Integrated Data Call Routing for Next-Best Agent Selection
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Solution Overview
Problem
Existing call routing systems in call centers are limited by static methods that do not adapt to changes in data or context, and skill-based routing systems lack accuracy and efficiency due to limited data consideration and context from both customer and organizational perspectives.
Innovation Solution
An integrated data system that unifies customer event data, customer profile data, and agent data to dynamically adapt agent recommendation algorithms based on available data combinations, using a machine learning-based model to identify the next best agent for customer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skill-based routing algorithms are used to match customers with agents based on expertise, then agent assignment accuracy improves, but system complexity increases due to maintaining agent profiles and skill matrices
Solution Approach 1:
The patent combines multiple data sources (customer profiles, interaction history, agent skills, real-time availability) into a unified routing system that processes all this information together through machine learning models, rather than using separate systems for each data type.
Solution Approach 2:
The patent replaces traditional rule-based routing mechanics with machine learning-based recommendation algorithms that can dynamically process complex data relationships and provide more accurate agent recommendations without requiring explicit programming of all routing logic.
2Device complexity
If static routing methods are used to handle predetermined conditions, then system complexity is reduced, but adaptability to changes in data or context deteriorates
Solution Approach 1:
The patent implements dynamic routing recommendations that adapt to changing conditions including real-time agent availability, customer context, and interaction history. The system continuously updates recommendations based on current data rather than relying on predetermined static rules.
Solution Approach 2:
The patent incorporates feedback loops where the system analyzes outcomes of previous routing decisions and uses this information to improve future recommendations. The machine learning models are trained on historical data and continuously refined based on performance feedback.
3Speed
If limited data is considered in skill-based routing systems, then processing speed improves, but assignment efficiency deteriorates due to insufficient context consideration
Solution Approach 1:
The patent performs preliminary processing and indexing of customer profiles, interaction histories, and agent skill data before routing decisions are needed. This preprocessing allows the system to quickly retrieve and analyze relevant information when routing requests occur, maintaining fast processing speeds while considering comprehensive data.
Solution Approach 2:
The patent applies different processing depths and data consideration levels to different routing scenarios. For simple queries, the system uses faster, lighter processing, while for complex interactions it engages more comprehensive data analysis, optimizing the balance between speed and thoroughness based on the specific context.
Data Source
AI summary
Systems, methods, and other embodiments associated with an integrated data system that recommends next best agents for call centers are described. In one embodiment, a method includes a customer interaction associated with an issue and a customer ID, querying a unified data source to identify which categories of data are available in response to receiving a customer interaction associated with an issue. The unified data source integrates a plurality of data categories from different data sources. A combination of available data categories is determined and based on the combination of available data categories, selecting and executing a routing algorithm from a plurality of routing algorithms. The executed routing algorithm generates an agent recommendation for the customer interaction and a communication channel is established between a device associated with the recommended agent and the customer interaction to route the customer interaction to the recommended agent.