Personalized Customer Dunning via Real-Time Data Ingestion
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Solution Overview
Problem
Traditional telecommunication dunning processes are not personalized and do not consider real-time customer data, leading to inaccurate and generalized dunning actions, which are time-consuming and costly, and often result in erroneous barring or other issues.
Innovation Solution
A centralized customer data platform that uses real-time data ingestion pipelines to generate personalized dunning scores and perform customized dunning actions based on individual customer data, allowing for real-time decision-making and flexible dunning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scheduled billing dunning process is used, then system simplicity is maintained, but dunning accuracy and personalization deteriorate
Solution Approach 1:
A customer data platform is introduced as an intermediary system between the billing system and dunning process. This platform aggregates real-time customer data from multiple sources (billing systems, customer relationship management systems, social media, etc.) and provides personalized dunning recommendations without requiring complex modifications to the existing billing system. The intermediary handles the complexity of data integration and analysis, while the billing system maintains its simplicity.
Solution Approach 2:
The dunning process is segmented into distinct components: data collection from multiple sources, customer scoring based on payment behavior analysis, dunning action recommendation, and execution. This segmentation allows each component to be optimized independently, improving overall dunning accuracy while managing system complexity through modular architecture.
2Adaptability or versatility
If real-time data processing is implemented, then dunning personalization is improved, but processing time and computational resources increase
Solution Approach 1:
Customer data is pre-aggregated and scored in advance using historical payment behavior and demographic information. This preliminary processing creates a ready-to-use customer profile that enables rapid real-time dunning decisions without requiring extensive computational resources at the moment of dunning action. The system pre-calculates customer scores and segments, so when a dunning event occurs, personalized actions can be determined quickly.
Solution Approach 2:
The system dynamically adjusts dunning parameters (such as dunning thresholds, notification frequencies, and action intensities) based on real-time customer data and scores. By changing these parameters adaptively rather than using fixed rules, the system achieves high personalization while maintaining efficient processing through parameter-based decision logic rather than complex computational models for each dunning event.
3Ease of operation
If generalized dunning actions are applied to all customers, then operational efficiency is maintained, but customer experience and engagement deteriorate
Solution Approach 1:
The dunning system applies local quality by tailoring dunning actions to individual customer characteristics, payment histories, and current status. Instead of uniform treatment, each customer receives customized dunning strategies (such as different notification channels, timing, and action thresholds) based on their specific profile. This localized approach improves customer experience while the system maintains operational efficiency through automated personalized decision-making rather than manual intervention.
4Stability of the object's composition
If rigid dunning rules are enforced, then policy consistency is maintained, but flexibility to handle individual customer situations deteriorates
Solution Approach 1:
The dunning system transitions from static rigid rules to dynamic adaptive policies. Dunning thresholds, action selections, and notification strategies are dynamically adjusted based on real-time customer data, scores, and contextual factors. The system maintains policy consistency through structured decision frameworks while achieving flexibility through dynamic parameter adjustment based on individual customer situations, allowing same-policy different-outcomes based on customer needs.
Data Source
AI summary
A method and a system for personalized customer dunning, performed by at least one processor. The method includes monitoring a status of a plurality of invoices; obtaining a customer score for a customer corresponding to an invoice of the plurality of invoices, based on the status of the invoice, and wherein the customer score is previously determined based on customer data; receiving customer-specific data via a real time data ingestion pipeline from one or more customer data sources; generating a dunning score for the customer based on the received customer-specific data and the customer score; determining one or more dunning actions, based on the dunning score; performing the one or more dunning actions; and notifying the customer of the performed one or more dunning actions via a first engagement channel.