The 'Taste' App Has Locals Pick Favorites Rather Than Reviewing
Michael Hemsworth — July 27, 2017 — Tech
References: wehavetaste & betalist
It's commonplace to look online or on an app to find a place to have a meal and read reviews, but the 'Taste' app looks to go against this in order to help users save time when it comes to finding the best spots to hit.
The app works by having locals pick their favorite spot to hit for specific things: this could include their go to spot for the best coffee or the best burger. The more users go out, the more clout they'll have in order to have their say in the competition to naming the best spots for food, drink and much more.
The 'Taste' app helps eliminate the endless review reading that can come with trying to find a spot to eat out to provide you with the best information in the simplest way.
The app works by having locals pick their favorite spot to hit for specific things: this could include their go to spot for the best coffee or the best burger. The more users go out, the more clout they'll have in order to have their say in the competition to naming the best spots for food, drink and much more.
The 'Taste' app helps eliminate the endless review reading that can come with trying to find a spot to eat out to provide you with the best information in the simplest way.
Trend Themes
1. Crowd-sourced Restaurant Suggestions - Platforms that allow for locals to suggest their favorite restaurants for specific food categories can disrupt traditional review-based apps.
2. Gamification of Restaurant Recommendations - Apps that incentivize users to provide recommendations or compete for the 'best spot' can revolutionize restaurant searching experiences.
3. Hyper-localized Restaurant Search - Tools that use hyper-local recommendations to surface the best spots can enhance user search experiences while eliminating redundant information.
Industry Implications
1. Food and Restaurant Apps - These apps need to incorporate new features like gamification and crowd-sourced recommendations to adapt to shifting user preferences.
2. Travel and Local Search - Hyper-localized recommendations can be applied to other industries like travel and local search, providing better results based on the user's location.
3. Crowd-sourcing Platforms - These platforms can leverage the power of localized, community-driven data to provide recommendations for other industries beyond restaurants.
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