Contact Callback System Using Web Activity Pattern Analysis
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Solution Overview
Problem
Current systems for customer callback are inefficient, as representatives often guess the best time to contact customers, leading to wasted resources and a degraded customer experience due to missed calls.
Innovation Solution
A contact callback system that analyzes customer interactions with websites and advertisements to identify patterns in computing activity, predicting optimal times for representatives to call customers based on their availability and interest in specific topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If representatives call customers at guessed times, then customer service coverage is maintained, but resource efficiency deteriorates and customer experience worsens
Solution Approach 1:
The system performs preliminary actions by analyzing customer computing activity patterns in advance to predict optimal callback times before the actual call is made. This prevents wasted representative time on unavailable customers and reduces customer unavailability time by scheduling calls when they are most likely to answer.
Solution Approach 2:
The system uses feedback from customer computing activity data (website visits, app usage, email opens) to continuously refine predictions of optimal callback times. This feedback loop improves resource efficiency by learning from past call outcomes and adjusting future callback timing predictions accordingly.
2Productivity
If representatives call customers without pattern analysis, then operational simplicity is maintained, but productivity deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing computing activity patterns and determining optimal callback times without requiring complex manual intervention. The automated pattern recognition and prediction algorithms improve productivity while keeping system complexity manageable through self-optimizing processes.
Solution Approach 2:
The system changes parameters by transforming raw computing activity data into meaningful temporal patterns and optimal time predictions. This parameter transformation improves productivity by converting unstructured data into actionable callback scheduling information without proportionally increasing system complexity.
3Reliability
If multiple callback attempts are made, then customer reachability is improved, but resource waste increases
Solution Approach 1:
The system performs preliminary analysis of customer computing patterns before making callback attempts, predicting the optimal time for successful contact. This improves contact reliability while minimizing representative time waste by avoiding calls during periods when customers are unlikely to be available.
Solution Approach 2:
The system uses feedback from call outcomes and ongoing computing activity monitoring to adjust callback timing predictions. This feedback mechanism improves contact reliability across multiple attempts while optimizing representative time usage by learning from each interaction and refining future callback schedules.
Data Source
AI summary
An embodiment of a customer callback system operates by determining a call action indicating a customer to whom a representative is to speak. An identifier associated with the customer is determined. A plurality of computing activities corresponding to interactions between the computing device and one or more websites provided to the computing device over a defined period of time are retrieved. A pattern associated with when the plurality of computing activities corresponding to the interactions between the computing device and the one or more websites were performed is identified. The overlapping time of day is provided to the representative during which to perform the call action.