Churn Likelihood Assessment Using Absorbing State Models
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Solution Overview
Problem
In a non-contractual setting, distinguishing active from inactive clients is challenging due to the lack of observable churn events, leading to inaccurate and reactive client status assessment.
Innovation Solution
A system that models client behavior using a statistical model with a churn type of event as an absorbing state, allowing for the estimation of client churn likelihood by treating missing data as such and using an expectation-maximization algorithm to calculate the likelihood of churn based on client data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If an arbitrary measure of activity (e.g., transaction within a given period) is used to determine client status, then the approach is simple to implement, but the accuracy of client status assessment deteriorates
Solution Approach 1:
The patent transforms the client status assessment from a simple binary classification based on arbitrary time thresholds to a probabilistic assessment using multiple parameters including transaction frequency, recency, and statistical models. This allows the system to calculate churn likelihood as a probability value rather than making definitive classifications based on fixed time periods.
Solution Approach 2:
The patent introduces statistical models and probability calculations as intermediary mechanisms between the raw transaction data and the final client status determination. Instead of directly classifying clients as active or inactive based on simple time thresholds, the system uses churn likelihood scores as an intermediary to enable more nuanced and accurate assessments.
2Ease of operation
If a firm uses reactive approaches to identify inactive clients, then the implementation is straightforward, but the ability to retain clients deteriorates
Solution Approach 1:
The patent enables firms to perform preliminary assessments of churn likelihood before clients actually churn. By calculating probability scores based on transaction patterns and using statistical models to predict future behavior, the system allows firms to take proactive retention actions before client loss occurs, rather than reacting after inactivity is confirmed.
Solution Approach 2:
The patent implements a feedback mechanism where churn likelihood assessments are continuously updated based on new transaction data. The statistical models learn from observed client behavior patterns and refine predictions over time, allowing the system to adapt to changing client preferences and provide increasingly accurate retention insights.
Data Source
AI summary
System, including method, apparatus, and computer-readable media, for evaluating client status for a likelihood of churn. Client data may be received, with the client data representing events from a set of different event types performed by clients. Parameters of a statistical model that describes client behavior may be estimated using a computer and based on the client data. A churn type of event may be encoded in the statistical model as an absorbing state of a stochastic process, with a time of transition to the absorbing state modeled as being infinite. At least one of the parameters may correspond to the churn type of event. A likelihood of churn may be calculated for a plurality of the clients at one or more time points using the statistical model and its estimated parameters.