Ecommerce ops · Production
Dynamic Yield Predictive Targeting increases Revenue per User 54.1% for English sports team
The problem
The sports team was running ordinary A/B tests without per-segment personalization and had not yet identified the optimal Product Recommendations strategy for each audience segment.
Workflow diagram · grounded in source
1
A/B test initiated
trigger
“Convert an ordinary A/B test into an incredible personalization opportunity”
2
Segment-level winner identification
ai_action
“Automatically identify the winning test variation for each of its traffic segments”
3
Revenue per User increase
output
“Increase Revenue per User by a whopping 54.1% within a couple of weeks”
Reported outcome
Using Dynamic Yield's Predictive Targeting, the team automatically identified the winning test variation for each traffic segment and increased Revenue per User by 54.1% within a couple of weeks.
Reported metrics
Revenue per User54.1%
Reported stack
Dynamic YieldPredictive Targeting
Frequently asked questions
What did this team achieve with this AI workflow?
Using Dynamic Yield's Predictive Targeting, the team automatically identified the winning test variation for each traffic segment and increased Revenue per User by 54.1% within a couple of weeks.
What tools did this team use?
Dynamic Yield, Predictive Targeting.
What results were reported?
Revenue per User: 54.1% (source-reported, not independently verified).
How is this ecommerce ops AI workflow structured?
A/B test initiated → Segment-level winner identification → Revenue per User increase.