OpenTable Uses AI to Track Dining Habits and Share Insights with Restaurants

OpenTable, a leading restaurant reservation platform, has integrated AI technology to track customer dining habits and provide insights to participating restaurants. These insights include data on drink preferences, spending patterns, and other behavioral information, which is categorized into AI-assisted tags. This development has sparked discussions regarding privacy, as users can manage their data through privacy settings.

Some users have reported that the insights generated by OpenTable may not always be accurate. For instance, a single business dinner could be misinterpreted as a pattern of high spending, while dining with friends who order cocktails might label an individual as a cocktail lover. Despite these potential inaccuracies, OpenTable claims that the AI technology is used for high-level classification of anonymized data, ensuring that no personal guest data is processed.

The AI-assisted insights aim to help restaurants enhance their services, such as suggesting dishes or adjusting the pace of dining. However, the system does not collect personal data on individuals, focusing instead on aggregate information. Users can control their data sharing by adjusting their privacy settings, which also influences the insights shared with participating restaurants.

OpenTable emphasizes that the use of POS system data is optional and regulated by the privacy settings chosen by users. By turning off the ‘Point of sale information’ setting, users can prevent their order history from contributing to AI-generated insights. This feature allows individuals to manage their data sharing, ensuring that their dining habits remain private and not followed to other restaurants.

This development highlights the increasing role of technology in personalizing the dining experience, while also raising important questions about data privacy and user consent. OpenTable’s approach to data collection demonstrates a balance between enhancing customer service and prioritizing user privacy, providing a model for other platforms to consider as they navigate similar challenges.