OpenTable, a popular online reservation platform, has introduced AI-assisted tags for customer profiles, which inform restaurant staff about guests’ dining preferences based on previous orders and spending habits. Notably, hosts may see indicators that a customer frequently orders specific drinks, spends beyond average, leaves reviews, or cancels reservations last minute.
Kat Menter, a host at a Michelin-starred restaurant, highlighted these tags, sharing insights via social media. While these tags offer anecdotal insights, concerns arise regarding their accuracy since they rely on data aggregated from multiple dining experiences, including information from friends or business dinners that may not reflect the individual’s preferences.
OpenTable has functioned as more than just a reservation service; it integrates various features for restaurants, such as marketing, waitlist management, and inventory tracking linked to point of sale (POS) systems. This integration allows OpenTable to collect data on customer behavior, including preferred drinks and spending patterns, unless users specifically opt-out of sharing their POS data.
Despite the intriguing nature of AI-assisted insights, restaurant staff may treat them as unreliable, as the tags can include generic or misleading data. For example, a person dining with high spenders could be incorrectly labeled as a high spender themselves. Menter cautioned fellow staff to view these tags with skepticism.
OpenTable, although it has the ability to gather extensive data, has its limits regarding personal information and provides an option for users to manage their privacy settings. Meanwhile, its primary competitor, Resy, shares different data types, but its policies prevent sharing sensitive dining history with unrelated restaurants.
In conclusion, while OpenTable continues to refine its technology aimed at enhancing the dining experience, the nuances of personal preferences may still require a human touch.
Source: https://www.theverge.com/report/822110/opentable-ai-assisted-data-restaurants