Predicting Product Deprecation Using Support Ticket Trends
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Solution Overview
Problem
Current methods for determining the optimal time to deprecate a product are financially driven and do not account for the product's health dynamics in the marketplace, such as support ticket data, which provides insights into usage and perception, leading to suboptimal decision-making.
Innovation Solution
A system that uses support ticket data to predict the end-of-life deprecation of a product by analyzing metadata, customer identifiers, installation, upgrades, and maintenance information, employing natural language processing and machine learning to estimate the probability of deprecation based on support ticket volumes and trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If financial metrics alone are used to determine product deprecation, then cost savings are achieved, but product health dynamics and customer satisfaction are ignored leading to suboptimal decisions
Solution Approach 1:
The patent combines financial metrics with product health metrics (support ticket volumes, installation trends, upgrade rates, customer satisfaction scores) into a unified predictive model. This merging allows the system to simultaneously consider both cost savings and product health dynamics, resolving the contradiction by integrating previously separate decision-making criteria into a single comprehensive framework that outputs a data-driven deprecation recommendation.
2Measurement precision
If support ticket data is analyzed to improve deprecation accuracy, then product health insights are gained, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional predictive model that processes multiple types of data (support tickets, financial metrics, installation data, upgrade data, customer satisfaction surveys) through a single unified system. This universal approach handles diverse data sources and analytical requirements within one framework, improving prediction accuracy while managing system complexity through consolidation rather than proliferation of separate systems.
Solution Approach 2:
The system automatically collects, processes, and analyzes support ticket data and other metrics without requiring manual intervention. The predictive model self-adjusts and generates deprecation recommendations autonomously based on the input data, reducing the operational complexity that would otherwise be required to manage manual data collection and analysis processes.
3Reliability
If multiple data sources are integrated for prediction, then decision quality improves, but data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on the most critical features from multiple data sources (support ticket volumes, installation trends, upgrade rates, customer satisfaction scores) that directly impact deprecation timing. By selectively extracting only the most relevant signals rather than processing all available data equally, the system maintains high decision quality while reducing unnecessary computational overhead and data processing requirements.
Data Source
AI summary
Predicting end-of-life support deprecation is described. A system receives an identification of a product, and then identifies support request records associated with the product. The system identifies support information associated with the support request records. A predictive model uses the support information to predict a deprecation for the product. The system outputs the prediction of the deprecation for the product.