Predicts customer value and lapse at group level
- Who we serve
- Private Sector
- Category
- Retail, hospitality & consumer
- Value
- Revenue
- Human role
- Human sets the rules
- Maturity
The problem
Without ecosystem lifetime value & lapse prediction, teams in group loyalty & customer data make these calls on averages and instinct, because transactions across units are not joined up, and the value in the data goes uncaptured.
How it works
Ecosystem lifetime value & lapse prediction predicts customer value and lapse at group level. The approach relies on ML. It draws on transactions across units and is typically owned by teams in group loyalty & customer data. The main constraint is attribution across units. Where the human comes in: CRM teams set retention actions. It is in production at multiple organisations; examples include Retail conglomerates.
Where the human comes in
CRM teams set retention actions
What it needs
Transactions across units
What to plan for
Attribution across units
In the field
Retail conglomerates
Public deployments by other organizations, listed for context. Not Sphere projects.