Conversion Stage Prediction Using Query Logs and Location Data
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Solution Overview
Problem
Content providers face challenges in targeting users with relevant content at the right stage of their multi-stage conversion process, as existing methods rely on manual keyword selection and lack accurate automation.
Innovation Solution
A computer-implemented method and system that predicts the conversion stage of a user by analyzing query logs, location information, and keywords using term-frequency/inverse document frequency (TF-IDF) and Chi-Square distribution to determine significant changes between queries, enabling personalized content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual keyword selection is used for content targeting, then content providers can select keywords based on past experience, but the system lacks automation and accuracy in identifying user conversion stages
Solution Approach 1:
The system enables self-service by automatically analyzing query logs and location information to predict conversion stages without manual intervention. The machine learning model autonomously processes user data and generates stage predictions, replacing manual keyword selection with automated stage-based content delivery
Solution Approach 2:
The patent replaces the manual mechanical process of keyword selection with an automated computational system. The machine learning model substitutes human analysts by processing query logs, location data, and user behavior patterns to automatically determine conversion stages and recommend appropriate content
2Measurement precision
If third-party keywords are used with little knowledge of user stage, then keyword selection is simplified, but content relevance to user conversion stage deteriorates
Solution Approach 1:
The patent segments the content delivery process into distinct conversion stages (awareness, consideration, decision, retention). By dividing the user journey into these segments, the system can tailor content specifically to each stage, improving accuracy of stage identification while maintaining manageable system complexity through structured stage classification
Solution Approach 2:
The patent introduces conversion stage prediction as an intermediary layer between user queries and content delivery. This intermediary model analyzes query logs and location information to determine user stage, serving as a mediator that translates raw user data into actionable content recommendations without requiring direct complex analysis
3Productivity
If manual conversion stage models are created for different verticals, then content can be tailored to specific industries, but the process is time-consuming and lacks scalability
Solution Approach 1:
The patent creates a universal conversion stage prediction model that can be applied across multiple verticals and industries. The system processes query logs and location information using a standardized machine learning approach that adapts to different content types (restaurants, retail, services) without requiring manual model creation for each vertical, thereby improving productivity while reducing time investment
Solution Approach 2:
The patent utilizes parameter changes in user behavior data (query patterns, location transitions, time of day) to dynamically predict conversion stages. By monitoring changes in these parameters, the system can quickly adapt to different verticals and content types without manual reconfiguration, enabling fast content delivery across diverse industries
Data Source
AI summary
A user can issue a query about a process that has a number of stages. A stage of the process is determined using the query and location data associated with the query, and a stage prediction model. A stage learning system can select a sample of query logs for a category from a database of millions or billions of users' queries. Queries can be parsed into keywords. A category can be determined from location information associated with each query and from query keywords. Queries are aligned based on location and, optionally, keywords. TF-IDF values are computed for queries and are used to determine a difference significance between aligned, adjacent queries. If aligned, adjacent queries have a substantially difference in keywords and TF-IDF, then a conversion stage is identified. Content can be presented to the user based on the category, keywords, location, and conversion stage.