Lead Routing Clustering Model for Contact Center Conversion
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Solution Overview
Problem
Traditional methods for routing leads in customer contact centers are inefficient and unreliable, as they lack sufficient data to properly match agents with leads, leading to suboptimal allocation of limited resources and poor conversion rates.
Innovation Solution
A predictive machine learning model is employed to analyze lead and advisor data, using clustering analysis and conversion models to assign leads to the most suitable agents or agent groups based on similarity and conversion scores, with the option to use unsupervised, supervised, or combined clustering techniques, and Bayesian Bandits for optimal assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional routing methods are used to assign leads to agents, then the system is simple and easy to operate, but the conversion rate is poor and resource allocation is suboptimal
Solution Approach 1:
The system performs preliminary clustering analysis on lead and advisor data before routing occurs. The predictive model pre-processes historical data to identify patterns and characteristics, creating ready-to-use clusters that guide real-time routing decisions. This preliminary action enables better conversion rates without adding complexity during the actual routing moment.
Solution Approach 2:
The patent introduces an intermediary predictive machine learning model between the traditional switch system and the agent routing process. This intermediary analyzes lead and advisor data, applies clustering algorithms, and generates routing recommendations that improve conversion rates while maintaining compatibility with existing telephony infrastructure.
2Measurement precision
If more data is collected and analyzed using predictive models, then lead routing accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex data processing task into distinct clustering phases. Instead of attempting to analyze all lead and advisor attributes simultaneously, the system divides the data into meaningful clusters based on shared characteristics. This segmentation reduces computational complexity while maintaining high matching accuracy by focusing analysis on relevant patterns within each cluster.
Solution Approach 2:
The system transforms raw lead and advisor data into clustered representations, changing the parameter space from individual attribute comparisons to cluster-based matching. This parameter transformation simplifies the matching process by grouping similar entities together, reducing the dimensionality of the problem while preserving the essential information needed for accurate routing decisions.
3Productivity
If leads are placed in call queues with traditional switching, then all leads can be routed eventually, but leads wait longer and resource utilization is inefficient
Solution Approach 1:
The patent implements dynamic routing that adapts to real-time conditions. Instead of static queue-based routing, the system continuously analyzes lead characteristics and advisor availability, dynamically assigning leads to the most appropriate agents. This dynamic approach reduces wait times by directing leads to agents who can handle them immediately rather than following rigid queue sequences.
Solution Approach 2:
The system incorporates feedback loops that monitor routing outcomes and conversion rates. By analyzing which lead-agent matchups result in successful conversions, the system learns and adjusts its clustering and routing strategies. This feedback mechanism improves resource allocation efficiency over time by directing leads to agents with higher likelihood of successful interaction.
Data Source
AI summary
A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.