Insights

AI search research

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.

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IndustriesConsumer & household2026-10-07

Handing appliance scheduling to AI: it captures 96.7–98% of the savings — and still needs a validator

Five LLMs acting as home energy managers: cheap is not the same as safe or feasible, models both invent schedules and explain a constraint correctly while committing an invalid one.

System: a tool-calling ReAct agent converts natural-language requests into half-hourly schedules for multiple household appliances, using dynamic tariffs, weather forecasts, PV generation estimates and household demand data.

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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.

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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 →

IndustriesConsumer & household2026-10-05

Can AI predict household spending? A scorecard across 6 surveys and 32 tasks

Most models beat a no-change baseline, but household predictions still carry systematic over- and under-prediction — and gradient-boosted trees still lead on most tasks.

Benchmark scale: six US household surveys unified into 32 prediction tasks spanning numeric, categorical and probabilistic outcomes across consumption, income, labour, expectations and housing.

Key numbers · 5References · 1Read →