Predictive Model for First Contact Resolution Across Channels
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Solution Overview
Problem
Conventional methods for determining First Contact Resolution (FCR) metrics lack insights into cross-channel customer journeys, and the limited availability of large, annotated datasets hinders the performance of machine learning algorithms in predicting contact resolutions.
Innovation Solution
The method involves determining contact data associated with customer data and FCR metric data, labeling contact characteristics according to predefined features, and using these features to train a predictive model that can predict the resolution of customer issues and suggest appropriate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine FCR metrics tracking customer journeys within a single channel, then the measurement process is simple, but the measurement precision is insufficient due to lack of insights into cross-channel journeys
Solution Approach 1:
The patent combines data from multiple communication channels (voice, video, text, mobile applications, web portals) into a unified data structure. This merging enables comprehensive tracking of cross-channel customer journeys, allowing FCR metrics to accurately reflect customer experiences across all interaction points rather than isolating single channels.
Solution Approach 2:
The patent creates a universal data structure and machine learning model that can process and analyze diverse types of customer interaction data across different channels. This multi-functional approach allows the same system to handle various channel types and interaction formats, improving measurement precision without requiring separate systems for each channel.
2Reliability
If machine learning models are trained with limited annotated datasets, then the annotation cost and time are reduced, but the prediction accuracy decreases due to insufficient training data
Solution Approach 1:
The patent uses synthetic data generation techniques to create additional training examples by copying and transforming existing annotated data. This approach multiplies the effective training dataset size without requiring proportional increases in expert annotation efforts, thereby improving model reliability while managing data quantity constraints.
Solution Approach 2:
The patent implements preliminary data annotation and labeling efforts to create a foundational dataset that can be subsequently used for synthetic data generation and model training. This preliminary action establishes a base of high-quality annotated data that amplifies the value of subsequent training iterations.
3Measurement precision
If expert observers are used for data annotation, then the data quality is high, but the availability and cost increase significantly
Solution Approach 1:
The patent implements automated data labeling and annotation systems that enable self-service processing of customer interaction data. This reduces dependency on expert observers for routine annotation tasks, significantly increasing annotation throughput while maintaining acceptable quality standards through automated quality control mechanisms.
Solution Approach 2:
The patent introduces intermediary automated processing layers between raw data and expert annotation. These intermediaries perform preliminary data preparation, filtering, and pre-annotation, reducing the burden on expert observers and allowing them to focus on higher-value validation and complex cases, thereby improving both quality and throughput.
Data Source
AI summary
Methods, systems, and apparatuses for predicting an estimate of a number of contacts a customer may perform before resolving one or more issues associated with the customer or a suggestion for resolving one or more issues associated with the customer. Contact data associated with customer data and first contact resolution (FCR) metric data may be used to train a predictive model. The predictive model may be trained to output a prediction indicative of one or more of a resolution of one or more issues associated with a customer within one or more contacts, and a suggestion for resolving one or more issues associated with the customer.