How Instacart uses machine learning-driven autocomplete to increase basket sizes and search engagement
Instacart's initial popularity-based autocomplete ranking left significant room for improvement: semantically duplicate suggestions cluttered results, misspelling handling was limited, and new or smaller retailers lacked sufficient search history to generate useful query suggestions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User types search prefix
A user begins typing in the search bar, producing a prefix that initiates query-suggestion retrieval.
Tools used
Search Embeddings model
Outcome
Successive ML improvements—fuzzy matching, semantic deduplication, catalog augmentation, and multi-objective ranking—yielded a 2.7% increase in autocomplete engagement rate, a 0.3% increase in search conversion rate, an increase in GTV per user, plus earlier gains of +2% items added to cart per search and +1.6% in ad revenue.
What failed first
A label-aggregation approach to multi-objective ranking ran into data sparsity problems, and naive removal of misspelled but high-converting queries would have reduced customer ordering of those products.