Recommender platform
Recommenders
- Almost 100 recommender algorithms out of the box
- Personalization (based on user behavior)
- Metadata based (category, brand, etc)
- Campaign based
- Collection based (for instance based on your shopping cart or uploaded list of products)
- Semantics based
- GDPR compliant
Recommender output
- Id-list
- Id-list + metadata
- Banner metadata
- Optional asynchronous computation
- File based recommendations
Architecture
Backend
- Load balancing
- Redundancy
- Monitoring
- Batch-system for handling file integrations
- Id-service that can identify a customer before login, and across devices. Can handle consents.
- Authentication service handling roles, login and access
- Test og dev environments
Frontend
- Rest-api
- Plug and play Java Script prepared for integrations
Architecture
- SAP Hybris ready
- Episerver ready
- Google feed, Solr
- Google Data studio
- Prepared file- and API-based integration points
Admin interface
Statistics
- A/B-tester
- Sales by recommendations
- Recommender requests
- Click/expose-statistics
- Reminder frequency
- Turnover
- Mails opened statistics
Configuration
- Pipes and pipelines
- Filters
- Rules
- Aggregate settings
Privacy
- Delete
- Order user data
Inspect data
- Metadata
- Aggregates
- Recommender output
Recommendation specifics
- Whole page optimizations (Assemblies)
- Customer targeting interface
- Campaign interface
- Interface for adding banner data and configure banner recommendations
