"Is personalizing restaurant recommendations without editorialized reviews or score averaging an entirely new concept? No. Not by a long shot. But those who have demonstrated success have built their recommendation platforms with a heavy reliance on "what people tell the world" (i.e., other people's opinions and social media activity) which may have been groundbreaking 5-10 years ago but that was in the pre-bot browsing era and prior to online social media activity (i.e., likes, shares and comments) being monetized and used as a means to an algorithmic end.
Audience Effect tells us that online public behavior is not a perfect reflection of genuine interest or feeling. The mere fact that online ratings are for the benefit and attention of others, demonstrably influences those ratings and reviews. That's one of the reasons we’ve found anonymous first-party granular data to be the most reliable, informative and immediately actionable data out there.
3 out of 4 Americans now consider themselves"foodies" and many are willing to adopt a more philanthropic approach to restaurants in exchange for the critic-culture mindset that plagued the industry with challenges that literally endanger millions of independent restaurants that are now also dealing with the hardships associated with 2 year global pandemic and current geopolitical events.
Traditional public rating platforms still encourage guests to issue one-size-fits-all scores to restaurants like food critics but modern technology now affords us the ability to analyze personal taste and help people find more restaurants they'll love without having to build another public review soapbox that serves more as a place for people to air their grievances than it does to build bridges between customers and restaurants that they'll enjoy.
Using first-party data to predict compatibility is not only doable - it's a more practical approach to making customers happy... and much more restaurant-friendly as well."