Customer Lifetime Value Sub-Component Prediction
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Solution Overview
Problem
Existing methods for determining customer lifetime value are limited in providing detailed, granular data necessary for making specific marketing and product decisions, as they primarily offer high-level predictions that do not account for sub-components such as revenue, propensity to attach, and other factors.
Innovation Solution
The use of predictive models trained on historical data for specific sub-components of customer lifetime value, which are then aggregated to provide detailed customer-level lifetime value data via a user interface, allowing for customizable and targeted insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If high-level lifetime value predictions are used, then decision-making speed is improved, but decision precision deteriorates
Solution Approach 1:
The patent segments the lifetime value metric into multiple sub-components (e.g., revenue, retention, acquisition cost) and applies separate predictive models to each sub-component. This allows the system to maintain high-level aggregation for speed while providing granular sub-component data for precision, resolving the contradiction between decision-making speed and decision precision.
2Measurement precision
If detailed sub-component level data is provided, then decision precision is improved, but data complexity increases
Solution Approach 1:
The patent divides the complex lifetime value metric into manageable sub-components, each handled by dedicated predictive models. This segmentation reduces the complexity of individual models while providing detailed precision through aggregated sub-component data, allowing users to drill down only when needed.
Solution Approach 2:
The patent adds a dimensional layer by separating lifetime value into multiple sub-component dimensions (revenue, retention, acquisition). This allows the system to present both high-level aggregated views and detailed sub-component views, managing complexity through dimensional organization rather than increasing overall data burden.
3Measurement precision
If multiple predictive models are used for sub-components, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by creating specialized predictive models for each lifetime value sub-component rather than using a single comprehensive model. This divides the computational task into smaller, more manageable segments that can be processed independently and then aggregated, improving measurement precision while controlling computational complexity through modular design.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for providing detailed customer-level lifetime value data via a user interface. Embodiments include receiving customer data related to a customer. Embodiments include using a plurality of predictive models to predict, based on the customer data, values for a plurality of sub-components of a lifetime value of the customer, wherein each predictive model of the plurality of predictive models corresponds to a sub-component of the sub-components. Embodiments include determining customer-level lifetime value data for the customer by aggregating the values for the plurality of sub-components. Embodiments include providing the customer-level lifetime value data for the customer, including a subset of the values for the sub-components, to an application for display to a user via the user interface.