and dozens of other sources) and push it into Google Sheets, Excel, or a data warehouse. The visualization and formatting are up to you.
This was closer to what we needed because it gave us raw data in a place where we could manipulate it. Priya set up a Supermetrics pull that dumped GA4 session data, traffic source data, and conversion data into three tabs of a Google Sheet every Monday morning. The data was clean and the scheduling was reliable.
The problem was everything after the data arrived. The raw output was exactly that: raw. Thousands of rows of dimension-metric combinations with no formatting, no summary, no interpretation. Priya still had to build the actual report on top of the raw data. She wrote formulas, built pivot tables, created summary sections. The data collection was automated. The report building was not.
After three months, Priya's process was: Supermetrics pulls the data on Monday at 6 AM. Priya opens the sheet at 9 AM. Priya spends 90 minutes formatting and summarizing. Priya posts the summary to Slack. We had automated the first 20% of the workflow and left the other 80% manual.
Supermetrics also has a steep learning curve for non-technical users. The query builder uses GA4's dimension and metric names, which are not intuitive. "sessionDefaultChannelGroup" instead of "channel." "screenPageViews" instead of "pageviews." Rafael tried to set up his own query and pulled three weeks of incorrect data because he used "sessions" when he meant "engagedSessions." Nobody caught it for two weeks.
Monthly cost: $99 for the Google Sheets connector. Setup time: 3 hours for the initial queries. Ongoing maintenance: about 2 hours/month fixing broken queries and updating the spreadsheet formulas when requirements changed. Plus 90 minutes every Monday for manual report assembly.
The Option We Missed
After cycling through all three tools over the course of a year, we ended up in a strange place. We had a Supermetrics subscription pulling data, a Looker Studio dashboard that nobody looked at, and a Databox app that the CEO checked but nobody else trusted. Three tools. Three subscriptions. The weekly report was still a manual process.
The thing none of these tools could do was the last mile: read the data, figure out what mattered, and write a summary that a busy person could absorb in two minutes. Dashboards show data. Supermetrics moves data. But nobody interprets the data except a human sitting in a chair on a Monday morning.
That is what an AI agent does. We set up the GA4 Weekly Traffic Report agent and it pulled the same GA4 data that Supermetrics was pulling, except it also wrote the analysis. "Organic traffic up 14% week-over-week, driven primarily by three blog posts published Wednesday. Paid search conversions down 22%, likely related to the campaign pause on Thursday. Mobile bounce rate at 68%, up from 61% last week. Investigate the new mobile landing page." The output landed in Slack. Formatted. Interpreted. Done.
Kenji called it "the report that reads itself." Not entirely accurate, but I knew what he meant.
The agent also did things none of the three tools could do natively. When traffic dropped, it did not just show a red arrow. It looked at which sources dropped, when the drop started, and what else changed around that time. When a new traffic source appeared, it flagged it and provided context. The analysis layer turned raw numbers into information.
What We Would Do Differently
If I were starting from scratch today, I would skip all three tools and start with an agent. The combined cost of our Databox, Looker Studio Pro, and Supermetrics subscriptions was $392/month, plus the hours Priya and Kenji spent setting up and maintaining them. The agent replaced all three.
That said, each tool has a legitimate use case. Looker Studio is excellent for ad hoc data exploration when you want to click around and investigate a hypothesis. Databox is great if your reporting needs are simple and your audience lives on mobile. Supermetrics is the right choice if you need to populate a data warehouse for BI tools downstream.
For weekly marketing reporting, though, all three tools solve the wrong problem. They assume the bottleneck is getting data out of GA4. The actual bottleneck is turning that data into a narrative that drives decisions. Dashboards do not tell stories. Data pipes do not write summaries. An agent does both.
Diana, who sits in the leadership meetings where these reports get discussed, said it plainly. "I don't want to look at a dashboard. I want someone to tell me what happened this week and what we should do about it. The agent does that. The other tools made me do the thinking myself."
Try These Agents
- GA4 Weekly Traffic Report -- Automated weekly analytics summary with trends, anomalies, and narrative delivered to Slack
- GA4 Channel Attribution Analyzer -- Multi-touch attribution analysis across channels with side-by-side model comparison
- GA4 Ecommerce Performance Tracker -- Revenue, product performance, and conversion funnel reporting for ecommerce teams