marketing_ops · media · workflow
Identify User Journeys at Pinterest Using Dynamic Keyword Extraction and ML Ranking
Pinterest needed to move beyond understanding users' immediate interests to comprehend their underlying, long-term goals, so that recommendations could assist users in achieving those goals rather than only surfacing transient interests.
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 activity data ingestion
User search history, activity history, and board interactions are aggregated as input data for the journey extraction pipeline.
Tools used
SearchSageLLMsQwenRay
Outcome
Journey-aware notifications delivered statistically significant gains in user engagement, including an 88% higher email click rate and a 32% higher push open rate compared to interest-based notifications, and a 23% increase in positive feedback from user surveys.
Results
Volume88% higher
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes, 2 dropped as unverifiable.
content generationpersonalizationpredictive analyticsrecommendation systemknowledge basesocial media postbuilder submittedmetric backednamed customerproduction runtime claimedworkflow describedecommercemediaconversion increasecustomer satisfactiontechnical build writeupecommerce opsmarketing opsdata sync enrichmentextract classify route