ML Entitlement Recommendation for Asset Service Needs
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Solution Overview
Problem
Customers face difficulties in selecting the appropriate entitlement plans that meet their service level agreements (SLAs) and cost requirements, often leading to over-payment or under-utilization of features, due to the complexity of available options and the lack of consideration for past and future needs in existing entitlement management systems.
Innovation Solution
The implementation of a method using machine learning models to analyze user data, predict asset replacement and service needs, and generate customized entitlement recommendations, which are transmitted to users, thereby optimizing entitlement plans based on historical and future requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional entitlement management systems are used, then entitlement plans can be offered to customers, but customers face difficulty assessing appropriate entitlements leading to over-payment or under-utilization
Solution Approach 1:
The system automatically generates personalized entitlement recommendations by extracting data from multiple sources and analyzing it using machine learning models, eliminating the need for customers to manually assess complex entitlement options. The system serves itself by autonomously determining appropriate entitlements based on customer asset data and historical patterns.
Solution Approach 2:
The machine learning model acts as an intermediary between raw customer data and entitlement recommendations. It processes and analyzes extracted data from various sources, transforming it into actionable insights that generate personalized entitlement suggestions, thereby mediating the complex information processing required for accurate entitlement assessment.
2Adaptability or versatility
If multiple entitlement options are provided, then customers have more choices, but the complexity of options makes it difficult to assess appropriate entitlements
Solution Approach 1:
The system provides personalized entitlement recommendations tailored to each customer's specific asset data and historical patterns rather than presenting generic options. Each recommendation is locally optimized for the individual customer's needs, making the assessment process simpler despite the versatility of available entitlement plans.
Solution Approach 2:
The system uses historical data and patterns as feedback to continuously improve entitlement recommendations. By analyzing past customer behavior and asset performance, the machine learning model refines its predictions, making the assessment process more accurate and less complex over time.
3Device complexity
If entitlements are based on current needs only, then selection is simpler, but past and future needs are not considered leading to suboptimal entitlement choices
Solution Approach 1:
The system extracts and analyzes historical data before generating current entitlement recommendations. By performing preliminary analysis of past asset performance and customer behavior patterns, the system prepares comprehensive insights that improve the accuracy of current and future entitlement matching without adding complexity to the user-facing selection process.
Solution Approach 2:
The entitlement recommendation system is dynamic, continuously adapting to changing customer needs and asset conditions. It incorporates historical patterns and predictive analytics to forecast future needs, making the system flexible and responsive while maintaining simplicity through automated analysis.
Data Source
AI summary
A method comprises extracting data for one or more assets corresponding to a user, and analyzing the data using one or more machine learning models. The analyzing comprises predicting whether the one or more assets will require at least one of replacement and service. In the method, one or more entitlement recommendations for the user are generated based on the analysis, and the one or more entitlement recommendations are transmitted to the user.