Uses computer vision AI to analyze social media images and videos, identifying product attributes, consumer behaviors, and contextual trends at a granular level. This enables brands to build authentic consumer panels, detect emerging trends, and predict product performance, helping them align offerings with real-time consumer preferences and market shifts.
Funding
Funding not disclosed
FFPAPGFounders
Product
Problem
Brands struggle to understand consumer preferences and emerging trends from social media images and videos due to the unstructured nature of visual data and the limitations of traditional text-based analysis. This makes it difficult to build authentic consumer panels, detect subtle shifts in product attributes, and accurately predict product performance.
Solution
Riviter uses computer vision AI to analyze social media images and videos, identifying product attributes, consumer behaviors, and contextual trends at a granular level. The platform dynamically builds "superpanels" of consumers on social media, verified for authenticity and relevance, to act as always-on focus groups. Riviter's visual search specializes in product domains, matching product attributes in images and videos to items in any inventory, SKU by SKU. This enables brands to pinpoint communities, detect trends with 5x the data volume of text-based analysis, and predict product performance by interpreting consumer signals.
Target Audience
The primary customers are consumer brands in industries such as makeup, hair color, fashion, packaged goods, and food and beverage.
Features
- Computer vision AI that recognizes people, products, and places in social media images and videos
- Dynamic creation of "superpanels" of verified consumers on social media
- Visual search that matches product attributes to items in any inventory
- Category-specific taxonomies that track a multitude of attributes and their changes over time
- Ability to pinpoint communities based on interests, affinities, networks, and behaviors
- Detection of trends with 5x the data volume of text-based analysis
- Prediction of product performance by interpreting consumer signals
- Customer support team to assist with setup and data interpretation