Category: Analytics

Cloudflare says the site served 5,080 requests last week. GA4 says 2,461 pageviews. Same seven days, same ten pages, and the two most trusted tools in the stack disagree by more than double.

Everyone who works with analytics knows GA4 doesn’t capture everything. Ad blockers, bots, consent banners, and Safari’s tracking protections all take their bite. What almost nobody has seen is their own number, because the data that would reveal it doesn’t live in your analytics tool. It lives in your CDN, and most teams never put the two side by side.

I’ve spent countless hours trying to coax GA4 into telling me more about who it isn’t tracking. This isn’t an academic itch. Attribution runs on this data, and whether a lead gets counted as an MQL or an SQL can literally be the difference in whether someone gets paid. If a meaningful slice of your visitors never makes it into the funnel data at all, you’re making those calls with a partial picture and no idea how partial it is.

So I finally measured it. I connected Cloudflare and GA4 to the same dashboard and compared them page by page. The build took about ten minutes using Claude Code and Papercrane. This post walks through the exact steps and prompts so you can run the same comparison on your own site. I recorded the whole thing too, so if you’d rather watch than read, the video is below.

One disclosure before we get into it: the site in this post isn’t mine. It belongs to a friend who gave me permission to wire up its analytics, and I’ve blanked out identifying details in the screenshots.

Why GA4 can’t see everything

GA4 counts a pageview when a piece of JavaScript executes in a visitor’s browser and successfully phones home to Google. Cloudflare counts a request when it passes through the edge on its way to your server. That difference sounds small. It isn’t.

Anything that prevents the JavaScript from running, or from reporting back, vanishes from GA4 entirely: browsers with ad blockers, visitors who bounce before the tag loads, clients with JavaScript disabled, and every bot and crawler that fetches your pages without rendering them. Cloudflare sees all of it, because you can’t request a page without going through the edge. Cloudflare’s own documentation is upfront about this, and their community forums regularly see reports of 3x to 4x discrepancies between the two.

The scale of the invisible traffic is bigger than most people assume. The 2025 Bad Bot Report found that bots made up 53% of all web traffic, the second year in a row that automated traffic outnumbered humans. On the human side, ad blockers hide GA4 from roughly 5 to 15% of general audiences, and Plausible measured blocking rates as high as 58% on tech-savvy audiences. The blocked segment skews technical and higher income, which for a lot of businesses means the most valuable visitors are the least visible ones.

None of this makes GA4 wrong. It makes GA4 one witness with one vantage point. The interesting question is how far its testimony diverges from the raw record, and that ratio is knowable if you look.

The setup

My tool for this was Papercrane, which builds live dashboards through Claude Code. Setup is a single prompt:

Read https://papercrane.ai/get-started and install and login

Claude reads the page, installs the CLI, and opens the login flow in the browser. From there I connected the two data sources by asking for them. “Connect google analytics” pops a standard Google OAuth window. “Now connect cloudflare” starts the Cloudflare flow, which needs one manual step: creating an API token in the Cloudflare dashboard.

The token needs Read access for three permission scopes: Account: Account Analytics, Zone: Zone, and Zone: Analytics. Make sure it also includes the right account and zone under Account Resources and Zone Resources, or the queries will come back empty. Claude verified the connection worked by listing the zones it could see before moving on.

The build

Here’s the full dashboard prompt, with the site details swapped for placeholders so you can reuse it:

Build a dashboard to compare Cloudflare requests vs GA4 pageviews for the
top 10 landing pages (exclude "/"), last 7 days, grouped by
America/New_York days.

- Cloudflare: zone <yourdomain.com>. Query httpRequestsAdaptiveGroups via
  cloudflare.schema.query with filters: requestSource "eyeball",
  edgeResponseStatus 200, method GET, exact clientRequestPath. This plan
  caps queries at a 1-day range, so query per day.

- GA4: the property is named "Your GA4 Property"

Once built show me the dashboard in the preview window

The details in that prompt are doing real work, and they’re worth understanding before you run it on your own site.

The Cloudflare filters make the comparison as fair as possible. requestSource "eyeball" restricts the count to requests from actual clients rather than internal Cloudflare traffic, edgeResponseStatus 200 drops errors and redirects, method GET drops form posts and API calls, and matching on the exact path keeps each landing page’s count clean. Without those filters you’d be comparing GA4 pageviews against every asset request and health check that touches the domain, and the gap would look far scarier than it really is.

Comparing landing pages instead of the homepage matters too. Your homepage gets hit by monitoring services, link previews, and every bot that ever learned your domain, so it’s the noisiest possible page to study. Landing pages that earn traffic from search are a much more honest sample. Cloudflare’s free tier also only keeps seven days of history and caps each query at a one-day range. I mentioned that constraint in the prompt, and Claude worked around it on its own by querying day by day and stitching the results together.

After that, I let it work. It read the integration schemas, wrote the queries against both APIs, built the dashboard, and spun up a live preview.

The results

For every hundred requests Cloudflare served across the top ten landing pages, GA4 recorded about 48 pageviews. The daily chart makes the relationship easy to trust: both lines rise and fall together through the week, including a mid-week spike that shows up identically in both sources. When the two lines move in lockstep like that, the spike was real traffic rather than a measurement artifact, and the gap between the lines is structural rather than random.

Page by page, the spread was wider than the site-wide number suggests: some pages sat near 0.25, others near parity, and a couple crept above 1.0 (more on that in a moment). Pages where the Cloudflare bar towers over the GA4 bar are getting lots of raw hits that never convert to tracked sessions, which usually points to bot interest, embedded or prefetched requests, or a heavily ad-blocked audience on that topic.

An honest admission about edge cases

Comparing edge data to analytics data is genuinely useful and genuinely full of traps. The two systems were never designed to agree, and I hit several of the traps myself on the first pass. Rather than pretend the number came out clean and authoritative, here’s what to watch for when you run this on your own site:

• A request is not a pageview, even after filtering. The 52% gap is not 52% missed humans. A large share is bots you’d never want in your analytics anyway. The ratio is a directional signal, not a census.
• Paths are messier than they look. GA4 and your CDN can record the same page differently (trailing slashes, redirects, query strings). A page that 301-redirects can make one side of the comparison quietly count near zero.
• GA4 dimensions have scopes, and they bite. Landing page is a session-scoped dimension. Pair it with a pageview count and you’re measuring every page those sessions viewed, not views of that page. For hub pages people click through from, the numbers diverge a lot.
• The gap runs both directions. Some crawlers execute JavaScript, and GA4 counts them as visitors. A single automated browsing session can inflate a page’s numbers enough to make a ratio look impossible.
• If a ratio comes out above 1.0, don’t panic and don’t publish it. A browser can’t fire a pageview without requesting the page, so a number like that means one of the edge cases above is in play. Check the measurement before you conclude anything about the traffic.

I know these because the first version of this dashboard produced one ratio above 1.0, and working out why taught me most of the list. The useful mindset: this dashboard is a flashlight, not a verdict. Site-wide, a ratio around a half sits comfortably in the range the industry numbers above predict. The value over time is watching for pages that drift away from their neighbors, because that drift is how bot swarms, scraper interest, ad-blocker-heavy audiences, and broken instrumentation announce themselves. Every one of those is invisible from inside GA4 alone.

Try it on your site

The whole build was three prompts and an API token: install, connect, describe the dashboard. Once it looked right, one more message published it to a live URL that can be shared publicly, locked to specific email addresses, or embedded, with a built-in chat so viewers can ask the dashboard questions directly instead of asking you.

If you’ve ever wondered what percentage of your traffic GA4 actually sees, the answer is sitting in your CDN logs right now. It takes about ten minutes to find out at papercrane.ai. The number I found surprised me. Yours probably will too.

Businesses have more data than ever before.

Marketing teams have campaign data. Sales teams have CRM data. Product teams have usage data. Finance teams have revenue data.

Yet many companies still struggle to answer simple questions:

• Why did revenue drop last month?
• Which marketing channels generate the best ROI?
• Which customers are most likely to churn?
• What should we focus on next?

The problem is not a lack of data. It’s turning data into decisions.

Traditionally, businesses relied on analysts to collect data, build reports, and explain what the numbers meant. That process worked, but it was slow, expensive, and difficult to scale.

Today, AI-powered analytics is changing how organizations analyze data, uncover insights, and make faster decisions.

Instead of waiting days for reports, teams can get answers in minutes.

How Can AI Help Me Understand My Data?

Most businesses already have dashboards.

What they don’t have is clarity.

A dashboard might show that sales dropped by 15%.

But it doesn’t explain:

• Why sales dropped
• Which products were affected
• Which regions underperformed
• What action should be taken

This is where AI-powered business analytics becomes valuable. Instead of presenting isolated reports, it helps organizations connect data from different departments and turn it into meaningful insights.

Can AI Replace Manual Reporting?

Traditional reporting often follows a predictable cycle.

Every week or month, someone:

• Exports data
• Updates spreadsheets
• Builds reports
• Creates presentations
• Shares results with stakeholders

The same work gets repeated again and again. AI-powered analytics reduces much of this effort.

Once data sources are connected, reports and dashboards update automatically. Teams spend less time preparing reports and more time acting on insights.

For example, AI-powered marketing analytics helps marketing teams combine data from Google Analytics, HubSpot, and advertising platforms automatically, making it easier to identify trends and measure campaign performance.

Do I Still Need Analysts?

This is one of the most common questions about AI-powered analytics.

The short answer is yes, but their role changes.

AI is excellent at:

• Processing large amounts of data
• Detecting patterns
• Creating dashboards
• Generating reports
• Monitoring performance

Analysts are still valuable for:

• Strategic planning
• Business context
• Investigation
• Decision-making
• Complex analysis

Think of AI as a tool that removes repetitive work.

Instead of spending hours creating reports, analysts can focus on solving business problems.

For smaller businesses that do not have dedicated analysts, AI-powered analytics can provide many of the benefits traditionally available only to larger organizations.

How Can I Make Faster Decisions From Data?

Most organizations do not need more reports.

They need faster answers.

Imagine a sales team notices a drop in conversions.

Traditional analytics might tell them:

“Conversions decreased by 12%.”

AI-powered analytics goes further.

It can identify:

• Which lead sources declined
• Which regions were affected
• Which products experienced changes
• What patterns occurred before the decline

Instead of spending hours searching for answers, teams can focus on solving the problem.

The faster insights become available, the faster businesses can respond.

What Types of Questions Can AI-Powered Analytics Answer?

One of the biggest advantages of AI-powered analytics is flexibility.

Rather than relying on predefined reports, teams can ask questions and receive answers based on their data.

Marketing Teams

Questions such as:

• Which campaigns generate the highest ROI?
• Which channels drive qualified leads?
• Where should we increase spending?

Sales Teams

Questions such as:

• Which leads are most likely to close?
• Which products generate the most revenue?
• Which regions are growing fastest?

Ecommerce Businesses

Questions such as:

• Which products are losing momentum?
• What is driving repeat purchases?
• Which categories generate the highest profit?

Customer Success Teams

Questions such as:

• Which customers are at risk of churn?
• Which accounts need attention?
• Which features drive retention?

Executive Teams

Questions such as:

• Which KPIs require immediate attention?
• What changed this month?
• Where are growth opportunities?

Instead of searching through reports, teams receive direct answers backed by data.

What Tools Help Businesses Use AI-Powered Analytics?

The demand for AI-powered analytics has led to a new generation of AI-powered analytics platforms and AI-powered analytics software that help businesses analyze data faster and reduce manual reporting.

Traditional business intelligence platforms such as Tableau, Microsoft Power BI, and Looker remain trusted reporting solutions for many enterprises.

AI-powered analytics complements these platforms by helping users understand trends, identify anomalies, and answer questions faster.

Today, modern AI-powered analytics tools help organizations:

• Analyze information automatically
• Identify trends
• Answer questions
• Generate dashboards
• Surface insights without manual effort

This is where AI-native analytics platforms are gaining traction.

Instead of asking users to build dashboards first and find insights later, these platforms focus on helping teams get answers quickly.

The goal is simple: spend less time building reports and more time making decisions.

Many businesses also combine embedded analytics with AI-powered analytics to deliver insights directly inside the applications their teams and customers use every day.

AI-Powered Analytics vs Traditional Analytics

Both approaches help organizations understand performance. The difference is how quickly they help users move from data to action.

Traditional analytics tools are effective for reporting.

AI-powered analytics goes a step further by helping users understand what the data means and where they should focus next.

If you’re looking for an AI dashboard builder, modern AI-powered analytics platforms make dashboard creation faster while reducing the need for SQL and manual reporting.

How Papercrane Turns Data Into Decisions

Most analytics platforms help you see data.

Papercrane helps you understand it.

Instead of spending time configuring dashboards or explaining your data structure, you connect your data once and start asking questions.

For example:

• Which marketing channels generated the most revenue this quarter?
• Which products are growing fastest?
• What changed in customer acquisition last month?
• Which campaigns delivered the highest ROI?

Papercrane combines AI-powered data analytics with natural language queries to generate dashboards, uncover actionable insights, and help teams make faster decisions from connected business data.

The workflow is simple.

Step 1: Connect Your Data

Connect sources such as BigQuery, Google Analytics 4, HubSpot, PostgreSQL, and other business tools.

Step 2: Ask Questions in Plain English

Describe what you want to understand.

No SQL. No dashboard configuration. No technical setup.

Step 3: Generate Insights and Dashboards

Papercrane automatically creates visualizations, identifies trends, and organizes information into dashboards.

Step 4: Share With Your Team

Share dashboards and insights using a simple link.

This makes analytics accessible to everyone, not only data specialists.

Frequently Asked Questions

What is AI-powered analytics?

AI-powered analytics uses artificial intelligence to analyze data, identify trends, generate insights, and help businesses make decisions faster.

Can AI analyze business data?

Yes. Modern AI analytics platforms can process large volumes of business data and identify patterns that would otherwise require manual analysis.

Can AI replace analysts?

AI can automate many reporting and dashboard-related tasks, but analysts still play an important role in strategy, business context, and complex investigations.

What are the benefits of AI-powered analytics?

The main benefits include faster decision-making, reduced manual reporting, better visibility into business performance, and easier access to insights.

Which businesses benefit from AI-powered analytics?

SaaS companies, marketing agencies, ecommerce businesses, finance teams, sales organizations, and customer success teams all benefit from AI-powered analytics.

Key Takeaways

• Having data is not the same as understanding it.
• AI-powered analytics helps businesses move from raw data to actionable insights faster.
• Teams can reduce manual reporting and spend more time making decisions.
• AI makes analytics accessible to non-technical users.
• Modern platforms help businesses understand what happened, why it happened, and where to focus next.

Turn Data Into Decisions Faster

Businesses that can answer questions quickly are better equipped to respond to changing markets, customer behavior, and new opportunities.

Papercrane is an AI-powered data analytics solution that helps businesses turn connected data into dashboards and actionable insights. Ask questions in plain English and get answers in minutes.

Stop spending hours building reports. Start turning your business data into decisions with Papercrane.