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AllLocal services2B2B & export3SaaS & tools1E-commerce8Media1Regulated9Travel & hospitality4Education3Auto & real estate1Pet care1Food & beverage1Logistics2Consumer & household5Support & after-sales1Professional services1Hiring & HR1Home services1Insurance1
IndustriesE-commerceConsumer & household2026-10-07

Cross-category recommendations: +36% add-to-cart on item pages, +15% on cart pages

Recommending general merchandise to grocery shoppers — a milk frother with the milk — has long been hard; LLM retrieval plus real-time cart context moved production A/B metrics.

Setting: recommending general merchandise to grocery-focused shoppers (pairing milk with a milk frother) is a long under-explored cross-category problem requiring product associations and real-time cart context.

Key numbers · 4References · 1Read →
IndustriesSupport & after-salesE-commerce2026-10-07

When service teams get AI: faster and better rated — but retrials unchanged, and top agents get worse

A large-scale field experiment in Alibaba's after-sales chat support: the assistant only drafts opening diagnoses and proposals, agents can adopt, modify or ignore — and who is using it decides the outcome.

Design: a large-scale field experiment with Alibaba randomised whether human chat agents could access a generative AI assistant that drafts issue diagnoses and solution proposals in the opening stage only; agents could adopt, modify or disregard them, so the study estimates effects of both access and usage.

Key numbers · 4References · 1Read →
IndustriesE-commerce2026-10-06

When AI agents spend money: 8 of 20 fraud classes are no better than guessing

The biggest agentic payment risk is not impersonation: the counterparty, settlement address and delivered service are all genuine — it just charges too much, and no identity check can see it.

Setting: AI agents already hold spend authority and settle payments without per-action human confirmation; the resulting loss is often not a security failure but a counterparty that is exactly who it claims to be, delivers the service, and overcharges.

Key numbers · 5References · 1Read →
SourcesConsumer & householdE-commerce2026-10-06

Incumbent advantage: with identical specs, a well-known brand is recommended 100% of the time

Three experiments on skincare: identical specifications produce near-total dominance for known brands — and less than a +0.1-star edge for a competitor breaks it.

Setup: skincare, a category where consumers cannot judge quality before buying and must rely on reputation, tested across three commercial models (GPT-4o-mini, Claude Sonnet, Gemini 3 Flash), with a robustness check on search goods.

Key numbers · 4References · 1Read →
IndustriesConsumer & householdE-commerce2026-10-05

Grocery recommendations move from items to categories — and cart-adds rise 7.5%

Online grocery has cyclical repurchase patterns that item-level models struggle to scale on; category-level recommendations with constrained decoding lifted production cart-adds by 7.5%.

Problem: online grocery recommendations must capture cyclical buying behaviour and diverse intents, while traditional item-level methods face scalability and accuracy limits — category-level recommendation is more structured and practical.

Key numbers · 4References · 1Read →
MethodsE-commerce2026-10-05

Can AI actually read your product pages? A 3,000-page extraction experiment

What AI needs from a product page is not pretty layout but extractable structured facts: specs, ingredients, price, certification.

Scope: 3,000 food product pages from three online retailers, with the goal of extracting key attributes such as ingredient lists and nutrition tables.

Key numbers · 4References · 1Read →