Building a check routine for the week after you buy traffic
Last updated: September 7, 2026
A campaign report showing every visit landing, every session lasting exactly the same length, and every action happening in a suspiciously tidy pattern usually signals automated traffic dressed up as a real audience, not a well-optimised supplier. Most buyers only look closely at these details after a campaign has already disappointed them, once digging through logs is slower and less conclusive than checking as delivery happens. A short routine run in the first week after a decision to buy traffic catches most of these problems while there is still time to act.
Checks worth running right after you buy traffic
A quick review of arrival timestamps, spread across the day rather than clustered into a few improbable bursts, is the fastest first check worth running right after you buy traffic, since a real audience rarely arrives in perfectly even five minute intervals for hours at a stretch.
A traffic source that is genuinely diverse also shows some natural clustering around peak browsing hours for the target country, which is a different pattern from either a perfectly flat distribution or a series of identical bursts, and distinguishing the two takes only a glance at an hourly chart.
Comparing the delivered volume against the promised volume for the exact time window paid for, rather than waiting for a full campaign to finish, catches an underdelivery problem early enough to raise it while the supplier still has an incentive to fix it.
A supplier who resists sharing raw delivery timestamps, offering only a summary total at the end of the campaign, is making this first check harder than it needs to be, and that resistance is itself worth noting before the next purchase from the same source.
A supplier who instead offers a live dashboard with raw, unaggregated numbers, updated throughout the day rather than once at the end, is signalling confidence in what they are actually delivering, and that confidence is worth something even before the numbers themselves are examined closely.
A five minute check worth running daily
Total sessions, average session duration and bounce rate, checked once a day rather than once at the end of the campaign, surface a problem within twenty four hours instead of within two weeks, which is usually the difference between a fixable issue and a wasted budget. This matters even more for a small test batch bought through buy web traffic cheap, where a delivery problem eats a much larger share of a modest budget.
Log signals that separate real sessions from bots after you buy traffic
A session duration that reads as suspiciously uniform across thousands of visits, down to the second, is one of the clearest signals available after you buy traffic and start reviewing logs, since real human behaviour naturally varies far more than any bot script bothers to simulate.
A single IP address or a narrow range of IP addresses responsible for a disproportionate share of total sessions is a second signal worth checking, since legitimate audiences are rarely concentrated behind so few connection points at once.
An adult ad network deals with this exact problem constantly, since narrower verticals attract a disproportionate share of low quality resold volume, and the same log review habits that catch it there apply without modification to a general audience campaign.
| Signal | Suspicion level | Why |
|---|---|---|
| Uniform session duration | High | Real behaviour varies naturally |
| Narrow IP range | High | Legitimate audiences spread out |
| Zero scroll depth every visit | Moderate | Some real visitors do bounce quickly |
| Identical device and browser string | High | Real audiences use varied devices |
Tools that make this check easier
Server side logs, cross checked against the analytics platform's own numbers, reveal a gap that client side tracking alone would miss, since a visit blocked by an ad blocker or privacy extension still hits the server but may never register in a dashboard relying only on a tracking script.
Exporting both data sets into a single spreadsheet, rather than comparing two dashboards side by side from memory, makes the gap between them easy to spot and easy to show to a supplier if the gap turns out to be worth raising as a dispute later. A general reference on which mechanisms carry more or less of this risk sits under buy web traffic for anyone comparing sources before committing to a specific supplier.
Refund conversations once you buy traffic and dispute a delivery
A written record of the delivery numbers, the timestamps checked and the specific signals that raised concern gives a refund conversation something concrete to point to, rather than a vague complaint that the traffic felt low quality once someone decided to buy traffic and later wanted money back.
Most legitimate suppliers respond better to a specific, evidenced claim than to a general one, since a specific claim gives them something to investigate on their own side rather than a subjective impression they have no way to verify or act on quickly.
Screenshotting the dashboard at the moment a concern is noticed, rather than relying on a description written from memory later, preserves evidence a supplier cannot dispute simply by pointing to numbers that changed after the fact.
What a fair refund request usually needs
The exact delivery window in question, the expected volume against the delivered volume, and the specific evidence of low quality, whether that is a bot signal or a documented underdelivery, are the three elements that turn a refund request from a complaint into a claim a supplier can actually process.
Sending this documentation promptly, within a day or two of noticing the problem rather than weeks later, also matters, since a supplier's own logs eventually roll off their retention window and a late claim may arrive after the evidence they would need to verify it has already been discarded.
Filters that keep future decisions to buy traffic accountable
A filter excluding known bot-heavy networks and data centre IP ranges, applied before a campaign starts rather than after reviewing disappointing results, keeps a future decision to buy traffic from repeating a mistake that a simple setting could have prevented from the outset.
Updating that filter periodically matters too, since the specific networks associated with low quality volume change over time, and a filter left untouched for a year is working from an outdated list of problems rather than a current one.
A quarterly calendar reminder, rather than an informal intention to check back eventually, is usually the only thing that actually gets a stale filter list updated on any consistent schedule at all. Most advertising platforms publish an updated exclusion list buyers can subscribe to directly.
| Filter type | Action | Why |
|---|---|---|
| Data centre IP ranges | Exclude by default | Rarely represent real visitors |
| Known bot-heavy networks | Exclude, review quarterly | List changes over time |
| Ad blocker detection | Track, do not exclude | Real visitors also use blockers |
| Duplicate device fingerprints | Flag for review | May indicate double counting |
Where to find a current filter list
Several open source and commercial lists of known low quality IP ranges get updated regularly, and subscribing to one rather than maintaining a private list by hand keeps the filter current without requiring ongoing manual research from a small team already stretched across other priorities.
Several of these lists are free to subscribe to and integrate directly into common analytics and ad platforms, which removes the excuse of cost from a team that has simply not gotten around to setting the exclusion up yet.
Documentation habits for anyone who will buy traffic again next quarter
A shared log documenting which supplier ran during which week, what the delivery numbers actually were, and which of them passed or failed the fraud checks above turns every future decision to buy traffic into an informed one rather than a fresh guess repeated every quarter.
New team members inherit this log instead of the informal memory of whoever ran the last campaign, which matters more than it sounds once the person who originally vetted a supplier has moved on to a different role or a different company entirely.
A log that lives in a shared spreadsheet everyone on the team can access, rather than in one person's private notes, survives staff turnover far better and takes only a few minutes to set up correctly the first time. The same habit runs through most of the supplier vetting guidance in the wider Popunder Ad Network library.
A minimum template worth keeping
Supplier name, delivery dates, promised versus delivered volume, fraud check result, and refund outcome if one was requested cover the minimum a future reader needs to judge a past supplier without re-running the entire investigation from scratch. A wider comparison of supplier vetting practices sits under website traffic pages for buyers not limited to this specific checklist.
None of these checks takes more than a few minutes individually, and together they turn a campaign from a leap of faith into something a team can actually evaluate. I run mine against the delivery notes published on buy traffic listings, since comparing promised specifics against actual logs is far more useful than comparing one invoice total against another. The invoice was never the part of the transaction worth arguing about first.