BrandJet Website Analytics lets you investigate campaign traffic by moving from acquisition to on-site behavior, then to audience and technology patterns. The most useful sequence is Channel -> Referrer -> Campaign -> Pages -> Entry Page -> Exit Link -> Map, Countries, or Cities -> Browsers -> OS, followed by a written conversion hypothesis.
Analyze campaign traffic in BrandJet Website Analytics: step-by-step screenshots
Follow the current BrandJet interface below. Each screenshot matches the numbered action directly beneath it.
Step 1: Choose the reporting window before reading any traffic report.
Step 2: Open Channel for the broad acquisition pattern.
Step 3: Open Referrer to identify the source behind the traffic.
Step 4: Open Campaign to isolate tagged initiatives.
Step 5: Open Pages to see where activity concentrated.
Step 6: Open Entry Page to see where sessions started.
Step 7: Open Exit Link to see where visitors left the site.
Step 8: Open Countries to check whether traffic reached the intended market.
Step 9: Open Browsers to spot browser-specific patterns.
Step 10: Open OS to reproduce device and operating-system issues.
Analyze one BrandJet campaign traffic window
Outcome: Identify where campaign traffic came from, where visitors entered and exited, whether the audience matched your intended market, and what to validate next.
Prerequisites: BrandJet Website Analytics must already be receiving data for the pages you want to analyze. You also need one campaign or campaign period to investigate, plus a clear idea of the page or conversion you expected that campaign to influence.
If tracking is not yet active, follow BrandJet's Website Analytics setup documentation before using this workflow.
1. Open Website Analytics and choose one campaign window
Start in BrandJet Website Analytics.
Before interpreting any report, define the campaign and date range you are investigating. Keep that same period throughout the analysis so Channel, Referrer, Campaign, Pages, Entry Page, Exit Link, Map, Countries, Cities, Browsers, and OS remain comparable. Use the summary cards, including Visitors, Returning visitors, Returning visitor rate, Bounce rate, and Avg. session time, as context rather than as substitutes for the report sequence below.
Write down four things:
- The campaign you are analyzing
- The date range
- The page you expected visitors to reach
- The action you wanted visitors to take
For example:
| Question | Example |
|---|---|
| Campaign | Partner launch |
| Expected traffic | Partner referral |
| Target page | Pricing page |
| Desired action | Demo request |
The example is illustrative. Use your actual campaign and website path.
Start with the campaign question, not the traffic spike
Do not begin by looking for the largest spike and then deciding what caused it. Start with the campaign question first.
A useful question is:
Did this campaign bring the expected type of traffic to the expected page, and what happened next?
That gives every following view a specific purpose.
2. Check Channel for the broad acquisition pattern
Open Channel first.
Use this view to answer:
What type of traffic changed during the campaign period?
You are looking for the broad acquisition category that best matches the campaign you ran.
Channel is the starting point, not the final attribution answer. A broad category can tell you where to investigate, but it does not necessarily identify the exact source or campaign.
Compare the visible channel mix with what you expected before launch.
For example, if a partner promotion was expected to produce referral traffic, ask whether the relevant channel appears meaningful during the selected period.
Use Channel as a starting point, not final attribution
If the channel mix does not resemble the campaign distribution plan, do not assume the campaign worked simply because total traffic increased.
Move to Referrer before drawing a conclusion.
3. Open Referrer to identify the source behind the traffic
Next, open Referrer while keeping the same campaign window.
Use Referrer to narrow the acquisition question:
Which source appears behind the visits?
Look for the website or source associated with the campaign you are investigating.
If a partner campaign was distributed through a specific site and that source appears in Referrer during the campaign window, you have stronger evidence than timing alone.
However, that still does not prove every visit from that source was generated by the campaign.
Treat Referrer as evidence, not causal proof
Treat Referrer as evidence, not automatic causal proof.
A visit can occur during the same period because of:
- Another promotion
- A forwarded link
- Existing referral traffic
- Organic discovery of the same page
- Another marketing activity using the same source
If the source is ambiguous, keep the attribution ambiguous.
4. Check Campaign for a tagged initiative
After Channel and Referrer, open Campaign.
Use this view to answer:
Can the traffic be connected to a specific tagged campaign?
Campaign analysis becomes much stronger when the links you control use consistent campaign tagging.
For example, a campaign URL might distinguish:
?utm_source=partner_name&utm_medium=referral&utm_campaign=q3_launch
The exact naming convention is up to your team. What matters is that the same naming logic is used consistently.
When a campaign label appears, compare it with the actual tagged URL used in the campaign instead of relying on memory.
Use Channel, Referrer, and Campaign as an evidence ladder
Use this acquisition hierarchy:
Channel -> Referrer -> Campaign
Do not stop at Channel if Referrer or Campaign can provide more specific evidence.
Do not assign an untagged or unclear visit to a campaign simply because it happened after a launch or send.
5. Open Pages to see where website activity concentrated
Once you understand acquisition, move to Pages.
Use Pages to answer:
Which pages received activity during this campaign period?
Focus on pages that matter to the campaign journey, such as:
- The landing page
- Pricing
- Product pages
- Feature pages
- Contact or demo pages
- Other pages connected to the intended conversion
A page appearing prominently in Pages tells you that visitors reached or viewed it. It does not tell you where their sessions started or ended.
That is why the next two views matter.
6. Check Entry Page to see where sessions started
Open Entry Page.
Now ask:
Where did campaign-period sessions begin?
Compare the leading entry pages with the URL the campaign was supposed to send people to.
If the campaign linked directly to a pricing page, but sessions appear to begin elsewhere, investigate before evaluating pricing-page performance.
Possible questions include:
- Was the campaign URL correct?
- Was the intended page actually used?
- Was there a redirect?
- Was traffic coming through another source?
- Is the campaign assumption wrong?
Do not infer the answer from the traffic pattern alone.
Keep Pages and Entry Page separate
Treat Pages and Entry Page as different questions.
- Pages tells you which pages received activity.
- Entry Page tells you where a session started.
A page can perform strongly in Pages without being the main entry page.
7. Use Exit Link to see where sessions ended
Next, open Exit Link.
Use this view to answer:
Where did sessions finish?
This is useful for identifying pages that deserve closer review, but do not use a simple rule such as:
High exits = bad page
A visitor can legitimately finish on:
- A confirmation page
- A pricing page after getting the required information
- Documentation
- A contact page
- A page that fully answered the question
Instead, compare the exit page with the intended journey.
A more useful decision table is:
| Pattern | Next action |
|---|---|
| Expected entry and expected final page | Check whether the desired action occurred |
| Expected entry and frequent exit before the intended action | Investigate possible friction |
| Unexpected entry page | Verify links, redirects, and traffic source |
| Strong page activity with weak campaign evidence | Keep attribution uncertain |
The useful sequence is:
Pages -> Entry Page -> Exit Link
Together, these views help you reconstruct the role each page played without treating every exit as a problem.
8. Use Map, Countries, and Cities to check the intended market
Open Map, Countries, or Cities using the same date range.
Ask:
Did the traffic appear in the markets the campaign was intended to reach?
If the campaign targeted a particular country or region, compare that expectation with the visible geographic distribution.
There are two basic outcomes.
Expected market is prominent
If the intended market appears strongly, continue to page behavior and conversion quality.
The traffic may be directionally aligned with your targeting, but geography alone does not tell you whether the visits were qualified or valuable.
An unexpected market is prominent
If another market appears unusually strong, investigate:
- Campaign targeting
- Referral context
- Distribution partners
- Campaign tagging
- Other traffic sources active during the same period
Use location only as an aggregate audience signal
Use geography as an aggregate audience-fit signal.
Do not use it to make claims about an individual visitor's identity or intent.
BrandJet's public Website Analytics documentation describes its tracking as cookieless and states that visitor PII is not stored. Company-level B2B identification may be available when a company can be resolved. See BrandJet's Website Analytics documentation.
9. Check Browsers for a possible technical pattern
Open Browsers.
Use it when the campaign appears to reach the intended pages but the resulting behavior is weaker or stranger than expected.
Ask:
Does one browser show a pattern that deserves technical review?
Do not immediately call the pattern a browser bug.
Use four checks:
- Spot the pattern: Identify the unusual browser segment.
- Check volume: Make sure the segment has enough relevant traffic to investigate.
- Reproduce: Test the actual campaign path in that browser.
- Confirm or reject: Change the site only if the problem can be verified.
A small sample can create a dramatic-looking pattern that has little practical importance.
10. Check OS and reproduce the environment
Open OS after Browsers.
Use it to narrow the technical hypothesis further.
The diagnostic sequence is:
Browsers -> OS -> reproduce the actual journey
For example, if one browser and operating system combination appears unusual, run the same campaign path using that environment.
Test the actual pages involved:
- Open the campaign link
- Follow the intended website path
- Reach the conversion step
- Check whether the suspected problem can be reproduced
Reproduce the Browser and OS pattern before changing the site
Technology data tells you where to look, not what caused the result.
Do not change campaign targeting, page copy, forms, or site behavior solely because Browsers or OS data looks different.
Verify the experience first.
11. Turn the pattern into one testable conversion hypothesis
Finish the analysis by writing an observation before writing an explanation.
For example:
Observation: Campaign-period sessions reached the pricing page, and the page also appeared frequently in exit behavior.
Do not immediately write:
Conclusion: The pricing page is causing visitors to leave.
Instead, list at least two plausible explanations.
Possible cause: the pricing page creates friction
The pricing page creates conversion friction.
Alternative cause: visitors completed their research
Visitors received the information they needed and intentionally finished there.
A third hypothesis might involve a technical problem affecting a particular browser or OS.
Now choose the cheapest validation step that can separate those explanations.
Check the evidence you already have:
- Conversion activity
- Entry Page context
- Exit Link context
- Browser patterns
- OS patterns
- The live conversion path
Then run the smallest useful test.
Use this framework:
Observation -> Possible causes -> Cheapest check -> Test -> Decision
That keeps Website Analytics focused on action rather than dashboard reporting.
If you also need to compare website behavior with outreach performance, use BrandJet campaign analytics. Website traffic and campaign outreach metrics answer different questions, so do not treat a website visit as a reply, qualified lead, pipeline event, or revenue result without supporting evidence.
FAQ
I can see traffic, but I cannot confidently connect it to a campaign
Check Channel, then Referrer, then Campaign. If the source remains unclear or tagging is missing, keep the traffic unattributed rather than assigning it by timing alone.
The expected landing page is not the main entry page
Verify the campaign URL, redirects, tagging, and actual distribution path. Do not assume the page itself is responsible.
A page has many exits
Check whether the page is a reasonable final destination. Then review conversion evidence and the intended journey before treating the exit pattern as friction.
Map, Countries, or Cities do not match the target market
Review targeting, referral context, tagging, and other acquisition sources active during the same period. Location is a signal to investigate, not proof of bad targeting.
One browser or OS looks weak
Check whether the segment has enough volume, then reproduce the actual campaign journey in that environment. Only escalate the issue if the problem can be verified.
The reports tell different stories
That is expected when they answer different questions. Read them in sequence:
Channel -> Referrer -> Campaign -> Pages -> Entry Page -> Exit Link -> Map, Countries, or Cities -> Browsers -> OS
The final goal is not to make every report agree. It is to produce one well-supported next test.