A designer handbag that normally costs around 200 Euro – for 0 Euro? That's exactly what happened in autumn 2021.
The background: Fashion house Marc Jacobs experienced a classic pricing glitch. Due to a website error, handbags were suddenly offered with a 100 per cent discount at checkout – meaning they were free of charge (source: Vogue Business).
In an email, the brand had to ‘backtrack’ and inform its customers that it was a pricing error.
This case impressively demonstrates that if automated pricing logic is not properly monitored, even a luxury label can risk significant sales.
At the same time, rule-based pricing logic is now standard in modern e-commerce.
Automated pricing logic: flexible – but prone to errors
Whether Shopify, Magento, Shopware or Salesforce Commerce Cloud: almost all modern shop systems now offer retailers so-called rule engines or promotion modules that can be used to automatically control discounts, graduated prices or vouchers.
For example: ‘20% discount on all items in the Sale category’ or ‘Free shipping on orders over 100 Euro.’
These rules save time and ensure consistency – as long as they are configured correctly.
But even a small logical error is enough to unintentionally lower prices or apply discounts to the entire product range.
What's particularly critical is that many such errors go unnoticed at first because they are technically ‘correct’.
From discount chaos to margin loss: typical sources of error
- Incorrectly set conditions: A rule applies to the entire product range instead of just the target category.
- Overlapping promotions: Two rules are activated at the same time (e.g. sale discount + loyalty discount).
- Outdated data: A rule refers to old product groups or IDs that are no longer valid.
- Manual overrides: Price changes in the system override existing discounts – without warning.
Such errors can significantly impact margins. With large product ranges, it only takes a few hours to cause tens of thousands of pounds in lost discounts – often without anyone immediately noticing why sales are slumping.
Anomaly detection: the silent shield against pricing errors
This is where anomaly detection in e-commerce comes into play.
It works like an early warning system: instead of manually checking price rules, AI-based systems continuously analyse key figures such as average order value (AOV), discount rates and sales patterns.
If a value suddenly deviates significantly from the usual behaviour – such as an abrupt drop in AOV or an unusually high discount peak – the system recognises that something is wrong.
This often allows faulty pricing rules or discount chains to be identified before economic damage occurs.
Best practices for rule-based price control
- Clearly structure and document rules to avoid duplicate conditions.
- Use test environments before new rules go live.
- Integrate monitoring tools that automatically detect price and sales deviations.
- Regularly review rules, especially after system updates or new campaigns.
- Define alert thresholds that indicate AOV deviations – a simple but effective safeguard.
Conclusion: automation needs control
Automated pricing logic is a powerful tool for increasing efficiency and revenue – but only if it remains under control.
A combination of clearly documented rules and intelligent anomaly detection, such as that offered by INTELLIFANT, ensures that errors are identified before they become costly.
After all, automation should reduce workload – not create additional risks.
#ecommerce #earlywarning system #technology #softwareengineering
