Improving Customer Journey
VALUE PROPOSITION
- Improving customer satisfaction, surpassing revenue per guest growth targets, and dramatically reducing marketing costs
- Enabling a major Digital Transformation by utilising ELEMENT’s revolutionary Knowledge Graph technology and advanced pattern-matching to holistically represent customers and characterise segment behaviour according to guest interaction scenarios
- Lowering Total Cost of Ownership by shifting to OPEX via cloud infrastructure and PaaS. Decreasing labour-intensive tasks by providing process-centric, AI-embedded applications
Improvement of a customer journey for a world-leading outdoor parks and recreation company, by refining its understanding of guests’ behaviours and interactions using information from IoT devices and public data sources
BACKGROUND
The company was unable to leverage its data assets to enhance customer journey, create personalised offerings, and drive revenue growth per visitor
Despite an increase in park guests, the impact of intense competition was eroding guest satisfaction ratings
Neither the existing IT infrastructure nor other, alternative solutions were able to meet functional, scalability, time and budget requirements
TARGETS
- Enhance guests’ experience according to personal preferences, providing a memorable vacations and reinforcing guest satisfaction
- Gain comprehensive insights on guests using onsite IoT (Internet of Things) devices, tracking on-premise activity and enriching this data with public information regarding their social behaviour and demographics, to discover cross-sale and up-sale opportunities
- Prioritise and tailor product offerings according to customer receptivity
ADDRESSING THE NEED
- Analysed a multitude of data types, including mobile application usage, credit card purchases, online/social activity and park & resort activity (geospatial and temporal data), using AI techniques including Natural Language Processing
- Created an enriched profile of guests, continually enhanced during in-park and online visits based on customer segment characteristics, behaviours indicating interest, and scenario-based guest interactions
- Deployed Machine Learning models to identify guest patterns (geo-temporal, purchase, etc.) and calculate correlation to scenario characteristics, allowing guests to receive personalised recommendations, promotions, discounts, etc.
- Applied Knowledge Graph technology and Machine Learning to identify guests with similar backgrounds, behaviours and interests to more effectively direct services and special offers
Other Use Cases
