Consumers often rely on ratings to make purchasing decisions, but these ratings are typically presented as static snapshots—like a "4.2-star average"—without historical context. This can lead to misinterpretations, such as assuming a product is consistently good when its rating has actually declined sharply due to a recent issue. A tool that visualizes how ratings change over time, along with annotations explaining key events (e.g., product recalls, management changes), could help users make more informed choices.
The idea would involve aggregating and displaying rating trends for products and services, with features like:
Data could be sourced from APIs (e.g., Amazon, Yelp) or crowdsourced, with automated scraping as a backup. Users might also contribute context, like noting when a restaurant changed ownership or a product had a known defect.
Such a tool could help consumers avoid products with declining quality, assist researchers in spotting industry trends, and even enable businesses to monitor competitors. However, challenges include:
One way to test the concept would be to build a minimal version focusing on a single category (e.g., electronics) with limited data sources. Over time, the tool could expand to include more categories, automated data collection, and monetization options like affiliate links or premium analytics for businesses.
By making rating trends visible and interpretable, this idea could add a new layer of transparency to consumer decision-making.
Hours To Execute (basic)
Hours to Execute (full)
Estd No of Collaborators
Financial Potential
Impact Breadth
Impact Depth
Impact Positivity
Impact Duration
Uniqueness
Implementability
Plausibility
Replicability
Market Timing
Project Type
Digital Product