Using robust inference algorithms built upon machine learning and rule based approaches, Skelter Labs’ Hyper-Personalization Eengine empowers brands to access a new dimension of customer insights based on their real-world behaviors and interests. With in-depth inference layers using mobile signals and data, it generates real-time contextual data and granular user profile enabling hyper-personalization in every customer experience.
- Recognize customer’s context in real-time abstracting raw signals into real-world behaviors with detailed entities.
- ex) GPS, WiFi, time, movie schedule and etc. → Visiting [Lotte Cinema] → Watching [Lady Bird]
- Rich individual customer profiles based on accurate user modeling with historical and real-time contextual data such as:
- Personal Interest: [Indie Movie Fan]
- Favorite Movie: [Lady Bird]
- Favorite Director: [Marc Webb]
- Go-to Theater: [Lotte Cinema]
- Movie pattern: [Movie Goer: every Sunday]
- Provide means to create highly personalized offers through micro-segmentation targeting the individuals.
- ex) “Indie Movie Fans who like Lady Bird, work in Seongsu-dong, and are currently commuting to work.”
Skelter Labs Hyper-Personalization Engine enable brands to supercharge hyper-personalization strategy with contextual customer data for use in decisioning and delivery.
- High Precision Targeting: Effective campaigning through granular profiling and contextual real-time data
- Customer Journey Mapping: New insights on the entire customer journey by knowing what they do before and after receiving personalized offerings
- Assistant-like Offerings: Proactive recommendation with contextual triggers and messaging
- Automation: Enable automation based on the customer’s daily routine
- Risk Assessment: Enhance risk profiling based on daily routine, lifestyle behaviors for health, finance insurance