Ecommerce ops · Production
Juniqe increases SEM campaign revenue 20% with Dynamic Yield predictive A/B testing and automatic optimization
The problem
Juniqe needed to increase sales by guiding customers to the most relevant products and highest-value pages, but the complexity of individual consumer tastes made it impossible to identify which banner variations, content combinations, and page layouts drove the best results.
Workflow diagram · grounded in source
1
Banner variation testing initiated
trigger
“Juniqe began by testing homepage banner variations for desktop, mobile apps, and tablets”
2
Predictive A/B selects top variations
ai_action
“Using predictive A/B testing, Juniqe tested various banner variations and automatically pushed the top three variations that generated the most revenue per session (RPS)”
3
Time-sensitive offer routing
routing
“The company also created conditions to allow for banners with time-sensitive offers to replace these winning variations for set periods of time”
4
Product page layout continuous optimization
ai_action
“To find the optimal product page layout, the company tested various page elements with the conversion goal of increasing revenue”
Reported outcome
Juniqe drove a 20% increase in SEM campaign revenue and an 8% uplift in revenue on product pages through predictive A/B testing and automatic optimization.
Reported metrics
SEM campaign revenue20%
Revenue on product pages8%
Reported stack
Dynamic Yield
Frequently asked questions
What did this team achieve with this AI workflow?
Juniqe drove a 20% increase in SEM campaign revenue and an 8% uplift in revenue on product pages through predictive A/B testing and automatic optimization.
What tools did this team use?
Dynamic Yield.
What results were reported?
SEM campaign revenue: 20%; Revenue on product pages: 8% (source-reported, not independently verified).
How is this ecommerce ops AI workflow structured?
Banner variation testing initiated → Predictive A/B selects top variations → Time-sensitive offer routing → Product page layout continuous optimization.