Traffic that shines but doesn't convert: 5 signals that your affiliate is a fraud

Traffic that shines but doesn't convert: 5 signals that your affiliate is a fraud
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In affiliate marketing, everything starts beautifully: traffic is growing, conversions are pouring in, ROI is shining. But after a few weeks, the chargebacks, returns, and zeros in LTV come in, and it turns out that half of the “success” was drawn by bots.

Fraud in affiliate programs is not only a problem of large CPA networks. It is also a daily problem for both beginners in arbitrage and small business owners who have launched a referral program without verification systems.

This article will help you learn to see red flags before the ad goes up in smoke:

understand what honest, “live” traffic looks like;
get a short checklist of fraud signals;
see how even simple AI analysis can save tens of percent of your budget.

Let’s start with the basics.

Honest traffic is when numbers behave naturally. When conversions grow gradually, and do not double in an hour. When the CR is adequate, and not similar to a casino win. When geography and devices match the terms of the offer, and do not look like all users are buying from the same data center. And most importantly, when after conversion customers do not disappear, but stay, return, buy again.

Because the true effectiveness of an affiliate is what will be left of the traffic in 7-30 days.

Top 5 signals that something unclean has started in your affiliate program

Fraud rarely looks like a movie “system warning”. More often, it looks like beautiful statistics that make a manager’s eyes sparkle: CR is growing, conversions are pouring in, and partners are boasting about “new connections”. But if you look closely, the numbers start to tell something completely different.

Here are five signals that should make you press “pause” and check if you’re not wasting money.

1. Suspiciously high CR – but no one is looking at anything

If one publisher has a CR five times higher than the others, but the average time on site is 10 seconds and there are zero page views, this is not “genius traffic,” this is a wake-up call.

Check:

  • averagesession duration and pages/session;
  • the difference between CTR and CR (whether users “jump” directly to conversion);
  • geo – perhaps it also floats suspiciously.

What to do now: stop the campaign, request raw logs, check the time between click and action (CTIT). If the CTIT is 20 seconds, there is almost certainly a script or bot involved.

2. “Instant” conversions or strange synchronization

When 70% of all conversions occur 30-60 seconds after a click, or come in “batches” every five minutes, you are not looking at marketing magic, but at feed automation.

Check it out:

  • the CTIT histogram to see if it is clustered in one short range;
  • time of day of activity;
  • repeatability of patterns in logs.

What to do now: add a random delay before processing postbacks and a limit on the number of conversions per minute. Such things “kill” synthetic peaks.

3. Geo and devices do not match the offer

You buy traffic for Australia, and the reports show India, Vietnam, and US data centers. Users seem to be “from everywhere and nowhere”. This is a classic: VPNs, proxies, emulators or headless browsers.

Check:

  • IP/ASN – whether traffic is coming from data centers;
  • user-agent – whether it is an “iPhone 25” on Android 10;
  • browser language and timezone, often they give out real geo.

What to do now: enable geo-filter, block data centers, add a JS check for mouse or scroll movements – bots don’t do this.

4. Anomalies in postbacks – backdates and duplicates

When conversions come with a delay in hours, or in a batch “with yesterday’s date”, and duplicate orders appear in CRM, it means that someone is playing with postback logic.

Check:

  • event time (event_time) vs. postback time (postback_time);
  • number of duplicatesorder_id;
  • if there are no conversions during the inactive period of the campaign.

What to do now: Enter a unique signature (HMAC) in postbacks, limit the delay window, check for duplicates.

5. Silence after the conversion

There are sales, but a week later there are solid returns, chargebacks, zero repeat purchases. This is a signal that the “users” do not exist, or they did not know what they were buying.

Check:

  • D7/D30 retention (customer return);
  • LTV and %refund;
  • time between conversion and first refund.

What to do now:Enter a hold period for payments, separate partners by traffic quality, track user behavior after the purchase – not just clicks.

Summary:Fraud has no face – only patterns. If you learn to notice these five signals and react immediately, you will get stable, clean statistics and partners you can trust instead of a budget drain.

Minimum set of metrics and where to look at them

To recognize a fraud, you don’t need deep analytics. A few basic metrics are enough to show whether your traffic is real or a bot.

Before the click: track the number of impressions, clicks, and CTR. This data will show how real the audience’s interest in advertising is. If the CTR has risen sharply, but nothing else happens, this is a reason to take a closer look.
After the click: look at the average time spent on the site, the depth of the view, and CTIT (time from click to targeted action). If users close the page in a second or conversions come almost instantly, such traffic is unlikely to be natural.
Post-conversion: analyze the percentage of returns, chargebacks, user retention after 7 and 30 days (D7/D30 retention), and LTV, even in a simplified version. These metrics show whether customers really stay or disappear right after the purchase.”

You can see all this data in the tracker, website analytics, or CRM. If you have access to payment systems, compare the number of successful payments with the actual data in the affiliate program – discrepancies often indicate a problem.

How does AI help without complicated math?

AI doesn’t necessarily mean complex models or an in-house data science department. Even simple algorithms can perform routine work for you and signal when something goes wrong.

Anomalies. Automated alerts notice suspicious spikes or dips. If the partner’s CR is several times higher than average or the CTIT suddenly drops below a threshold, the system will immediately warn you of the risk.
Clusters. AI can group events by IP, device, or user-agent, identifying “clouds” of similar activity. This helps to see botnets, emulated devices, or identical patterns of behavior that a human would not notice in a data set.
Rules and scoring. The most effective approach is to combine several signals into a single risk score. If a high CR, identical IPs, and an unnaturally short CTIT coincide at the same time, the system can automatically put such a partner on hold or transfer the case for manual verification.

AI does not replace an analyst here, but works as an assistant that filters out noise and allows you to respond faster to real threats.

Tools for people: what really works without complicated settings

There is no need to buy expensive software to detect fraud. Basic tools that are easy to connect even without a technical background are enough.

Tracking and logging

If you don’t have a sophisticated tracker yet, any solution like RedTrack, Keitaro, Voluum, or even a Google Sheets spreadsheet where you export your statistics every day will do. The main thing is to keep raw logs (click_id, IP, click time, user-agent). It is this data that will help you prove that certain activity was dishonest later, rather than arguing by eye.

Anti-bot and antifraud filters

It is not necessary to install complex enterprise-level systems right away. There are services that can be connected in a few clicks – for example, Anura, FraudScore, Spider AF, mFilterIt. They track user behavior on the website, detect headless browsers, suspicious IPs, VPNs, and emulated devices. Most of them have a free test or API for quick integration.

Visualization and alerts

When there is too much data, it is better to see everything on one dashboard. Looker Studio or Metabase is perfect for this: a few graphs and you can already see where traffic “jumps”. And for daily monitoring, a simple bot in Telegram or Slack that sends notifications is enough:
“CR of partner #45 is 3 times higher than the average. Check it out.

Such a system does not require a data analyst, but gives you control over the situation in real time.

Frequent mistakes of beginners

Fraud often occurs not because of the lack of systems, but because of the wrong approach to analysis.

1. Evaluate a partner only by CR or ROI “per day.”

Short slices do not show the full picture. Today, an affiliate may look “golden”, and in a week, you will receive a wave of returns.

2. Mix retargeting and organic with affiliate traffic.

It’s like measuring the effectiveness of advertising by counting your own users for the second time. Always separate the sources, otherwise the analytics will be distorted.

3. Do not save raw logs and do not sign postbacks.

Without logs, you will not be able to prove that the action really took place or that the partner has changed the data. A signature in postbacks (HMAC) is the minimum level of protection for any affiliate.

4. Ban everything without evidence

Impulsive blocking only spoils relations with bona fide partners. Collect facts first, check logs, and then make a decision. A company’s reputation is worth more than one dubious publisher.

Fraud doesn’t like consistency. If you have basic tracking, filters, and clear verification rules, you are already one step ahead of most people who hope to get lucky.

Conclusion

Fraud is not defeated by one-time bans. It is restrained by consistency: transparent rules, basic metrics, raw logs, simple automations, and cold discipline in checks. If you look not only at the “current CR” but at the quality of traffic in 7-30 days, connect basic alerts, and keep partners under the same payment rules, most risks disappear before budgets have time to burn.

After reading this article, you already have a minimum set: what honest traffic looks like, five key fraud signals, a short checklist, and a clear way to use AI as an assistant to detect anomalies, clusters, and risky combinations.

What to do today:

  1. Enable saving raw logs and signing HMAC postbacks.
  2. Add three alerts: CR partner > 2.5× median, p25 CTIT < 30 seconds, share of IP from data centers > 5%.
  3. Revise the payout policy: a small hold for new partners and rate increases only after proven quality.
  4. Separate retargeting, organic and affiliate traffic in reporting.

An affiliate who chooses quality and process will always win over an affiliate who is chasing a quick CR. Create a framework, give tools, stick to data, and your budget will work for growth, not for feeding other people’s bot farms.

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