If you’ve spent any time in the modern Google Analytics ecosystem, you’ve probably found yourself staring at three different tools, wondering which one you’re supposed to use.
GA4 Explorations, Looker Studio, and BigQuery all touch your analytics data. They all produce reports. So, it’s easy to conflate them, yet they’re built for very different jobs. That’s an important detail that’s easy to overlook when you’re under pressure to pull numbers for a meeting that started five minutes ago.
Why is that a problem? Consider:
- Analytics managers wasting hours building a report in the wrong tool, only to have to rebuild it again later.
- Development leads stuck fielding repeated requests for BigQuery access when a two-minute Exploration would do the trick.
- Marketing directors impatiently waiting days for dashboards that could’ve been self-serve from the start.
Those are process nightmares we see with the teams we work with. The good news is that this isn’t a problem with your tools, but a tool selection problem, and one that’s easy to fix. Once you know which tool fits which job, everything gets faster, cleaner, and a lot less frustrating.
In this post, we’ll break down when to use each tool, who each one is really built for, and how to stop defaulting to whatever you used last time. Whether you’re an analytics lead trying to streamline your reporting stack or a marketing director trying to get answers without filing a ticket, this framework will help you work smarter.
A Quick Primer: What Each Tool Actually Does
Before we get into the decision-making framework, let’s level-set on what makes these three tools distinct.
GA4 Explorations
This is the advanced analysis workspace built directly into the GA4 interface. If you’ve clicked over to the “Explore” tab, you’ve seen it. Explorations enables you to build custom reports using techniques like funnel analysis, path exploration, segment overlap, and free-form data tables. It’s flexible, fast, and doesn’t require any setup beyond GA4 access.
Sounds simple, but here’s the catch: It’s tied to your GA4 property, limited to roughly 14 months of historical data, and can hit sampling thresholds on high-traffic sites. It’s also not built for scheduled reporting or sharing with people who don’t have GA4 logins.
Looker Studio
Looker Studio (formerly Google Data Studio) is Google’s free dashboarding and visualization tool. It connects natively to GA4, Google Ads, Google Sheets, and dozens of other sources via community connectors. You can blend data, build polished dashboards, schedule email delivery, and share interactive reports with anyone, all with no login required.
Those qualities make it ideal for recurring reports and stakeholder-facing dashboards. However, it’s not a great fit for deep, ad-hoc analysis. And while you can connect it to BigQuery, Looker Studio alone won’t help you query raw event-level data or join analytics with your CRM.
BigQuery
BigQuery is Google’s cloud data warehouse, and it’s where your raw GA4 data can live if you enable the export. By raw data, we’re talking about event-level, unsampled, unlimited-retention data — the full firehose.
With BigQuery, you can write SQL queries to answer questions that GA4 simply can’t. Think of things like joining web behavior with backend revenue data, analyzing user journeys across 18+ months, or building custom attribution models. The trade-off is that it requires a GCP project, some SQL knowledge (or a BI tool on top), and a bit more setup than the other two options.
The Decision Framework: Five Questions to Ask Before You Build Anything
Now that we’ve highlighted what makes each tool unique, how can you decide which will be the best for the job? Following this five-question framework can save you (and your team) a lot of wasted effort.
1. Who is the audience?
This is the most important question, and it should be your starting point every time.
- Executives and senior leadership? They want polished, digestible dashboards they can glance at between meetings, ideally delivered straight to their inbox. That’s Looker Studio.
- Analysts and data-savvy marketers? They need flexibility to dig, segment, and explore. That means GA4 Explorations or BigQuery, depending on complexity.
- Cross-functional teams or clients? They need self-service access with guardrails, think clean dashboards that they can filter without breaking anything. Looker Studio again.
Building a BigQuery pipeline to serve an executive who just wants a weekly traffic snapshot is overkill. Building a Looker Studio dashboard for an analyst who needs to investigate a conversion drop won’t get the job done. Match the tool to the audience.
2. Is this a one-time analysis or a recurring report?
- Recurring, scheduled reports → Looker Studio (with scheduled email delivery) or BigQuery feeding a BI layer
- One-time deep dives → GA4 Explorations or BigQuery
- Ad-hoc analysis you’ll probably repeat → GA4 Explorations (save it as a template for next time)
If you find yourself rebuilding the same Exploration every month, that’s a sign it should be a Looker Studio dashboard. If you’re constantly tweaking a Looker Studio report to answer slightly different questions, maybe Explorations would serve you better.
3. How far back do you need to look?
GA4’s interface — including Explorations — typically retains around 14 months of data. For most day-to-day reporting, that’s fine. But if you need to analyze long-term trends, compare year-over-year performance beyond that window, or build cohort analyses that stretch back more than 18 months, you’re going to hit a wall.
That’s where BigQuery shines. If you’ve enabled the GA4-to-BigQuery export, your data lives there indefinitely. No retention limits, no sampling, no compromises, no problem.
4. Do you need to combine analytics data with other sources?
This question helps distinguish “reporting” from “real analysis” for your teams.
- GA4 data only? Explorations handles this well.
- GA4 plus a few marketing platforms (Google Ads, Meta, LinkedIn)? Looker Studio can blend these with native and community connectors.
- GA4 plus CRM data, revenue systems, inventory, and offline conversions? You need BigQuery. It’s the only place you can join GA4 event data with external tables using a shared key, such as user ID or client ID.
If your CMO wants to see how web behavior correlates with closed-won revenue in Salesforce, Looker Studio alone won’t cut it. You’ll need BigQuery to do the join, then (optionally) Looker Studio to visualize the output.
5. What’s the technical skill level of the person using this?
Be honest here. Tools are only useful if people can actually use them.
- Non-technical stakeholders → Looker Studio. There’s no SQL, no GA4 login, just point-and-click filtering anyone can use.
- Analytics-literate marketers → GA4 Explorations. There will be some learning curve, but it’s manageable for anyone comfortable in GA4.
- Data team members, SQL-proficient analysts → BigQuery. Very powerful, but it’s not going to be accessible to everyone.
There’s no shame in meeting people where they are. A beautiful BigQuery pipeline is worthless if the marketing director can’t get answers without pinging your data team.
The Use Case Playbook
Frameworks are great, but specifics are better. Here’s how tool selection plays out in real scenarios we see all the time.
| Use Case 1: Weekly Executive Dashboard | Best tool: Looker Studio |
| Why: Executives don’t want to log into GA4. They want a clean, scannable dashboard that hits their inbox every Monday morning. Looker Studio is the best choice to schedule delivery, control exactly what’s shown, and keep things visually polished. | Pro tip: Resist the urge to cram everything onto one page. One to two pages max, focused on the KPIs that tie directly to business goals. If it doesn’t drive a decision, leave it out. |
| Use Case 2: “Why Did Conversions Drop Last Tuesday?” | Best tool: GA4 Explorations → BigQuery (if needed) |
| Why: This is a classic ad-hoc investigation. Start with Explorations, build segments, compare date ranges, and look at funnel drop-off. In most cases, you’ll find your answer in 15 minutes.However, if you’re dealing with high traffic volumes and sampling is skewing your data, or you need to look at individual user sessions to find the culprit, escalate to BigQuery for event-level precision. | Pro tip: Build a saved “conversion diagnostics” Exploration template. That way, when something breaks, you’ll be ready. |
| Use Case 3: Customer Journey and Path Analysis | Best tool: GA4 Explorations (Path Exploration) |
| Why: GA4’s Path Exploration is purpose-built for this. You can visualize how users move through your site, filter by audience segment, and spot unexpected drop-off points. The best part is you can do all of that without writing a line of code. | Pro tip: GA4 does have a limitation to watch for: Path reports can hit sampling thresholds on high-traffic sites. If precision matters (and it usually does for journey analysis), be sure to validate key findings in BigQuery to ensure nothing is being missed. |
| Use Case 4: Blending GA4 with Ad Platform Spend Data | Best tool: Looker Studio |
| Why: Looker Studio has native connectors for Google Ads and community connectors for Meta, LinkedIn, TikTok, and more. You can blend this with GA4 data to create a unified view of spend, traffic, and conversions. | Pro tip: Be careful with data blending. Mismatched date granularity is the most common mistake and can lead to a lot of headaches. Make sure your date fields align across sources, or you’ll end up with confusing gaps and duplications. |
| Use Case 5: Cohort Retention Analysis Over 18+ Months | Best tool: BigQuery |
| Why: GA4’s UI caps your historical lookback at roughly 14 months. So, if you want to understand how last year’s cohort compares to this year’s — or track retention over longer windows — BigQuery is your only option. | Pro tip: Schedule your cohort queries to run monthly and pipe the results into Looker Studio. That way, you get BigQuery’s power with Looker Studio’s accessibility. |
| Use Case 6: Joining Web Behavior with CRM Revenue Data | Best tool: BigQuery |
| Why: This is where BigQuery becomes non-negotiable. To connect what users do on your site with what they’re worth to your business, you need to join GA4 event data with your CRM or revenue system. That join happens in BigQuery, typically via user ID or a hashed client ID. *Note: You can pull CRM data into Looker Studio, but complex data needs may not be possible. | Pro tip: Hash any PII before joining and document your key-matching logic clearly. This is one of those analyses that’s easy to get wrong in subtle ways. |
| Use Case 7: Self-Service Reporting for Marketing Managers | Best tool: Looker Studio with controlled data sources |
| Why: Marketing managers want answers without waiting for the data team. Looker Studio lets you give them autonomy. They can filter by campaign, date range, or channel without risking misinterpretation of raw data. | Pro tip: Use Looker Studio’s field editing features to pre-define clean metric names and hide confusing dimensions. A little upfront curation goes a long way toward preventing “the numbers don’t match” conversations. |
Mistakes We See All the Time
Even smart teams fall into these traps. Here are some common mistakes to avoid.
Using GA4 Explorations for recurring reports. There are a few issues here. 1) Explorations don’t support scheduling. 2) Links can break when property settings change. And 3) anyone who wants to see the report needs a GA4 login. If it’s recurring, move it to Looker Studio.
Building executive dashboards in BigQuery without a visualization layer. BigQuery outputs tables. The problem is that most executives don’t want tables; they want charts, trendlines, and color-coded KPIs. Always pair BigQuery with Looker Studio (or another BI tool) for stakeholder-facing reports.
Jumping to BigQuery for questions GA4 answers in two minutes. BigQuery is powerful, but it’s not always necessary. If you can get your answer with a quick Exploration, don’t over-engineer it. Save yourself time by saving BigQuery for the jobs that actually require it.
Ignoring sampling in GA4 Explorations. On high-traffic properties, Explorations can sometimes use as little as 10% of your data. If you’re making business decisions based on Exploration data, check the sampling indicator. If it’s significant, validate the findings in BigQuery.
Creating new Looker Studio dashboards without governance. It’s easy to spin up new dashboards. It’s much harder to maintain 47 of them. Dashboard sprawl makes everyone’s life harder so be sure to establish good governance practices. Our tips: establish a naming convention, clarify document ownership, and sunset dashboards that aren’t being used.
Building Your Reporting Stack: A Tiered Approach
Understanding how and when to use each tool is essential, but here’s a trick to keep in mind: they all work best when they’re connected. Instead of thinking of GA4 Explorations, Looker Studio, and BigQuery as three separate tools, think of them as three tiers of a single reporting stack. Each tier has a job, moving from smaller, daily tasks up to deep data dives.
The magic happens when these tiers talk to each other. For example:
- Using GA4 Explorations to validate a hypothesis before you invest in a full BigQuery analysis.
- Then using BigQuery to power your Looker Studio dashboards to get a comprehensive look at the data
- Then using Looker Studio to present the 20% of insights that matter to the 80% of stakeholders who’ll never touch GA4.
Quick-Reference Decision Matrix
When in doubt about which tool to use, refer to this quick reference guide:
The Bottom Line
When you’re doing analytics, there is no single “best” reporting tool — there’s only the best tool for this question, this audience, and this moment.
The teams that get this right aren’t the ones with the fanciest dashboards or the most complex BigQuery pipelines. They’re the ones who’ve thought intentionally about which tool belongs where and then built habits around choosing them.
- Start with the stakeholder.
- Consider the use case.
- Match the tool to the job.
One last piece of advice: revisit your reporting stack quarterly, because your team’s capabilities and data needs will evolve.
When your tools are working in concert, reporting stops being a bottleneck and starts being the thing that actually drives better decisions.
Need help building a reporting stack that fits your team? We work with analytics and marketing teams to design measurement strategies that scale, everything from GA4 implementation to BigQuery architecture to dashboards that stakeholders actually use.
