You’ve probably heard the doom and gloom predictions. Third-party cookies are disappearing. Affiliates are using AI for everything. Regulations keep getting stricter. Any one of these changes should have hurt the industry.
Instead, affiliate marketing in the U.S. hit $13.6 billion last year. It’s growing faster than e-commerce overall. So what’s actually happening? Why is the channel thriving when all the old methods are falling apart?
Cookies dead in the water for millions of users
The controversy over cookies has been going back and forth for quite a while now. For instance, Google announced plans to phase out third-party cookies in Chrome, then repeatedly pushed back the timeline, and finally reversed course entirely in April 2025. Safari and Firefox, on the other hand, have started blocking cookies years ago – the former in 2017, and the latter in 2019.
More specifically, Safari outright blocks third-party cookies via its Intelligent Tracking Prevention feature, while Firefox keeps them walled off website-by-website to prevent tracking across the web. Combined, these browsers represent around 19% of web traffic globally – and that’s not accounting for users who block cookies manually or run ad blockers in their browsers.
In practice, this leads to inconsistent tracking. Some conversions get attributed correctly, others vanish into thin air, and some show under the wrong affiliate – depending on which browser your customer uses and what privacy settings they have enabled.
Alternatives to cookie-based tracking
The silver lining here is that cookies are not the be-all and end-all of tracking. Here’s a handful of approaches that successful brands use – and that you might want to adopt for your own business:
- Server-side tracking shifts data collection from the user’s browser to the company’s own servers. By making it harder for ad blockers or browser privacy features to interfere, this approach ensures more consistent tracking across devices. Its only substantial downside is that it requires more technical setup than traditional methods.
- First-party data collection involves gathering data directly from users through account registrations, purchase history, and email subscriptions. Basically, whenever users provide information voluntarily, companies are able to track them across sessions regardless of their cookie settings.
- Unique promotional codes assigned to specific affiliates work just fine no matter what users are doing with their browser settings. In fact, many of the more successful programmes use affiliate-specific discount codes as their primary attribution method because they’re straightforward – and reliable to boot.
- Multi-touch attribution platforms record the full journey a user takes before converting, and give credit to everyone who helped make the sale happen. This is different from traditional affiliate programmes that give 100% of the credit to whoever sent the final click before a purchase – ignoring the fact that customers typically see multiple recommendations (articles, video reviews, what have you) before deciding to buy.
To be sure, these solutions come with a price tag, but the sooner you can make the upgrade, the stronger your affiliate relationships will be, with partners feeling secure in the knowledge they’ll receive due credit for their achievements.
What AI actually does in affiliate programmes
Recent findings of the Performance Marketing Association (PMA) indicate that a staggering (albeit unsurprising) 97% of affiliate marketers now use AI tools, with common applications being content creation (90%), optimisation strategies (67%), and affiliate recruitment (47%).
This, of course, doesn’t mean that affiliate managers have become obsolete – it’s just that now, with more sophisticated and easily accessible AI, they’re able to find the right partners more quickly. Until recently, this process relied entirely on individual judgement. With AI, however, recruiters can analyse thousands of candidates and identify the best performing ones faster than ever before. In some cases, they can even predict how well prospective hires are likely to do in the future based on historical data.
Arguably, this is felt most acutely when it comes to production. As the study by PMA referred to above has found, the use of AI-generated content jumped from 31% to 90% in just 18 months. What this reflects is that successful marketers now typically use AI for first drafts and research, with human input reserved for assuring quality and accurate communication of brand voice.
Understanding generative engine optimisation
With the advent of Large Language Models (LLMs), it didn’t take long for search behaviour to change accordingly. Instead of asking Google and then clicking through links, people now get the answers they need through conversational summaries provided by platforms like ChatGPT and Perplexity. According to Bain & Company, roughly 60% of searches now end without the user progressing to another destination.
Called “zero-click search”, this phenomenon poses a significant challenge for affiliate marketing. Research from Bain & Company found that approximately 80% of consumers now rely on zero-click results in at least 40% of their searches, contributing to an estimated 15-25% reduction in organic web traffic. When AI platforms like ChatGPT or Perplexity generate answers that reference products, users may never click through to the affiliate links that drive revenue.
To address this shift, digital marketers have begun exploring Generative Engine Optimisation (GEO) – a practice formalised by Princeton University researchers in 2023. The goal is to increase the likelihood that AI systems will cite your content as a source when answering user queries, even if users don’t click through to your site.
According to the Princeton study, which tested nine optimization methods across 10,000 queries, several strategies proved effective at improving visibility in AI-generated responses:
- Content that answers specific questions with clear, direct information – not just general overviews. The research found that answer-first formats (40-75 word direct responses) significantly improved citation likelihood.
- Structured formats that algorithms can easily parse – the study confirmed that comparison tables, step-by-step guides, and Q&A sections improve what researchers call “extractability” – how easily AI can pull information from your content.
- Detailed content with expertise signals – adding statistics increased visibility by up to 115% in the Princeton study, while including citations to authoritative sources boosted performance by an average of 31.4% when combined with other methods. Content demonstrating Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) consistently performed better.
When AI platforms cite your brand while answering questions like “What’s the best product category?”, your visibility increases even without direct clicks. Research from First Page Sage and other GEO agencies shows that brands appearing in AI-generated responses benefit from increased brand recognition and trust – though the impact varies significantly by industry and query type.
It’s worth noting that the effectiveness of these strategies depends heavily on content domain, with the Princeton study finding that different approaches work better for historical content, factual queries, or technical topics. Early adopters in content marketing and SaaS have reported measurable improvements, though this remains an evolving field as AI platforms continue to refine their algorithms.
How B2B and SaaS programmes adapt
B2B affiliate marketing and SaaS affiliate marketing face distinct challenges because their sales cycles differ from consumer purchases. Let’s take a brief look at each in turn.
B2B programs deal with longer consideration periods and more thorough research. The shift toward first-party data collection actually works in their favor – business buyers willingly provide information when they see value. This makes it easier to track journeys across weeks using logged-in sessions rather than cookies.
SaaS affiliate programmes, on the other hand, benefit from subscription-based models where initial attribution matters most. Once you’ve fairly credited the affiliate who brought in a customer, the recurring revenue makes partnership economics work well for both parties.
Both B2B and SaaS programmes need to reconsider their content approach. Technical audiences want in-depth analysis, not surface-level promotion. AI-generated content performs better when it provides genuine insights, detailed comparisons, and answers to specific technical questions.
Practical changes brands should make
- Upgrade tracking infrastructure now. Moving to server-side tracking and implementing robust first-party data collection should happen now, not when you start seeing serious measurement gaps.
- Integrate AI thoughtfully. AI works best handling research, drafting, data analysis, and optimisation while actual humans focus on strategy, quality control, and relationship building.
- Invest in content that earns AI citations. Generic promotional content doesn’t get referenced by AI systems. Detailed, authoritative content that genuinely helps people does. Work with affiliates who create this kind of value.
- Prioritise adaptable affiliate partners. The affiliates who understand how discovery is changing, who create content AI systems reference, and who build audiences across multiple channels are driving growth.
- Maintain transparent attribution. When tracking methods are changing, affiliates worry about fair credit. Clear reporting, competitive commission structures, and proactive communication builds trust that keeps top performers engaged.
Looking ahead
The affiliate marketing industry reached $18.5 billion in 2024 and keeps growing. But if you’re running an affiliate programme, you know the real story: everything changed in the last few years.
Privacy regulations killed third-party cookies. AI tools automated tasks that used to take hours. Google algorithm updates hammered affiliate sites. In the US, spending jumped from $9.56 billion in 2023 to nearly $12 billion in 2025 – a 25% increase in just two years.
The programmes that work now updated their tracking, use AI for tasks that actually make sense (fraud detection, content optimisation), and wrote clear contracts with partners. The ones struggling are still using methods from 2022.
Privacy regulations and AI didn’t kill affiliate marketing. 81% of brands still run affiliate programmes. What changed was that you can’t rely on third-party cookies and manual processes anymore.
If your affiliate programme isn’t performing, it’s probably not because affiliate marketing doesn’t work. It’s because your programme hasn’t kept up with how it works now.
