ecommerce_ops · media · workflow
Dynamic Yield Predictive Targeting increases Revenue per User by 54.1% for an English sports team
An English sports team needed to identify the optimal product recommendation strategy for each of its audience segments and lacked an automated way to determine which test variation to serve to which traffic segment.
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 · A/B test launch
An ordinary A/B test serves as the starting point for the workflow.
Tools used
Dynamic YieldPredictive Targeting
Outcome
Predictive Targeting automatically identified the winning test variation per segment, converting an ordinary A/B test into a personalization opportunity and increasing Revenue per User by 54.1% within a couple of weeks.
Results
Time saveda couple of weeks
Cost replaced54.1%
Grounding & classification
Source type: vendor customer story
17 fields verified against source quotes.
personalizationpredictive analyticsrecommendation systemproduct catalogmetric backedtools describedworkflow describedmediaconversion increaserevenue increasevendor customer storyecommerce opsmarketing opsextract classify route