The AI Revolution in Ultra-Low-Cost E-Commerce

The AI Revolution in Ultra-Low-Cost E-Commerce: How Artificial Intelligence is Reshaping Online Shopping

Explosive Market Growth of Ultra-Low-Cost Online Platforms

The digital commerce landscape has witnessed an unprecedented transformation as ultra-low-cost platforms like Shein, Temu and AliExpress have rapidly shifted from niche players to dominant forces in online retail. In just a few years, they have evolved from being curious newcomers to becoming one of the main engines of e-commerce growth, especially in markets such as France and across Europe.

One striking finding is the surge in everyday use: between the first quarters of 2022 and 2023, the number of payment cards showing at least one monthly purchase on a discount site jumped by 20%. This is not a marginal trend. It means millions of people are now building these platforms into their regular shopping habits, not just as occasional bargain-hunting stops.

This behavioural shift manifests in physical logistics infrastructure as well. By mid-2025, low-cost platforms were already responsible for 22% of all parcels handled by the French postal service. Five years earlier, they represented only 5%. In other words, in half a decade they multiplied their share of parcel traffic by more than four, turning postal systems into pipelines for ultra-cheap, high-volume online shopping.

This growth trajectory shows no signs of deceleration. The sector is projected to expand by another 6.5% in 2025 alone. This momentum connects partly to recent high inflation: as everyday life becomes more expensive, consumers become more open to offers that promise "everything for less". But inflation is only the visible tip of the iceberg.

Beneath the surface, these platforms rely on artificial intelligence at the core of their business model. AI helps them understand what people want, often before the customers themselves realise it. By analysing browsing habits, clicks, and purchases, the platforms can identify who is most likely to buy, then surround those users with perfectly timed offers and hyper-targeted recommendations. This AI-driven precision turns casual visits into regular spending, and regular spending into explosive market growth.

AI-Driven Behavioural Profiling and Predictive Recommendations

The result is a powerful engine of expansion: millions of personalised online storefronts, each tuned to a specific person's tastes, budget and impulses. These platforms are not just cheaper versions of traditional online shops; they are data-driven machines, constantly learning how to make buying as easy, tempting and frequent as possible. In market terms, that combination of ultra-low prices, AI-powered targeting and massive logistics scale is exactly what is pushing their share of online commerce ever higher.

Platforms like Shein and Temu are not just online stores – they are powerful AI machines that learn, predict and gently nudge users into buying more than they ever planned. At the core of this model is AI-driven behavioural profiling and predictive recommendation systems that quietly reshape how people shop.

From Browsing to Being Profiled in Real Time

Each click, scroll and pause on a product is captured and analysed. These platforms use AI tools to build detailed profiles of every user, tracking their browsing patterns, purchase history, time spent on different categories, and even mouse movements and scroll speeds. This data creates comprehensive user profiles that predict shopping behaviour with remarkable accuracy.

This process, called behavioural profiling, treats each visitor uniquely. Instead of showing everyone the same content, the AI separates them into invisible categories and adjusts what each person sees. The goal is simple and powerful: find the users most likely to purchase and show them exactly what will tip them over the edge.

Recommendation Engines That Create Desire Before It Exists

Predictive algorithms go beyond simple "you may also like this" suggestions. These systems analyse vast datasets to identify patterns in consumer behaviour, predict what items a user might want next, and create artificial scarcity through limited-time offers. They constantly test different product combinations and monitor user responses to optimise future recommendations.

The effect is that the platform does not just respond to a shopper's needs – it creates them. By playing on scarcity ("only a few left!") and urgency ("offer ends soon"), it taps directly into the user's fear of missing out. The system is always adjusting, always optimising, so that the next recommendation feels almost uncannily well-timed.

Even familiar tools like "items also viewed" are supercharged. These sections are not static lists. They are live experiments where AI constantly rotates content, measures reactions and then uses the resulting data to push even more products the user never intended to look for.

Hyper-Personalised Stores and Dynamic Pricing Strategies

One of the most striking aspects is that there is no single version of a platform like Shein or Temu. Thanks to AI, every user effectively walks into their own personalised store. The systems collect and connect huge volumes of data including demographic information, purchase history, browsing patterns, time of day preferences, and even device usage patterns.

This data transforms into a tailor-made storefront. Two people looking at the same app at the same time will not see the same products, discounts or suggestions. The layout, the order of items and even the types of promotions can all be adjusted by AI to match each user's profile.

This hyper-personalisation radically increases the odds of impulse buying. When nearly every item shown has been picked because the system believes it might trigger a purchase, users are not just browsing a catalog – they are walking through a carefully engineered sales funnel that adapts to them in real time.

Gamification and Psychological Triggers

These platforms transform online shopping into something that feels less like browsing a store and more like playing a game – a game designed to keep people spending. They use gamification techniques borrowed from casinos and mobile games to hold users' attention and nudge them toward more purchases.

On Temu, for example, users encounter spinning wheels for discounts, countdown timers creating urgency, pop-up notifications about "limited offers", and progress bars showing how close they are to free shipping. These constant visual and emotional stimuli mimic the excitement of gambling, creating cycles of anticipation and quick satisfaction hits.

The platforms incorporate full mini-games directly into their apps, with names like Farmland and Fishland. These promise users free items and discount coupons for continued engagement. To maintain user retention, they employ daily check-in bonuses, achievement badges for purchases, and social sharing rewards.

AI-Powered Supply Chain and Market Prediction

AI influence extends beyond customer-facing interfaces to supply chain management and product development. Shein has built proprietary AI tools that scan the broader internet, monitoring search trends, social media posts, influencer content, and competitor websites. These systems track emerging colours, spreading patterns, popular cuts and acceptable price points in real-time.

This information feeds directly to suppliers and manufacturers, enabling production in very small initial batches – often 100 pieces or fewer per new item. This micro-production strategy dramatically reduces risk: failed items result in minimal losses, while successful products can be quickly scaled up based on real demand signals.

Dynamic pricing algorithms represent another AI application, changing prices constantly based on demand, user behaviour and market trends. Instead of fixed pricing, users encounter flash sales creating urgency, personalised discounts based on browsing history, and prices that fluctuate based on demand patterns.

AI serves as the invisible orchestrator behind both playful interfaces and dynamic pricing. Every user interaction becomes data feeding the next algorithmic decision: adjusting game elements for individual users, timing discount offers for maximum impact, and changing product recommendations based on real-time behaviour.

Regulatory Challenges and Transparency Concerns

This AI-driven transformation raises significant concerns about system opacity and user manipulation. The recommendation engines and dynamic pricing tools continuously test, learn and adapt based on user reactions, yet customers have minimal insight into how their data is used, what profiles are created, or why specific offers appear.

Behind the entertaining games, spinning wheels and flash deals operates a complex AI engine nudging people towards unplanned purchases. Ethical concerns include intensive data collection without clear user understanding, psychological manipulation through artificial urgency, and opaque algorithmic decision-making affecting user choices.

These practices become particularly troubling when combined with limited transparency. Users often remain unaware of how extensively they are profiled, how precisely they are targeted, or how systematically the experience is designed to overcome their self-control.

Regulatory responses are emerging across Europe. The Digital Services Act requires transparency in recommendation systems and gives users rights to understand algorithmic decisions. The AI Act establishes rules for high-risk AI applications, including those used in consumer-facing systems. Together, these frameworks challenge business models built on secretive recommendation systems and hyper-personalised manipulation.

Future Implications and Open Questions

Critical questions remain about implementation and effectiveness. How will regulators scrutinise opaque recommendation algorithms that subtly influence user behaviour? What auditing tools and compliance measures will ensure genuine protection rather than superficial changes? How can anyone measure whether users are making more informed choices instead of being guided by invisible psychological mechanisms?

The transformation extends beyond individual shopping experiences to broader market dynamics. AI-enabled hyper-personalisation increases conversion rates and builds customer loyalty by making platforms feel intuitive and responsive. Simultaneously, AI-driven production ensures optimal product availability with minimal waste, creating self-reinforcing systems that encourage frequent user engagement.

This represents more than a story about inexpensive products or bargain shopping. It constitutes a live experiment in AI's capacity to shape human behaviour and regulators' ability to maintain oversight and transparency. The entire business model rests on data collection and automation: personalised interfaces for individual users supported by ultra-responsive, AI-guided supply chains.

As these platforms continue expanding their market share and refining their AI capabilities, the balance between innovation, consumer protection and market fairness will require ongoing attention from regulators, consumers and industry stakeholders. The ultimate outcome will likely determine how AI integration proceeds across digital commerce and what precedents emerge for algorithmic transparency in consumer-facing applications.

AI and online shopping: how Shein, Temu and others get you hooked

The success of e-commerce platforms Temu and Shein is based on highly effective marketing tools. Artificial intelligence has become a major lever for building customer loyalty.

The ConversationGhassan Paul Yacoub