Website Capability Packages Using Editing History and Channel Selection
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Solution Overview
Problem
Existing website building systems offer rigid, predefined packages that fail to accurately meet the diverse needs of individual users, particularly those using systems serving millions across various geographies, industries, and expertise levels, lacking personalization and adaptability.
Innovation Solution
A system and method that analyzes user and website parameters, editing history, and business intelligence to create personalized promotional packages, utilizing A/B testing and machine learning to determine the best interface and communication channels for offering tailored capabilities, deals, and coupons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined service level packages are manually defined by the website building system vendor, then the system can offer basic capabilities and general-use premium capabilities, but the packages fail to accurately meet the diverse needs of individual users across various geographies, industries, and expertise levels
Solution Approach 1:
The patent implements dynamic package configuration that automatically adapts to user needs based on real-time analysis of user parameters, website parameters, and editing history. The system transitions from static predefined packages to dynamic, context-aware package generation that adjusts capabilities, limitations, and pricing based on individual user characteristics and behavior patterns.
Solution Approach 2:
The system changes multiple parameters simultaneously including user parameters (geography, industry, expertise level), website parameters (type, size, functionality), and package parameters (capabilities, limitations, pricing) to generate personalized promotional packages. This multi-parameter adjustment enables precise matching of package features to individual user needs while maintaining system manageability through automated rules.
2Ease of operation
If the system offers personalized promotional packages based on analysis of user parameters, website parameters, and editing history, then user engagement and satisfaction are enhanced, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary analysis of user parameters, website parameters, and editing history before generating promotional packages. By pre-processing and storing this information in structured formats, the system reduces computational complexity during the actual package generation process and enables faster, more efficient personalization when users interact with the system.
Solution Approach 2:
The system incorporates feedback loops that analyze user responses to promotional packages and continuously refine future package recommendations. This feedback mechanism improves user engagement over time while managing system complexity through iterative learning and adaptation rather than requiring complex upfront configurations.
3Productivity
If A/B testing and machine learning are utilized to determine the best interface and communication channels, then the promotional package delivery is optimized, but the implementation complexity and resource requirements increase
Solution Approach 1:
The patent segments the user base into different groups based on parameters such as geography, industry, expertise level, and behavior patterns. This segmentation enables targeted A/B testing and machine learning applications for each segment rather than treating all users uniformly, improving delivery efficiency while managing implementation complexity through focused, segment-specific optimization.
Solution Approach 2:
The system implements dynamic interface and communication channel selection that adapts based on real-time analysis of user preferences, device characteristics, and contextual factors. This dynamic approach optimizes promotional package delivery efficiency by presenting information through the most effective channels for each user while managing complexity through automated decision-making algorithms.
Data Source
AI summary
A system for a website building system server, the server having at least one processor and a memory, the system includes: an analyzer and updater to construct a promotional package for a user of the website building system according to at least editing history and/or business intelligence of a website belonging to the user, a channel determiner to determine the best mode of interface to present the promotional package to the user and a marketer to modify the website building system interface for the user according to the best mode of interface.