Proactive Knowledge Offering System for Contact Center Self-Service
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Solution Overview
Problem
Customer contact centers face challenges in deflecting calls by providing self-service options effectively, as existing systems fail to anticipate and offer relevant information to customers proactively based on their needs and navigation patterns.
Innovation Solution
A processor-driven system that gathers user interaction data to anticipate needs, generates queries, and proactively offers relevant knowledge documents or engagement invitations, using feedback to improve relevance scores and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides information to customers as early as possible to enable self-service, then customer self-service capability is improved, but the system complexity increases due to the need to anticipate and proactively identify customer needs
Solution Approach 1:
The system performs preliminary actions by analyzing customer navigation patterns and interaction data to anticipate needs before customers explicitly express them. The proactive knowledge offering system identifies potential information requirements in advance and presents relevant knowledge documents before customers would naturally seek them, enabling self-service without requiring customers to actively search for information.
Solution Approach 2:
The system enables customers to serve themselves by automatically providing relevant information based on their navigation behavior. Instead of requiring customers to actively search for information or contact support, the system monitors their interactions with the website and proactively delivers appropriate knowledge documents, allowing customers to resolve their issues independently.
2Reliability
If the system proactively identifies and suggests knowledge to customers before they request it, then customer satisfaction is improved, but the loss of information increases due to the challenge of accurately anticipating customer needs
Solution Approach 1:
The system incorporates feedback mechanisms where customer interactions with proactively offered knowledge documents are tracked and analyzed. This feedback is used to continuously refine and improve the accuracy of need anticipation algorithms, adjusting to individual customer preferences and behaviors over time to reduce information loss and improve satisfaction.
Solution Approach 2:
The system performs preliminary analysis of customer navigation patterns and interaction data to build accurate models of customer needs before explicit requests are made. By studying historical interaction data and navigation behaviors in advance, the system develops predictive capabilities that reduce the risk of inaccurate information delivery.
3Productivity
If the system extends engagement offers to customers based on anticipated needs, then productivity is improved by deflecting calls to the contact center, but the loss of time increases due to the processing required to analyze interactions and generate offers
Solution Approach 1:
The system performs preliminary analysis of customer interaction data continuously in the background, building profiles and anticipating needs before engagement offers are required. This pre-processing of data enables rapid generation of targeted offers when customers are most likely to engage, reducing the time penalty for offer generation while maintaining high deflection effectiveness.
Solution Approach 2:
The system implements partial analysis by focusing on key navigation patterns and interaction indicators that are most predictive of customer needs, rather than analyzing every single interaction in detail. This selective approach reduces processing time while maintaining sufficient accuracy to effectively deflect calls through targeted knowledge offers.
Data Source
AI summary
A system and method for proactively making knowledge offers. A processor is configured to gather information on interactions by a user with resources provided by an enterprise having a customer contact center. The processor anticipates need of the user based on the gathered information, and generates a query based on the anticipated need. Prior to the user expressly requesting knowledge relating to a particular topic, the processor proactively identifies and suggests the knowledge to the user based on the generated query. The processor receives feedback relating to the suggested knowledge and outputs based on the feedback, a relevance score for the suggested knowledge.