One of the most useful things I learned while working on SaaS SEO projects was that rankings and traffic only tell part of the story.
A page can bring thousands of visitors every month and still have very little influence on actual revenue. On the other hand, some pages with relatively low traffic can quietly play an important role in getting users to submit a demo request or become a qualified lead.
To better understand this, I used to perform page-wise lead analysis for most of the SaaS clients I worked with.
The Basic Process
Most of our clients used CRMs like HubSpot or Salesforce for lead tracking.
This allowed us to see:
- The first page a user landed on
- The page visited immediately before submitting a form
- Whether the lead became an MQL
- Whether the lead became an SQL
- Whether the lead was eventually considered a qualified opportunity
Every month I would export the raw lead data and build reports showing:
- Total leads
- Junk leads
- MQLs
- SQLs
- Qualified opportunities
Along with this, I would create two page-level reports.
The first report focused on first-touch attribution.
This showed which pages were introducing users to the website and starting their journey.
The second report focused on last-touch attribution.
This showed which pages users were viewing immediately before submitting a demo request or contact form.
Both reports were useful, but I always felt like something was missing.
The Missing Piece
The problem was that I could see where the journey started and where it ended, but I couldn’t clearly see everything that happened in between.
Google Analytics can help answer this through funnels and path exploration reports, but in practice these reports can become difficult to maintain because users rarely follow the same journey.
Every visitor takes a slightly different path.
I wanted a simple way to see the complete sequence of pages visited before a conversion.
Capturing the Full Journey
To solve this problem, I worked with a developer and implemented a custom solution.
A hidden field was added to forms across the website.
Using JavaScript, the field stored information about pages the user had visited during their session.
When a form was submitted, that journey data was passed into the CRM alongside the lead record.
As a result, I was no longer limited to first-touch and last-touch attribution.
I could see the full path users followed before becoming a lead.
The implementation was not perfect.
After cookie banners and consent mode were introduced, some tracking became less reliable. However, the solution still provided significantly more visibility than I previously had.
An Interesting Observation
After reviewing enough customer journeys, I started noticing a pattern.
Certain integration pages appeared repeatedly in successful journeys.
This was surprising because many of those pages did not receive large amounts of organic traffic.
If I had only looked at traffic reports, I might have considered them low-priority pages.
However, the journey data told a different story.
Users were frequently visiting these pages before becoming leads.
The same pattern appeared with some use-case pages and feature pages.
They were not always responsible for bringing users to the site, but they were helping users build confidence and move closer to conversion.
A Better Way to Measure Page Importance
One enterprise SaaS company I worked with used a simple attribution model that I found useful.
Every conversion was assigned a value of 1.
If a user visited four pages before submitting a demo request, each page received 0.25 points.
For example:
Homepage → Integration Page → Use Case Page → Demo Request
Each page would receive:
- Homepage = 0.25
- Integration Page = 0.25
- Use Case Page = 0.25
- Demo Request Page = 0.25
Over time, pages accumulated attribution points across thousands of journeys.
Pages with higher scores were influencing more conversions, even if they were not generating the most traffic.
This created a much clearer picture of which pages mattered in the buying journey.
How I Used the Data
Once I identified important pages, I could prioritize improvements more confidently.
Some of the actions included:
- Reviewing Microsoft Clarity recordings
- Improving page messaging
- Strengthening calls-to-action
- Adding customer proof and trust signals
- Improving page design and usability
- Fixing technical issues
- Updating content based on user intent
Instead of optimizing pages based only on traffic, I could optimize pages based on their contribution to conversions.
What I Learned
One of the biggest lessons from this process was that some of the most important pages on a SaaS website are not always the pages with the highest traffic.
Traffic explains how users arrive.
Journey analysis helps explain why they convert.
If I were building this system today, I would explore more advanced attribution models and product analytics tools. However, even this relatively simple approach helped uncover insights that were invisible in traditional traffic reports.
For me, it was one of the most practical ways to connect SEO performance with actual business outcomes.