Campaign Data Normalization via Unified Marketing Model
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Solution Overview
Problem
Current analytics tools for advertising campaigns are limited in their ability to efficiently analyze and understand the performance of cross-platform campaigns due to the vast volume and varied formats of campaign data, leading to inefficiencies and revenue losses for businesses.
Innovation Solution
A system and method for normalizing campaign data from multiple advertising platforms by mapping data dimensions to a marketing data model, normalizing data values, and optimizing the dataset for faster manipulation, allowing for unified and efficient analysis across different platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If campaign data is collected from multiple advertising platforms independently, then the volume of data increases providing more comprehensive campaign information, but the complexity of analyzing and managing this data increases making it almost impossible to analyze using existing tools
Solution Approach 1:
The patent merges data from multiple advertising platforms into a unified data structure. The system consolidates campaign data from different sources (social media, display networks, search engines) into a single normalized format with unified schemas, allowing comprehensive analysis without the complexity of handling multiple separate data systems.
Solution Approach 2:
The patent creates a universal data model that can handle various types of campaign data from different platforms through a single interface. The unified schema and normalization layer provide multi-functional capability to process impressions, clicks, conversions, and other metrics across all advertising channels using the same analytical tools.
2Adaptability or versatility
If campaign data is gathered in many different forms and formats from different companies, then the data represents diverse platform-specific metrics, but the difficulty of normalizing and comparing data across platforms increases
Solution Approach 1:
The patent applies parameter changes by transforming diverse data formats into a unified schema. The system changes the structural parameters of data from various platforms (different field names, data types, formats) into standardized parameters through normalization processes, making comparison and analysis feasible across platforms.
Solution Approach 2:
The patent introduces an intermediary normalization layer between raw platform data and analytical tools. This intermediary layer acts as a mediator that translates diverse platform-specific formats into a common unified format, enabling seamless comparison and analysis without requiring changes to the original data sources or analytical tools.
3Productivity
If traditional analytics tools are used to analyze large volumes of cross-platform campaign data, then the analysis process becomes extremely time-consuming and inefficient, but switching to new specialized tools would require significant implementation complexity
Solution Approach 1:
The patent performs preliminary action by pre-normalizing and structuring campaign data from multiple platforms before analysis. The system prepares the data in advance by applying unified schemas and normalization rules, so that when analysis is performed, the data is already in the optimal format, significantly improving efficiency without requiring complex analysis tools.
Data Source
AI summary
A system and method for normalizing campaign data gathered from a plurality of advertising platforms. The method comprises receiving campaign data related to at least one campaign gathered from a plurality of advertising platforms; mapping data dimensions in the received campaign data to a marketing data model to produce a dataset that is organized and functions as the marketing data model; normalizing data values in the dataset according to a unified notation defined for each of the data dimensions in the marketing data model; and optimizing the normalized dataset to allow faster manipulation of data.