Ecommerce ops · Production
APMEX drives double-digit growth with Dynamic Yield personalization
The problem
APMEX had a diverse customer base and an enormously large product catalog, and lacked a sophisticated enough understanding of user motivations to serve targeted promotions across different segments.
Workflow diagram · grounded in source
1
Shopper motivation discovery
trigger
“APMEX turned to Dynamic Yield to discover the real motivations of its shoppers in order to serve more targeted promotions across a wide range of different segments”
2
Real-time segment analysis
ai_action
“Analyze and identify high-value segments in real time for experience-tailoring”
3
Targeted promotion delivery
output
“serve more targeted promotions across a wide range of different segments”
4
Product discovery optimization
ai_action
“Optimize product discovery for vast assortment of SKUs based on traffic quality”
Reported outcome
Key financial metrics including conversion rate, average order value, and revenue per session increased between 5-7% through Dynamic Yield's personalization capabilities.
Reported metrics
Conversion rate5-7%
Average order value5-7%
Revenue per session5-7%
Reported stack
Dynamic YieldCRM
Frequently asked questions
What did this team achieve with this AI workflow?
Key financial metrics including conversion rate, average order value, and revenue per session increased between 5-7% through Dynamic Yield's personalization capabilities.
What tools did this team use?
Dynamic Yield, CRM.
What results were reported?
Conversion rate: 5-7%; Average order value: 5-7%; Revenue per session: 5-7% (source-reported, not independently verified).
How is this ecommerce ops AI workflow structured?
Shopper motivation discovery → Real-time segment analysis → Targeted promotion delivery → Product discovery optimization.