AI-supported dynamic pricing is changing e-commerce: more agility, more risk. Anomaly detection is becoming the key to stable systems.
Booking a flight ticket? There have always been countless tips circulating online about when the ideal time to book is – Tuesday morning, six weeks before departure, never after 6 p.m.
Is that now a thing of the past? According to its own statements, Delta Air Lines, for example, has long been working with AI-supported dynamic pricing systems that continuously adjust prices based on demand, capacity utilisation, time of day, weather or regional booking behaviour.
Simple ‘hacks’ will probably no longer be of any use to travellers.
The new AI wave: revenue opportunities through AI-supported dynamic pricing
The same applies to the entire e-commerce industry: automated pricing decisions, personalised recommendations and dynamic discounts are currently becoming the new standard.
The extent to which AI is actually involved – whether through machine learning models, real-time algorithms or rule-based systems – remains partly a trade secret in order to secure a competitive advantage.
However, one thing is clear: numerous leading retailers and platforms are already using AI-supported pricing and recommendation logic, often initially in pilot phases that are gradually being expanded.
Study shows increase in sales through dynamic pricing with AI
In a recent pilot study by Poznań University of Technology, for example, various machine learning algorithms (including Naive Bayes, Support Vector Machines and Decision Trees) were applied to real e-commerce data in order to automatically optimise pricing decisions.
The study showed that AI models can predict price changes much more accurately and adapt to market behaviour than static rules – the SVM algorithm achieved an accuracy of around 87 per cent.
The researchers concluded that machine learning makes dynamic pricing more agile and market-sensitive.
The catch: continuous data monitoring remains necessary to avoid undesirable developments (Nowak & Pawłowska-Nowak, 2024, Applied Sciences, 14(24), 11668).
The study thus points to a new problem: AI-supported dynamic pricing not only increases sales potential, but also the vulnerability of the systems.
If the AI logic reacts incorrectly, incorrect prices are set – and intelligent pricing quickly becomes a financial risk.
The risk of error in AI-supported dynamic pricing
If an AI engine is not implemented or monitored properly, it can set incorrect prices or make inappropriate recommendations.
For example, a product may be marked as ‘on sale’ but receive prominent recommendation postings – even though there is hardly any stock left.
Or dynamic pricing logic may override the price beyond an acceptable level because data quality was limited or segmentation was incorrect.
This is where the key question comes in: who notices when the AI logic backfires?
The domino effect of incorrect AI recommendations
Incorrect recommendations or inappropriate pricing strategies can trigger chain reactions:
- Customers receive product suggestions that do not match their purchasing behaviour.
- Excessive prices lead to abandoned purchases or negative reviews.
- Low prices in the wrong segments jeopardise margins and brand trust.
Such errors are often difficult to detect because the shop's interface functions smoothly – the ‘wrong decision’ happens in the background. This is precisely where the need for data-based monitoring systems arises.
AI-supported dynamic pricing requires anomaly detection
A sustainable strategy in e-commerce means not only automation, but also monitoring of automation.
Anomaly detection systems act as a safety net here: they monitor key figures when values suddenly deviate from normal behaviour.
Such deviations can be an indication of incorrect pricing decisions or inappropriate recommendations by the AI.
Anomaly detection makes these patterns visible before they develop into real sales losses – thus protecting not only against bugs, but also against misguided AI logic.
Conclusion: How to protect yourself against AI errors in dynamic pricing
AI-supported systems for recommendations and price optimisation are no longer a thing of the future – they are currently being implemented and tested everywhere and have become part of everyday e-commerce.
At the same time, they harbour risks: faulty AI logic can distort prices, devalue recommendations and weaken customer relationships.
In our view, the solution is not to avoid AI, but to secure it with anomaly detection. This makes the system not only ‘functional’, but also controllable and secure.
#ecommerce #earlywarning system #dynamicpricing #anomalydetection
