Asset-Level Social Feedback Attribution for Marketing Campaigns
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Solution Overview
Problem
Conventional marketing solutions cannot attribute user feedback from social networks to individual assets within a campaign, making it difficult for marketers to determine the effectiveness of specific assets in a marketing campaign.
Innovation Solution
A system that collects and analyzes user comments on social networks, using natural language processing to attribute comments to individual assets and generate social metadata, including a social mention count and sentiment score, to enhance asset metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional marketing solutions are used to track campaign effectiveness, then overall campaign performance can be measured, but individual asset effectiveness cannot be attributed
Solution Approach 1:
The patent segments user feedback analysis from campaign-level to asset-level by dividing comments into asset-specific groups using image recognition and keyword matching. Each asset receives individual sentiment analysis and metadata generation, enabling precise measurement of individual asset effectiveness rather than treating the entire campaign as a single unit.
Solution Approach 2:
The patent introduces an intermediary feedback analysis system that acts as a mediator between user comments and asset performance measurement. This system uses image recognition, keyword extraction, and sentiment analysis as intermediary processes to bridge the gap between raw user feedback and actionable asset-level insights.
2Loss of information
If social network comments are collected and analyzed, then asset performance insights can be generated, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional feedback analysis system that simultaneously performs image recognition, keyword extraction, sentiment analysis, and metadata generation through a unified platform. This universal system handles diverse social network comment formats and sources while producing standardized asset-level insights, reducing the need for separate specialized systems.
Solution Approach 2:
The system employs automated self-service mechanisms including automatic image recognition from campaign assets, autonomous keyword extraction from comments, and self-executing sentiment analysis algorithms. These self-service capabilities reduce manual intervention requirements and simplify system operation despite the underlying analytical complexity.
Data Source
AI summary
Associating social comments with individual assets used in a campaign is described. In one or more embodiments, a campaign that includes one or more assets (e.g., images or videos of products) is published to one or more social networks. Comments (e.g., user comments, user shares, or other textual feedback) to the campaign on the one or more social networks are collected and analyzed to attribute each comment to an individual asset of the campaign. Social metadata, such as a social mention count and a social sentiment score, is generated based on the comments to enhance metadata of the individual asset.