Streaming Inference for Proactive Customer Service Modeling
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Solution Overview
Problem
Current customer service technologies in financial institutions are unable to anticipate customer needs proactively, leading to inefficient support solutions that often require human interaction and result in increased costs and customer frustration.
Innovation Solution
An event-driven architecture utilizing predictive analytics and generative AI models to analyze customer usage patterns and sentiment, enabling proactive, personalized engagement through digital channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If generic support solutions (chatbots and digital assistants) are provided to customers, then resource costs are reduced, but customer satisfaction deteriorates because the support does not specifically address individual customer pain points
Solution Approach 1:
The system performs preliminary actions by proactively identifying customers who are likely to call and reaching out to them before they contact the bank. This allows the system to prepare personalized support solutions in advance, addressing specific customer pain points before they escalate to generic support needs.
Solution Approach 2:
The system applies local quality by providing customized, personalized support to each customer based on their specific usage patterns and pain points, rather than applying generic support solutions uniformly to all customers. This targeted approach improves customer satisfaction while maintaining resource efficiency.
2Device complexity
If current generic support solutions are used, then implementation complexity is reduced, but support effectiveness deteriorates because they cannot anticipate customer needs proactively
Solution Approach 1:
The system performs preliminary analysis of customer usage patterns and proactively identifies customers who are likely to need support. This allows the system to anticipate customer needs before they arise, significantly improving support effectiveness without requiring complex real-time intervention systems.
Solution Approach 2:
The system continuously updates the customer servicing model with new data, creating a feedback loop that improves prediction accuracy over time. This iterative learning process enhances support effectiveness while maintaining manageable system complexity through automated model training and updates.
3Ease of operation
If manual customer service is provided to address individual customer needs, then customer satisfaction improves, but operational costs increase due to the need for dedicated support resources
Solution Approach 1:
The system enables a form of self-service by automatically analyzing customer usage patterns and generating personalized support solutions without requiring manual intervention. This automated approach maintains the personalized attention that improves customer satisfaction while eliminating the need for dedicated human support resources for each customer.
Solution Approach 2:
The system changes the parameter of support delivery from manual to automated while maintaining personalization through predictive analytics. By transforming the support model from human-driven to AI-driven, the system achieves cost reduction while preserving customer satisfaction through targeted, personalized interventions.
Data Source
AI summary
Techniques for applying predictive analytics to usage data in an event-driven architecture comprise systems, methods and storage mediums. A system having an event-driven architecture that facilitates proactive engagement with a customer over a network may comprise a memory storing instructions, a data storage that stores prompt data of one or more customer actions, and one or more processors. The one or more processors may execute the instructions to receive customer input data from the customer, provide the customer input data to a streaming inference engine that identifies one or more customer usage patterns, generate a customer servicing model based on the one or more customer usage patterns and the prompt data, store the customer servicing model in the data storage, provide an output to the customer based on the customer servicing model, and continuously update the customer servicing model stored in the data storage.