SaaS Feature Upgrade Recommendation Engine
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Solution Overview
Problem
The current Software as a Service (SaaS) update process is time-consuming and confusing for tenants, as they struggle to determine applicable upgrades based on their configuration and licenses, leading to potential selection of unavailable features and requiring extensive communication with customer support.
Innovation Solution
A computer-implemented method and system that identifies feature configurations for customer instances, determines applicable feature upgrades from an upgrade library, and presents these upgrades to tenants, ensuring only eligible features are enabled, with options to provision upgrades automatically or with user consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ASP manually verifies tenant understanding and provisions upgrades through customer support, then upgrade reliability is improved, but the time required and process complexity increase significantly
Solution Approach 1:
The system enables tenants to independently determine applicable upgrades by automatically comparing their feature configuration against the upgrade library. The provisioning service autonomously provisions eligible upgrades without requiring customer support intervention, allowing the system to serve itself and eliminating manual verification steps.
Solution Approach 2:
The system performs preliminary analysis by automatically identifying which upgrades are applicable to each tenant based on their current feature configuration before the upgrade process begins. This preliminary determination eliminates the need for subsequent manual verification and accelerates the overall upgrade timeline.
2Manufacturing precision
If multiple communication rounds between tenant and ASP are required for upgrade verification, then upgrade accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The provisioning service acts as an intermediary that automatically compares the tenant's feature configuration with the upgrade library to determine eligibility. This automated intermediary eliminates the need for multiple communication rounds between tenant and customer support while maintaining accurate upgrade determination through systematic configuration analysis.
Solution Approach 2:
The system provides immediate feedback to tenants about which upgrades are applicable based on their current configuration. This automated feedback mechanism replaces iterative verification communications with a single, accurate determination, reducing process complexity while maintaining upgrade accuracy.
3Loss of information
If tenants manually review release notes and training materials before upgrading, then information completeness is improved, but loss of time and ease of operation worsen
Solution Approach 1:
The system performs preliminary identification of applicable upgrades by automatically analyzing the tenant's feature configuration against the upgrade library before the tenant needs to make a decision. This preliminary action provides complete information about eligible upgrades upfront, eliminating the need for tenants to manually review release notes and training materials to determine what they can upgrade.
Data Source
AI summary
Techniques are described for recommending updates to a customer instance in a software as a service (SaaS) model. A request can be received to upgrade features belonging to the customer instance. In response to receiving the request, a feature configuration that corresponds to the customer instance can be identified. The feature configuration can a plurality of features from the SaaS model that are available to the customer interface. Once the feature configuration has been identified, a feature upgrade from a plurality of feature upgrades in an upgrade library can be determined to be applicable to the customer instance. The determination can be made by evaluating the upgrade library and the feature configuration.