Tag: #affiliate_marketing

Duplicates on Facebook* occur when the same user submits a form multiple times, and the advertiser pays for each lead separately. This problem most often arises when working with lead forms—a built-in Facebook* tool that allows you to collect contact information directly within the social network without redirecting to a third-party site.

The platform’s algorithms don’t always manage to correctly filter out duplicate actions. The situation becomes even more complicated during mass account suspensions: as soon as the main advertising account goes under review, access to pixel data is lost. When new accounts are launched, ads start running again to the same audience, leading to duplicate submissions and unnecessary spending.

In the context of changes to the platform’s algorithms, this becomes particularly critical—it’s discussed in more detail here.

The problem of duplicate leads: why standard tools don’t help

Facebook’s advertising algorithms process data with a delay. By the time the system records a conversion, the user may have seen the same ad multiple times and submitted a duplicate lead. As a result, duplicates appear in the statistics—identical leads from the same person.

Such situations occur particularly often when using lead forms. Unlike classic landing pages, where a pixel tracks the transition to the confirmation page, the mechanism for tracking submissions within the social network operates less reliably.

Even if limits on the number of impressions or submissions are set in the ad group settings, the platform often ignores these parameters and continues to show ads to users who have already interacted with the form.

During large-scale campaigns, the problem is exacerbated by account bans. Once an account is blocked, the advertiser loses access to the pixel—the tool that stores data on audience actions. New ad accounts start from scratch and repeatedly show ads to the same users.

As a result, the budget is spent on an audience that is already in the tracker or affiliate system but returns via a different account. In such situations, it is important not only to monitor traffic sources but also to regularly check the entire funnel.

The “Exclude Leads” feature, available in the standard settings, does not fully solve the problem. It works only with users whom the system has already recognized as having completed the target action. To truly clean up the traffic, a more rigorous approach is required—excluding everyone who has already interacted with the funnel.

This method reduces the frequency of impressions to “burned-out” audiences and forces algorithms to seek new users, which directly impacts the cost per lead.

Method 1: Fully Exclude the Audience via “All Website Visitors”

One of the simplest and most effective ways to combat duplicates is to use pixel data to completely exclude all website visitors.

Typically, advertisers limit themselves to excluding only those users who have already converted. However, a stricter approach involves excluding absolutely all visitors.

The logic here is simple: if a person has already clicked on an ad but did not complete the desired action, the likelihood that they will do so upon seeing the ad again is extremely low.

To set this up, go to the Audiences section and create a Custom Audience by selecting the Website source. In the audience settings, instead of specific events, select the “All website visitors” option.

An important point is choosing the correct audience retention period. Depending on traffic volume and geography, this can range from 30 to 180 days. When working with an offer over the long term, it is recommended to set the maximum duration to completely exclude users who have already interacted with the ad.

This approach not only eliminates duplicates but also allows you to reallocate the budget to new audience segments. When configured correctly, this directly impacts conversion rates and traffic processing quality.

However, this method only works until the account is blocked. After a ban, access to the pixel is lost, and alternative filtering methods must be used.

Method 2: Using External Lists (Custom List)

When an advertising account is blocked, pixel data becomes unavailable. To maintain control over the audience, external user lists—Customer Lists—are used.

These lists are generated based on data from trackers, CRM systems, or Telegram bots. This allows you to retain user information and continue removing duplicates even when launching new ad accounts. You can find a selection of the top trackers and services for affiliate marketing by following the link https://affcommunity.org/en/tools/ 

The process works as follows:

1. Data export. User contacts who have already performed a target action are exported from the tracker or CRM. These are usually email addresses and phone numbers.
2. File preparation. The received data is compiled into a CSV file. To upload to Facebook*, a single column labeled “email” or “phone” is sufficient.
3. Uploading the audience. In the Audiences section, a Custom Audience is created with the Customer List as the source, after which the file is uploaded to the system.

If the upload is successful, the platform automatically recognizes the data types and matches them with users. This is visually displayed in the interface, allowing you to verify that the settings are correct.

Using external lists makes it possible to exclude users who are already in the database immediately after launching a new account.

Thus, ads are shown only to a new audience, which reduces the number of duplicates and improves traffic quality.

Conclusion

Filtering duplicate leads allows you to exclude users who have already submitted requests from ad impressions.

Combining pixel data and external lists helps redirect algorithms to a new audience and avoid unnecessary costs. This directly impacts the cost per lead and the overall effectiveness of advertising campaigns.

Technically, lists can be updated without pausing ads or requiring re-moderation.

When accounts are regularly blocked, external data sources become a key tool for preserving audience history and avoiding paying for the same users multiple times.

Almost everyone who works with paid traffic has encountered account bans. Sometimes an account gets banned immediately after launching an ad campaign, and sometimes after a few days of running. To beginners, this may seem like a coincidence, but more often than not, the reasons are quite clear.

Traffic sources now closely monitor what content is being promoted and where the ads lead. This is especially true for major platforms like Google Ads and Meta. Their algorithms analyze not only the ad itself but also the entire chain of events after a click.

If a suspicious signal appears anywhere in this chain, the account may be restricted.

Mismatch between the ad and the landing page

One of the most common reasons for bans is when the ad’s content doesn’t match what the user sees on the website.

For example, an ad promises one service or product, but the website turns out to offer a completely different offer. Ad network algorithms quickly flag such situations.

Sometimes the problem arises even from minor details. The ad looks neutral, but the landing page contains aggressive wording or misleading text. To the system, this looks like an attempt to circumvent the rules.

It’s best to use White pages for launches. If you’re running gray offers, you won’t get anywhere without White pages. Currently, the best service for White pages is Money Safe.

Suspicious Accounts

New accounts are under special scrutiny. If an ad account was created recently and immediately launches a large number of ads, the system may view this as a risk.

This happens especially often when new domains or pages with no history are used. The algorithm detects that the advertiser appeared suddenly and is trying to scale campaigns quickly.

That’s why many teams try to launch ads gradually so the account has time to build up a normal activity history.

Look for verified accounts on the AffCommunity website. The services page lists only verified tools.

User complaints

Even if an ad passes moderation, that doesn’t mean it will run for long. Users can report an ad if they find it misleading or too intrusive.

A large number of complaints lowers the ad’s quality score. After that, ads start performing worse, and sometimes the account gets restricted.

Therefore, creativity with aggressive promises may generate quick clicks, but at the same time increase the risk of being blocked.

Technical issues with the website

Another factor is the technical condition of the website. If a page takes a long time to load, looks suspicious, or contains many redirects, this can cause problems.

Platforms check how safe it is for users to click on ads. If the system deems the site unsafe, ads may be disabled.

Read about how affiliate marketing has changed following the tightening of Google Ads and Meta moderation in 2026 in the article at the link.

Is it possible to completely avoid bans?

It’s nearly impossible to completely rule out bans. Algorithms are constantly updated, and platform rules are getting stricter.

But you can significantly reduce the risk. To do this, it’s important to ensure your ads and landing pages align, avoid running overly aggressive campaigns on new accounts, and carefully review ad copy.

Ads that appear honest and clear to the user usually last much longer.

Conclusion

Most bans in affiliate marketing do not happen by accident. Most often, they result from overly aggressive creativity, unprepared accounts, or problematic landing pages.

If you view ads through the eyes of both the user and the platform, the risk of bans becomes much lower. This doesn’t guarantee perfect results, but it allows you to run campaigns more consistently and keep accounts active for longer.

There are many myths surrounding affiliate marketing that scare beginners and prevent them from starting to earn money. People believe in various horror stories: some think that all opportunities disappeared many years ago, while others fear that without a lot of money or programming skills, nothing will work out. Because of this, many don’t even try to get started.

Myth 1: “Affiliate marketing died in 2012.”

This phrase has been repeated for many years, but it has nothing to do with reality. Of course, it used to be easier to make money, but there are still opportunities today. As long as people use the internet, advertising will be in demand. New areas and niches are emerging, and those who learn to adapt to change continue to earn a good income.

Many such beliefs are formed because of initial mistakes and unsuccessful attempts to get started. Beginners often enter affiliate marketing without understanding the basic principles, which leads to quick losses and the conclusion that the market is “dead.” In practice, the problem is most often not the niche or the timing of entry, but the typical starting mistakes that almost everyone makes. A detailed analysis of such situations can be found in the article What mistakes do beginners make in affiliate marketing.

Myth 2: “You can’t do without special courses.”

Many people sell expensive courses, promising secret methods of earning money. But real knowledge comes only from practice. It is better to learn from your mistakes and analyze real examples. Courses can provide basic knowledge, but they cannot replace real work with advertising campaigns.

Most successful webmasters started with free materials from the internet. They studied other people’s experiences, tried different approaches, and gradually accumulated their knowledge. Paid training only makes sense when a person already understands the basics and can distinguish useful information from empty promises.

Myth 3: “Conferences provide ready-made working schemes.”

Conferences rarely reveal truly effective strategies. Presentations usually contain general information. Truly useful ideas emerge in conversations with colleagues during breaks. The main advantage of conferences is the connections they provide, which can lead to partnerships or collaborations.

Myth 4: “You can’t get started without a big budget.”

You don’t need a huge budget to get started. Many people started with $50-100 and gradually increased their income. What’s more important than the amount of investment is the ability to understand what works and what doesn’t.

Myth 5: “You need to know how to program.”

Programming is not required to work in affiliate marketing. There are now many ready-made solutions: website builders, advertising templates, and ready-made tools from affiliate programs. Even a beginner without technical skills can create a working advertising campaign.

Myth 6: “The only profitable niche is iGaming.”

Although casinos and betting are popular, you can also make money in other areas. Over the years, push notifications, app installations, goods from China, and educational courses have worked well. Success depends not on the choice of niche, but on the ability to understand the audience and create good ads.

Myth 7: “Facebook is the main source of traffic.”

Facebook is a powerful platform, but it is not the only one. Its rules change frequently, which creates difficulties. But there are other options: Google, VK, messengers. Different platforms are suitable for different countries and audiences. It is a mistake to think that nothing will work without Facebook.

Myth 8: “Affiliate marketing is a scam.”

Many people believe that affiliate marketing is nothing but deception and dubious offers. It’s a field dominated by info gypsies. But there are many honest areas: courses, travel, home goods, real estate. It is possible to earn money legally, without breaking the law or damaging your reputation. It all depends on the choice of advertising offers.

This perception is largely formed by a superficial view of the industry and a lack of understanding of how long-term work in affiliate marketing is structured. In reality, affiliate marketing is not a one-time scheme, but a systematic activity where reputation, analytics, and skill development are important. For those who view affiliate marketing not as a temporary experiment, but as a profession, it is useful to understand how to build a career in affiliate marketing and create a sustainable income model.

Myth 9: “Competition leaves no chance.”

There is competition, but it does not close the market to newcomers. Affiliate programs are constantly offering new terms and bonuses. You need to start small, learn, and gain experience. Over time, a newcomer will become an experienced player who will set trends.

Myth 10: “Affiliate marketing is a lottery.”

This is a dangerous misconception. Success in affiliate marketing is rarely accidental. It is based on data analysis, testing different options, and hard work. Although luck sometimes helps, the basis of income is the right strategy and continuous learning. This is a business, not a game of chance.

Myth 11: “Affiliate marketing is difficult for beginners.”

Specialized terms can be intimidating, but in reality, it’s simpler than it seems. Most tools can be mastered in a few evenings with the help of free instructions.

Myth 12: “Affiliate marketing is illegal.”

It is not affiliate marketing itself that is illegal, but tax evasion. If you register your activity correctly (as a self-employed person or individual entrepreneur), you will have no problems with the law. Affiliate marketing is just advertising on the Internet, and it is completely legal when done correctly.

Myth 13: “All affiliate offers are the same.”

All offers require their own approach. The audience, advertising materials, and country all affect the result. What works in one niche may not work in another.

Myth 14: “It is impossible to earn a stable income in affiliate marketing.”

The market is changing, but stability is achieved through a systematic approach: data analysis, testing new ideas, and gradually increasing successful campaigns. Those who are looking for quick money rarely succeed. Those who build a long-term strategy and analyze successful cases receive a stable income.

Myth 15: “Affiliate marketing is quick money.”

This is a serious mistake. Big money in affiliate marketing rarely comes quickly. Real income comes through hard work, lots of experimentation, and learning from your mistakes. Expecting easy money often leads to disappointment.

Reality instead of myths

These myths are just other people’s fears and excuses for inaction. Some people don’t start because they think it’s “too late.” Others blame the market after their first failures. In reality, affiliate marketing continues to evolve and provides opportunities for those who are willing to work and learn.

Real results in affiliate marketing are almost always the result of systematic work, not random luck. Those who stay in the niche for a long time build processes, analyze numbers, and gradually scale successful combinations. This approach allows you not only to earn money, but also to achieve stable results. The factors that really influence success in affiliate marketing are described in detail in a separate article.

The most important step is to start taking action and choose the right vertical. Almost every successful webmaster started with a small budget and basic knowledge.

Just a few years ago, texts generated by artificial intelligence were considered a novelty. Now they have become a common working tool. They are used by individual freelancers, large publishing houses, and entire marketing departments. The algorithm writes quickly, smoothly, without a hint of emotion or fatigue. But this apparent perfection hides a major pitfall.

The crux of the problem is that the results of AI work are increasingly being published without any verification. In practice, the consequences are almost always the same. The material contains inaccuracies or half-truths that sound convincing but fall apart when the facts are examined in detail. The wording becomes unnaturally smooth and predictable. It seems correct, but there is no spark of living thought in it. And then reader trust falls, SEO optimization suffers, and the reputation of the brand or publication suffers.

That is why modern editorial and content departments in the West are already talking about introducing a special stage—AI review—a return to the fundamental rule of journalism: any material, regardless of who created it, human or machine, must be reviewed before publication.

At this stage, many editors make a critical mistake — they limit themselves to a single review tool and consider the task complete. But the AI market is developing too quickly, and recognition methods are also evolving. To understand what signs really give away machine-generated text and what search engines and editorial offices are paying attention to today, it is important to be familiar with the current analysis tools and their limitations. A detailed analysis of such solutions allows you to build a more reliable AI review and not rely on blind automation.

Detectors are not judges, but signal flags

When it comes to checking text from a neural network, many people’s first idea is: “Let’s run it through a detector, and everything will become clear.” The desire is understandable, but this is often where mistakes begin.

All reputable sources agree on one thing: detectors do not give “yes” or “no” verdicts. They only calculate probabilities, look for patterns, and work with statistics.

So it is a mistake to perceive such a tool as a judge. Its real role is to be a filter or a red flag. It will not answer whether the text can be published. It will point out areas that require special attention from a live editor.

Experts strongly advise against trusting a single service. One detector may show a low percentage of “artificiality,” while another may show an off-the-charts percentage. This does not mean that someone is lying. It simply means that their analysis algorithms are different. Practitioners recommend checking the text in two or three systems and looking not at bare percentages, but at consistent patterns.

The main value of such a check is not even the final figure, but the paragraphs that different services consistently mark as problematic. This is where the typical weaknesses of machine-generated text usually lie: overly polished wording, clichéd phrases, and bare generalizations without details. For an editor, this is not a death sentence for the material, but a clear signal that manual work is needed here.

In the AI-review checklist, detectors play an important but auxiliary role. They cannot replace humans or make decisions for them. Their task is to help quickly find areas where “just normal” text can be made truly high-quality — human, accurate, and suitable for publication.

Where the algorithm most often “gives itself away”

If we distract ourselves from detectors, AI most often gives itself away not with its vocabulary, but with the structure of the text. Observations by leading experts confirm this: neural networks write with unnatural accuracy. And this exaggerated correctness begins to hurt the eyes.

A typical AI text looks as if it has already been edited before it was written. The paragraphs are almost identical in length. Thoughts flow smoothly, without the slightest pause or hesitation. Everything is logical, consistent, and… lifeless.

Another characteristic feature is universal clichés that can be inserted into an article on any topic. Phrases such as “It is important to note that…” or “Thus, we can conclude…” are not a sin in themselves. But when they wander from text to text without carrying any unique meaning, it is almost a sure sign of machine origin.

For the reader, such predictability kills interest. For the editor, it is a clear warning sign: the text is too smooth, too correct, and too safe.

Therefore, the next checkpoint is a targeted search for this very “perfection.” Are the paragraphs too symmetrical? Can the formal introductions and conclusions be cleaned up or rewritten “for the sake of form”? Are there lively connections, clarifications, and unique details in the text, rather than generalities?

The editor’s job here often boils down to simple things: cutting out template constructions, breaking the monotonous rhythm of paragraphs, allowing the text to breathe and be uneven. In real journalism, thoughts are rarely arranged in a flawless parade. It is this slight chaos that makes the material lively and convincing.

How to work with facts and statements

While the style and structure of AI text can still be dealt with through editing, the factual side of things is more serious. This is where neural networks make mistakes most often, and the consequences of these mistakes are the most serious.

AI can speak with frightening confidence. It can present a dubious or simply fabricated statement in such a way that it sounds like an irrefutable truth. This is especially true for numbers, historical dates, and logical connections between events. The algorithm can round off statistics, mix data from different studies, or “think up” a cause-and-effect relationship that has not been proven.

The result is a paradox: the text sounds convincing, but is far from the truth. And this is the most dangerous part. The reader will not suspect anything until they check the information themselves. And for the author, media outlet, or brand, such an oversight can become a reputational disaster.

That is why SEO specialists insist on one simple rule: any fact in a text generated by AI is considered unverified by default. In other words, manual verification of all specific data in the text is vitally important. Search for original research, official reports, and authoritative publications. And if the source cannot be found, the editor must do the only honest thing: either soften the wording by adding “possibly” or “according to some data,” or remove the controversial point from the text altogether.

How SEO optimization works

AI is often presented as the ideal SEO assistant. It knows everything about keywords, can build structure, and instantly generates texts for queries. But without editing, the algorithm can easily turn from an assistant into a source of problems for search engine optimization.

The most common problem is oversaturation. AI tries to please and generously sprinkles keywords throughout the text. The result is material that search engines can evaluate, but which is unbearable to read. The second problem is formal headlines. They contain the right words, but they are not catchy and have no value. The third is ignoring the user’s true intention. The text seems to respond to the query, but does not provide a specific, useful answer.

Here it is important to remember the principle that experts insist on. Google and other systems have long been evaluating not technical perfection, but quality, usefulness, and compliance with the expectations of a living person. If the text does not solve the reader’s problem, no optimization will save it.

Therefore, SEO verification as part of an AI review is about meaning. In the checklist, it looks like this:

1. Review the headlines: are they catchy and useful, or do they just contain keywords?
2. Ask yourself: does the material really answer the question that the user came with?
3. Ruthlessly clean out SEO fluff, meaningless repetitions, and phrases that exist only for robots.

At this stage, the editor must put themselves in the reader’s shoes. Would they themselves answer their own question after reading this text? Would they want to read to the end? If there is even a shadow of doubt, then the algorithm has only done half the job, and there is still work to be done.

Where does the responsibility lie?

Artificial intelligence is not the enemy. The threat lies elsewhere—in the disappearance of a living person between the moment the text is generated and its publication.

Neural networks help overcome the fear of a blank page, sketch out a structure, and find the first formulations. But they do not and cannot bear responsibility for the truthfulness, depth, and consequences of the published word. This responsibility has been and remains with authors, editors, companies, and publishers.

Many people perceive neural networks exclusively as text generators, but in practice, AI has long been used much more widely — from analytics to creativity and content scaling. Understanding real-world scenarios for using neural networks allows you to build processes so that algorithms enhance the result rather than creating hidden risks for reputation and SEO.