Shelfware Prediction Using Machine Learning Models
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Solution Overview
Problem
Software providers face inefficiencies in resource allocation and software adoption due to the challenge of predicting which software products will become shelfware, leading to wasted resources on unused software and inadequate post-sales support.
Innovation Solution
The development of an intelligent shelfware prediction system using machine learning models that analyze historical data to forecast the likelihood of software products becoming shelfware, providing proactive risk assessments and automated adoption assistance to mitigate these risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software products are sold to customers without usage prediction, then sales revenue is improved, but resource waste increases due to shelfware
Solution Approach 1:
The system performs preliminary shelfware risk assessment before software deployment by training machine learning models on historical data and generating risk predictions for new software products. This early prediction enables proactive resource allocation and mitigation strategies before the software is fully deployed, preventing waste while maintaining sales opportunities
Solution Approach 2:
The system continuously collects usage data from deployed software and feeds it back to retrain and improve machine learning models. This feedback loop enables the system to learn from actual software usage patterns, improving prediction accuracy over time and enabling better resource allocation decisions for future software deployments
2Ease of operation
If post-sales resources are allocated without prediction data, then customer support coverage is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The system applies different resource allocation strategies to different software products based on their individual shelfware risk profiles. High-risk products receive intensified monitoring and support resources, while low-risk products receive standard support. This localized quality approach optimizes resource allocation efficiency while maintaining adequate coverage across all products
3Measurement precision
If machine learning models are trained on historical shelfware data, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system automatically collects historical shelfware data from usage tracking, performs data preprocessing, trains machine learning models, and generates predictions without requiring manual intervention. This self-service automation handles the complexity of model training and data management internally, providing accurate predictions while keeping the user-facing interface simple
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for intelligent shelfware prediction and system adoption assistance. One example method includes identifying historical shelfware information for software products for customers of a software provider. The historical shelfware information is used to train machine learning models to generate a prediction that indicates a likelihood that a particular product for a particular customer will turn into shelfware. A request is received to generate a shelfware prediction for a first software product for a first customer of the software provider. A first trained machine learning model corresponding to the first software product and the first customer is identified. A first shelfware risk prediction is received from the first trained machine learning model that indicates a likelihood that the first software product turns into shelfware for the first customer. The first shelfware risk prediction is provided in response to the request.