Causal Reason Detection for Targeted Churn Prevention
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Solution Overview
Problem
Traditional probabilistic models fail to provide actionable insights into the underlying causes of customer churn or network failures, limiting the ability to effectively prevent these events by addressing the specific factors driving them.
Innovation Solution
A framework using artificial intelligence and machine learning to estimate the risk of events like customer churn or network failures, identify causal reasons, and automatically determine targeted treatments to mitigate these risks, employing a four-phase operation of prediction, causal reason identification, treatment determination, and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional probabilistic models are used to predict customer churn or network failures, then prediction capability is provided, but actionable insights into underlying causes are not obtained
Solution Approach 1:
The patent segments the prediction model into two distinct components: a probabilistic prediction module that estimates the likelihood of churn or failure events, and a causal reasoning module that identifies the underlying causes. This segmentation allows each module to specialize in its strength while the integrated system delivers both accurate predictions and actionable causal insights.
Solution Approach 2:
The patent introduces causal reasoning as an intermediary layer between raw data and prediction outcomes. This intermediary component analyzes relationships and dependencies in the data to infer causal factors, thereby bridging the gap between statistical correlation and causal understanding without compromising prediction accuracy.
2Productivity
If generic service improvements and promotions are offered to prevent churn, then customer retention efforts are made, but targeted interventions addressing specific causal factors are not implemented
Solution Approach 1:
The patent implements preliminary action by proactively identifying causal factors for potential churn before it occurs. By analyzing customer behavior patterns and inferring causal reasons early in the churn process, the system enables service providers to intervene with targeted solutions before customers actually leave, thereby improving retention effectiveness.
Solution Approach 2:
The patent applies local quality by customizing retention interventions based on the specific causal factors identified for each customer or network segment. Rather than applying uniform promotions, the system tailors solutions to address the root causes detected in different contexts, such as pricing issues, service quality problems, or competitive pressures.
3Loss of information
If comprehensive data analysis is performed to identify causal reasons, then actionable insights are obtained, but computational resources and processing time are increased
Solution Approach 1:
The patent applies partial action by focusing the causal analysis on the most relevant features and data points specific to each prediction case. Rather than analyzing all possible variables uniformly, the system identifies and concentrates computational resources on the subset of data most likely to reveal causal factors, thereby reducing overall processing time while maintaining insight quality.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the depth and scope of causal analysis based on the predicted risk level. For high-risk cases, more comprehensive causal investigation is performed, while for low-risk cases, lighter analysis is applied. This adaptive parameter adjustment optimizes the balance between obtaining causal insights and consuming computational resources.
Data Source
AI summary
The present teaching relates to detecting causal reasons for certain action and determining treatments to prevent the action. Information on services to users is collected and used to generate targeted segments of users, each of which corresponds to a level of risk associated with a user action with users estimated at the level of risk. The information is also used to generate causal segments, each of which corresponds to a causal reason that causes the user action. Based on the targeted segments and causal segments, causal reason(s) associated with each user to carry out the user action is estimated. A market action directed to each estimated causal reasons may be automatically recommended and executed to prevent a user to carry out the user action.