ecommerce_ops · ecommerce · workflow
Dynamic Yield delivers affinity-based personalized recommendations across web and email, driving 40% RPM increase
The marketing team lacked access to AI-based targeting and was manually handpicking products for email recommendation widgets, creating hours of manual work for the merchandising team.
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 · Capture omnichannel user events
Omnichannel events capture new and returning user activity to begin building customer profiles.
Tools used
Dynamic YieldUser Affinity algorithmdeep learning AImachine learning
Outcome
Implementing the User Affinity algorithm and machine learning-powered recommendations resulted in a 40% increase in revenue per thousand impressions (RPM) from email campaigns and eliminated hours of manual work for the merchandising team.
Results
Cost replaced40%
Grounding & classification
Source type: vendor customer story
19 fields verified against source quotes.
personalizationrecommendation systememailproduct catalogmetric backedtools describedworkflow describedecommercerevenue increasetime savedvendor customer storyecommerce opsmarketing opsdata sync enrichment