Predictive Landing Pages for Pre-Filtered User Navigation
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Solution Overview
Problem
Existing advertising systems direct consumers to generic landing pages that require manual filtering, leading to frustration due to irrelevant results and inefficient navigation.
Innovation Solution
A generator application that dynamically creates customized landing pages based on user interactions, using an intermediary interface to understand consumer needs and navigate them to relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic landing pages are used for all users, then system complexity is reduced and ease of manufacture is improved, but user satisfaction deteriorates due to irrelevant results and manual filtering requirements
Solution Approach 1:
The system performs preliminary actions by predicting user intent before the user actually navigates or searches, using machine learning models to anticipate what products or information the user is likely seeking based on their behavior patterns, device type, location, and other contextual factors. This allows the landing page to be pre-customized with relevant products and information before the user even interacts with it.
Solution Approach 2:
The landing page is made dynamic by continuously adapting its content based on real-time user interactions and contextual data. The system dynamically generates personalized product recommendations, adjusts the layout and prioritization of information, and modifies the overall page structure based on predicted user intent, making each landing page unique to the individual user while maintaining ease of deployment through automated generation.
2Productivity
If personalized landing pages are created for each user, then user satisfaction and conversion rates improve, but system complexity and computational resources increase
Solution Approach 1:
The system implements self-service by using automated machine learning models and algorithms that independently analyze user data, predict intent, and generate personalized landing pages without requiring manual intervention. The system serves itself by automatically training models on collected data, generating predictions in real-time, and deploying customized pages, thereby reducing operational complexity despite the personalization capability.
Solution Approach 2:
The system achieves universality by creating a multi-functional platform that handles diverse user types, devices, and contexts through a single unified architecture. The same core prediction engine and landing page generation system serves all users across different scenarios (mobile/desktop, new/returning visitors, different product categories), reducing overall system complexity through standardization while maintaining personalization capabilities.
3Device complexity
If manual filtering is required on generic landing pages, then implementation simplicity is maintained, but user time and effort increase leading to frustration
Solution Approach 1:
The system performs preliminary filtering and organization of content based on predicted user intent before the user arrives at the landing page. By anticipating what products or information the user is likely seeking, the system pre-sorts and prioritizes content, eliminating the need for users to manually filter through irrelevant items and significantly reducing the time to find relevant information.
Solution Approach 2:
The system replaces the mechanical manual filtering process with automated computational intelligence. Instead of requiring users to manually click through filters and categories, machine learning models automatically analyze user preferences and behavior patterns to programmatically organize and present relevant content, substituting human cognitive effort with automated algorithms.
Data Source
AI summary
Systems and methods are provided for generating landing pages with predictive analytics. A client can customize data inputs to generate a dynamic landing page for a customer such that a primary filter can be applied to a set of results. The dynamic landing page can be an intermediate application separate from a destination web site. The client can add a plurality of layers of filters to be applied by the generator, separate from the destination web site, before the user is navigated to the destination web site associated with the client.