Generative AI Campaign Data Mining System
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Solution Overview
Problem
Advertisers face challenges in mining large campaign-related data sets to gain insights due to limited options and the requirement for proficiency in building custom queries, which many marketers lack.
Innovation Solution
An interactive reporting system utilizing a dedicated campaign large language model (LLM) that converts natural language requests into queries and generates natural language responses, allowing users to mine campaign data without programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional predefined graphs and tables are used to mine data, then the system structure remains simple, but the ease of operation deteriorates as marketers cannot effectively analyze complex campaign data without programming expertise
Solution Approach 1:
The patent introduces a large language model (LLM) as an intermediary between marketers and complex campaign data. The LLM translates natural language queries into executable data queries, processes the data, and presents results in intuitive formats. This mediator eliminates the need for marketers to directly interact with complex data structures or programming languages, thereby improving ease of operation while managing system complexity through automated translation layers.
Solution Approach 2:
The patent replaces the mechanical system of manual query building and data processing with an AI-based natural language processing system. Instead of requiring marketers to construct SQL queries or navigate complex data interfaces, the system uses LLMs to automatically interpret natural language requests, execute appropriate queries, and generate visualizations. This substitution dramatically improves ease of operation by eliminating the need for technical proficiency in data mining tools.
2Measurement precision
If custom queries in programming language are used to mine data, then the measurement precision is improved, but the ease of operation deteriorates due to the requirement for programming proficiency
Solution Approach 1:
The LLM serves as a translator intermediary that converts colloquial natural language queries into precise, executable data queries. This ensures that measurement precision is maintained while eliminating the need for programming knowledge. The system accurately interprets intent from natural language and generates appropriately complex queries behind the scenes, delivering precise results without requiring users to understand the underlying technical complexity.
Solution Approach 2:
The patent changes the parameter of query input from programming language to natural language. By accepting queries in the form of plain English sentences rather than structured code, the system maintains the precision of data analysis while dramatically improving ease of operation. The LLM dynamically adjusts the complexity of generated queries based on the data structure and user intent, ensuring accurate results regardless of user technical level.
3Quantity of substance
If more data and metrics are collected to provide comprehensive campaign insights, then the information quantity increases, but the difficulty of detecting and measuring relevant insights worsens
Solution Approach 1:
The patent replaces manual data analysis mechanisms with AI-powered automated insight detection. The LLM processes large volumes of campaign data and metrics automatically, identifying patterns, anomalies, and actionable insights without human intervention. This substitution allows the system to handle increasing data quantities while maintaining ease of insight detection through automated processing and natural language presentation of findings.
Solution Approach 2:
The system performs self-service data analysis by automatically processing, interpreting, and presenting insights from collected data. The LLM autonomously explores data structures, identifies relevant metrics, generates appropriate visualizations, and formulates conclusions without requiring user guidance on analysis methods. This self-service capability enables comprehensive data collection while simplifying the detection and measurement of insights through automated intelligence.
Data Source
AI summary
One or more computing devices, systems, and/or methods that provide an interactive reporting system for analyzing and reporting on campaign related data and mining insights using generative AI are provided. In an example, a user interface is configured to receive a natural language request and display a natural language response. A serving system is coupled to a data store housing campaign data sets and comprises a dedicated campaign large language model (LLM) configured to convert the natural language request to a suitable query capable of being run against the campaign data sets and to run the query against the data sets, to receive output from the query, to encode relevant query output together with the natural language request and/or the suitable query into an encoded output, and to decode the encoded output to generate a natural language response to the natural language request.