During a routine analytics project, a Power BI dashboard flagged something that had nothing to do with the original brief.
One user account was hitting a subscription research platform ten times more frequently than anyone else. Five searches per minute. A one-minute pause. Then five more. The same account, logging in simultaneously from China, Taiwan, Australia, and the UK.
It wasn’t a heavy user. It was an automated scraper — and it had probably been running for years.
What started as a project to help an organization understand how its subscribers were using their platform turned into something more valuable: visibility into activity that existing analytics tools had never surfaced.
The challenge: understanding how a subscription platform was being used
Cloudwell was working with an organization that operates a subscription-based online platform used by professionals to access specialized information and digital resources.
Subscribers log in to search, browse, and reference a large library of content that supports their work.
However, the organization had limited visibility into how subscribers were actually using the platform.
The aim was to answer practical questions such as:
– Which organizations were using the platform?
– What were users searching for?
– Which content areas received the most traffic?
As Cloudwell CIO Chris Alechko explains:
“They wanted analytics on who’s using it the most, what types of searches are they making, what pages are they going to the most frequently.”
While the organization already had analytics tools in place, the data was difficult to interpret and didn’t provide depth the company needed.
Building deeper visibility with Power BI analytics
To provide clearer insight into platform activity, Cloudwell implemented a usage analytics pipeline connecting several technologies together.
The approach included:
– Google Analytics to capture user activity
– BigQuery to store high volumes of behavioral data
– Power BI dashboards to analyze behavior patterns and visualize insights
Chris explains why combining these tools was important:
“A lot of people have Google Analytics, but they’re just using it to see how many people go to their site. In this case we went deeper, detecting anomalies and specific users. We built a model that joined the analytics from GA with the customer information from the CRM so we could see not only how people were using the site, but who was using it and for what purposes.”
Once the data was visualized in Power BI dashboards, behavior patterns that were difficult to detect in raw analytics logs became much easier to identify.
The unexpected discovery: unusual behavior in the analytics
After the Power BI dashboards were introduced, one activity pattern quickly stood out.
One account appeared to be accessing the platform far more frequently than others.
Chris recalls:
“One user started popping up very high. They were hitting the site 10 times more than the next person.”
When the team looked more closely at the activity, they noticed a pattern that didn’t resemble normal user behavior.
“They were making five searches every minute, waiting one minute, and then doing another five searches.”
This type of repeated, high-frequency activity strongly suggested the requests were being automated rather than performed by a human user.
What the analytics revealed
Further analysis uncovered additional anomalies.
The same account appeared to be accessing the platform from multiple global locations within short timeframes, something highly unusual for a single user account.
Chris explains:
“The user was logging in with the same username, but the traffic was coming from multiple countries at the same time — China, Taiwan, Australia, the UK.”
Taken together, these patterns strongly suggested automated scraping activity.
Importantly, the data itself wasn’t new.
The activity logs already existed within the analytics environment. The difference was visibility.
Once behavior patterns were visualized in dashboards, the anomaly became immediately obvious.
A problem that may have existed for years
One of the most surprising aspects of the discovery was how long this behavior may have been happening.
Before the dashboards were implemented, the organization had limited visibility into abnormal usage patterns.
According to Chris:
“It had probably been going on for years. The previous Partner they were working with wasn’t collecting the data and they weren’t visualizing it.”
Without proper analytics and visualization tools, unusual behavior can easily remain hidden within large volumes of raw activity data.
Power BI made it possible to surface those patterns quickly.
Responding to suspicious activity
Once the unusual behavior had been identified, Cloudwell was able to introduce additional safeguards to protect the platform.
This included measures like:
– Identifying and reviewing suspicious account activity
– Defining Azure WAF rules to detect abnormal usage patterns
– Introducing rule-based throttling to slow repeated requests
– Enabling front and back-end CAPTCHA solutions that can be targeted to specific users or companies
– Improving monitoring of unusual platform behavior
Chris describes the approach:
“We’re putting more rules in place now with rule-based throttling. If we see abnormal activity like that, we can shut it down automatically.”
These safeguards help ensure legitimate users can continue accessing the platform while reducing the risk of automated data extraction.
The analytics revealed more than anyone expected
The original goal of the project was simply to improve usage analytics and reporting.
But the insight delivered something far more valuable: visibility into behavior that had previously gone unnoticed.
Chris recalls the client’s reaction:
“They said this is something we never had before, and now we have it.”
By transforming raw analytics data into clear visual insights, the organization was able to identify and respond to a potential issue much sooner than would otherwise have been possible.
Frequently Asked Questions
Can Power BI detect suspicious user behavior?
Yes, though it’s the combination of tools that makes detection possible. Power BI doesn’t collect behavioral data itself — it visualizes it. When connected to data sources like Google Analytics and a CRM, Power BI dashboards can surface patterns that are invisible in raw logs: unusually high request frequency from a single account, access from multiple geographic locations simultaneously, or activity that follows an automated rather than human rhythm. The visualization is what makes the anomaly obvious.
How can organizations detect data scraping on their platforms?
The clearest indicators are behavioral: search requests arriving at a fixed cadence (rather than varying human patterns), a single account accessing the platform far more frequently than any other, and logins appearing from multiple countries within the same session. These patterns exist in most analytics environments already — the issue is usually that the data isn’t being visualized in a way that makes them visible. Connecting usage analytics to a Power BI dashboard is often enough to surface activity that has been present, undetected, for years.
Why are usage analytics important for subscription platforms?
Subscription platforms typically have good data on whether users are logging in, but limited visibility into what they’re actually doing. Usage analytics — particularly when behavioral data is joined with CRM records — answer the questions that matter commercially: which organizations are getting the most value, what content is driving engagement, and whether access patterns suggest legitimate use. They also create the conditions to detect misuse, including automated scraping of content that subscribers are paying to access exclusively.
Need Better Visibility Into How Your Platform Is Used?
Understanding how users interact with your systems is essential — not only for improving user experience, but also for identifying unusual behavior and protecting valuable data.
As this example shows, analytics can reveal insights that go far beyond reporting, helping organizations detect abnormal activity, reduce risk and strengthen platform governance.
If your organization would benefit from deeper visibility into platform usage, Cloudwell can help. Our team works with organizations to design and implement analytics solutions using technologies such as Power BI, Microsoft 365 and the Power Platform, enabling clearer insight into how systems are used and where potential risks may exist.
Contact Cloudwell to start a conversation about improving your analytics and platform visibility.