Bot Traffic in eCommerce: how can you fight the plague?

For a long time, when people spoke about bots in the digital context, the most common image was that of relatively simple crawlers, focused on indexing, basic monitoring or elementary scraping. That scenario has changed significantly. With the acceleration of artificial intelligence, bots have started to behave in a more sophisticated, more persistent way and, in many cases, in ways that are much harder to distinguish from a real human visit. They no longer limit themselves to requesting pages in a linear way. They simulate sessions, move through categories, load products, visit parameterised URLs, trigger events and reproduce behaviours that, at first glance, may appear legitimate.

In e-commerce, this evolution has become especially problematic. Platforms such as online stores built on Shopify or WooCommerce have started receiving increasingly intense volumes of non-human traffic, often driven by advertising tools, competitive intelligence systems, recommendation engines, platforms with AI features and automated ecosystems that crawl websites very aggressively. Instead of a few hundred suspicious visits, many brands have started seeing thousands — and, in some cases, tens of thousands — of sessions per day coming from automated behaviour.

The problem is not only the volume. Above all, it is the analytical impact. When these accesses enter the core reporting of an online store, they begin to distort essential metrics for marketing and e-commerce teams. The most sensitive one is often the conversion rate. If the denominator grows artificially because of sessions that never had any real purchase intent, the store’s apparent performance deteriorates, even when the commercial operation remains strong. The brand starts to look less efficient when, in reality, it may simply be a victim of traffic noise.

At BYDAS, a digital marketing and e-commerce agency based in Porto, we have been observing this reality with increasing frequency. In projects built on Shopify and also on WooCommerce, we regularly see situations where automated traffic interferes with performance analysis, creates false alarms, raises doubts about apparent efficiency drops and forces marketing teams to revisit diagnoses that, without technical context, may seem logical but are actually wrong. Today, fighting bot traffic is no longer a peripheral concern. It has become part of serious data management in e-commerce.

Why bots have changed so much with artificial intelligence

Today’s bots do not operate like traditional crawlers that simply read HTML or tested predictable endpoints. Many now benefit from more advanced models for navigation, page structure interpretation and behaviour simulation. This means they can open more complete sessions, move between collection and product pages, interact with scripts, trigger events and imitate patterns that make them harder to exclude immediately. In some contexts, their behaviour becomes so close to that of a real visit that the boundary between human and non-human traffic is no longer obvious in a superficial reading.

This sophistication has increased for several reasons. On the one hand, the digital economy itself has become more dependent on automation. On the other, various AI-powered tools need to collect data, observe website structures, interpret catalogues, monitor pricing, analyse product pages and understand commercial signals. The result is a more aggressive ecosystem of technical, automated and semi-automated visits that puts pressure, above all, on websites with rich commercial and informational value.

Why Shopify and WooCommerce feel this problem more strongly

E-commerce platforms are especially attractive for this kind of crawling because they concentrate highly useful information: catalogues, pricing, variants, availability, product content, promotions, taxonomies, filters, collection navigation and conversion structure. All of this is valuable for advertising engines, competitive analysis systems, AI tools and machine learning technologies that need to observe real websites in order to feed models and optimisations.

In Shopify and WooCommerce, this pressure can become especially intense because many integrations related to paid media, optimisation, recommendation and analytics depend on continuous reading of the store. In theory, part of this behaviour is legitimate and even useful. The problem emerges when the volume spikes and when the boundary between an acceptable technical bot and traffic that distorts metrics is no longer under control. The store keeps selling, but the numbers begin telling a degraded story about its performance.

What gets damaged in e-commerce metrics

Conversion rate is the most visible example, but it is not the only one. When a store receives an abnormal volume of automated sessions, the reporting for users, sessions, page views, average session duration, navigation depth and even certain behaviour indicators can become contaminated. In some cases, the team starts interpreting what is actually an invasion of technical traffic as a drop in user interest. In other situations, the operation appears to be losing efficiency at the top of the funnel when, in practice, the problem lies in the artificial increase of the statistical denominator.

That is why this topic has become so relevant for marketers. Poor data interpretation leads to poor decisions. It can lead to wrong campaign changes, unnecessary creative adjustments, unjustified checkout revisions, negative interpretations of pricing or value proposition, and rushed conclusions about performance. Before trying to fix the store, it is necessary to make sure that the store’s data is describing human beings and not merely automated traffic moving through pages.

Why Meta Ads and Google Ads can mitigate part of the problem

There is, however, an important nuance. When an online store is well configured at the integration level with Meta Ads, Google Ads and Google Analytics, part of the impact can be mitigated. These platforms work with their own mechanisms for deduplication, filtering and invalid traffic exclusion, which helps reduce the influence of bots in performance views focused on paid media and attribution. This does not mean the problem disappears, but it does mean that certain performance views can remain cleaner than the native reports of the e-commerce platform itself.

This difference matters because it explains a very common scenario: the platform’s conversion rate appears to worsen, but campaign and analytics indicators do not show an equivalent degradation. When that happens, it is worth suspecting non-human traffic before concluding that the business has lost efficiency. A well-built tracking architecture does not remove bots from the ecosystem, but it does prevent part of them from completely destroying strategic analysis.

Blocking bots brings benefits, but also risks

Faced with this scenario, many companies quickly arrive at the same conclusion: these accesses need to be blocked, limited or excluded. And in many cases, that conclusion is correct. Regulating suspicious traffic can restore report quality, recover a more accurate reading of conversion rate, clean up internal analysis and reduce noise in marketing decisions. The problem is that not every block is neutral. When action is taken without criteria, there is a risk of blocking legitimate advertising tools, analytics platforms or automated systems that are part of the healthy functioning of the digital ecosystem.

This is where technical mitigation requires maturity. The objective should not be to “kill all bots”, but to separate what is harmful, what is irrelevant for analysis and what remains necessary for campaigns, measurement or integration. In some cases, excluding everything that looks suspicious can create unwanted side effects, such as failures in advertising tracking, issues in platform diagnostics and loss of useful signals for acquisition systems.

Geographic origin is often an important clue

In practice, one of the most useful ways of identifying problematic traffic is still its geographic origin. In many cases, abnormal spikes come from regions where traffic is cheaper or where there is a high concentration of automated activity, such as China, Bangladesh or Pakistan. When a brand with no real operation in those markets starts receiving disproportionate volumes from those locations, there is a strong probability that it is facing non-human traffic or traffic that is technically irrelevant for the business.

In those scenarios, using marketplace apps, exclusion rules or developing blocking scripts at backend or frontend level can be an effective response. However, not every country offers the same clarity for decision-making. When suspicious traffic also comes from Ireland or the Netherlands, for example, the problem becomes more delicate. Those geographies may concentrate technical infrastructure, proxies, distribution networks and automated systems, but they may also contain legitimate traffic. Blocking by country, in those cases, may mean eliminating potential real visits.

The specific case we faced in Shopify

At BYDAS, we had a particularly striking case involving a well-known brand. In December 2025, the store started being bombarded with traffic originating in China. The volume was so intense that it reached around 80 thousand sessions, with direct impact on performance reporting. 

The most visible result was an 11% reduction in conversion rate, without any strong enough commercial explanation to justify that drop on its own.

The problem did not stop there. In January 2026, the scenario worsened and the marketing team asked for our opinion. 

Our diagnosis was clear: we were probably dealing with agentic e-commerce bots, in other words, more advanced bots guided by AI logic and capable of producing fuller, less obvious sessions than traditional automated traffic. As this was a regular client, we decided to act. We started looking for solutions in mid-January, but the first attempts did not produce the desired effect.

Because the store was built on Shopify and there was no access to the backend, we had to look for mitigation paths from the frontend and intermediate control layers. It was a demanding technical process, involving iteration, testing and refinement. In February 2026, the first encouraging signs began to appear: we managed to reduce traffic coming from China by 93%. Even so, the real validation would have to come in the following month, with greater stability.

In March 2026, the proof that mattered arrived. After we had practically eliminated all traffic from suspicious locations, the store’s conversion rate increased by 30%. This did not mean that the store had suddenly become “better” from a commercial perspective overnight. It meant, above all, that reporting had moved closer again to the human reality of the operation. Instead of technical noise crushing apparent performance, the brand could once again see usable numbers on which to make decisions.

When accumulated knowledge becomes a cross-project advantage

This kind of intervention did not remain isolated. The experience we gained helped us in other projects, including the case of a furniture brand in which, after our mitigation work, the store’s conversion rate increased by 91%. 

The client initially identified and reported an excessive amount of traffic coming from China.

With experience already gained from other projects, we quickly started trying to mitigate the issue using our expertise, which resulted in the total mitigation of that type of traffic in the following month’s reporting.

Finally, we were able to confirm the total mitigation of the problem, with the client seeing its conversion rate rise again.

Once again, the central point was not to “manufacture” performance, but to remove relevant distortions from measurement. When traffic is no longer poisoned by non-human sessions, the ability to read performance improves and commercial management gains a more solid foundation.

That is precisely why this topic should not be treated as a technical curiosity. Bot traffic in e-commerce already influences budget decisions, campaign assessment, UX diagnostics, funnel analysis and the overall perception of store efficiency. If the analytical base is contaminated, even experienced teams may end up optimising the wrong problem.

How to fight the plague without destroying what works

Fighting this plague requires method. The first step is to diagnose correctly: understand where the traffic comes from, what behaviour it shows, which pages it visits, how it interferes with metrics and to what extent it is, or is not, entering media and analytics reporting. The second step is deciding the right mitigation layer: exclusion in reports, rules in apps, scripts, location-based blocking, event filtering or a more technical intervention in how the store responds to certain access patterns.

The third step, often neglected, is to validate the impact without compromising legitimate campaigns and integrations. Not all automated traffic is harmful and not every exclusion is smart. In e-commerce, maturity lies in balancing analytical protection with operational continuity. A useful solution is not the most radical one; it is the one that improves visibility without sabotaging the store’s marketing ecosystem.

The future of this problem will be even more demanding

Everything suggests that the pressure will continue. As AI becomes integrated into more products, platforms and advertising tools, e-commerce websites will continue to be targeted by automatic sessions that are more sophisticated, more intense and harder to interpret. That means marketing, e-commerce and technology teams will also need to evolve how they read traffic, diagnose noise and decide when to intervene.

Today, fighting bots is no longer just an infrastructure issue. It is a matter of analytical quality, strategic clarity and protecting decision-making capacity. A store may be selling well and, at the same time, appearing worse than it really is because its reports have been contaminated by non-human traffic. Identifying that deviation early can prevent weeks or months of wrong decisions.

At BYDAS, we help brands protect the analytical quality of their e-commerce operations, with technical and strategic intervention in cases of suspicious traffic, including Shopify integrations and mitigation of distorted reporting. When the problem lies in the data rather than in real demand, acting well can restore clarity to the business.