Exploring How OpenTable Utilizes AI to Analyze Dining Habits

When dining out, individual preferences, such as ordering sparkling water or always starting with an appetizer, often go unnoticed. However, restaurant reservation platform OpenTable is actively tracking these habits for many diners.

Recently, advancements in technology have led to some restaurants introducing new AI-assisted tags that log dining behaviors when patrons reserve a table. These tags monitor aspects like drink choices, spending patterns, review habits, and even last-minute cancellations.

The discovery of these tags came from Kat Menter, a host at a Michelin-starred restaurant known for her culinary insights under the name Eating Out Austin. By sharing her experiences on TikTok, she captured the attention of food enthusiasts and media outlets confirmed the implementation of this feature across multiple dining establishments.

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OpenTable seamlessly integrates with point-of-sale (POS) systems like Toast and Epos, which manage orders, payments, and meal timing. When diners’ contact details align with their OpenTable accounts, the platform links their visit data to their profiles.

This data can include the time of arrival, specific order details, duration of stay, and total bills. Reports indicate that such information enables OpenTable to generate AI summaries from non-identifiable guest data, provided that the restaurant utilizes a compatible POS system and has chosen to enable data sharing. Importantly, this tracking does not necessitate reservations being made directly through OpenTable; one only needs an account with consistent contact information.

Interestingly, users who have accessed their data via OpenTable’s privacy request form indicated they had limited insights available—mostly basic contact information and a list of prior reservations. This suggests that the depth of insights accessible hinges on the restaurants’ POS integrations and the duration for which they have been utilized.

Understanding Guest Preferences in Hospitality

For years, restaurants have been cataloging guest preferences, taking notes on favorite dishes and preferred seating arrangements. Staff are also attuned to patterns of frequent lateness or repetitive celebrations, refining the dining experience along the way.

The goal behind OpenTable’s AI-based guest insights is to provide a simplified version of these observations. By highlighting drink categories, spending ranges, or behavior patterns, the platform aims to facilitate improved service. Nonetheless, Menter cautions that the AI tags may not always accurately reflect reality. A single high-value business dinner can classify someone as a frequent spender, while dining with friends who indulge in cocktails could skew an individual’s profile as a cocktail aficionado. For this reason, Menter treats the insights as approximate suggestions rather than definitive conclusions.

The Role of AI in Data Handling

OpenTable asserts that its AI functions independently of personal guest data. Instead, it focuses on a high-level classification and systematic categorization of extensive, anonymized data sets. For example, the AI evaluates various point-of-sale terms (such as “glass of cabernet”) and consistently categorizes them into broader groups like “red wine” or “white wine,” while avoiding any direct interaction with specific guest profiles.

This functionality is not merely for efficiency; it aims to enhance the dining experience. OpenTable representatives argue that the insights derived from AI can guide servers in recommending dishes or adjusting the dining pace. Moreover, the platform stipulates that the use of POS data relies on individual privacy settings, which users can adjust or opt-out of at any time. Nevertheless, the privacy policy encompasses broad terminology, especially concerning dining preferences.

As an OpenTable representative stated, the insights serve as the bedrock of personalization within the dining experience. They enable establishments to optimize their services and provide a memorable atmosphere beneficial to both the business and diners. These insights are curated from a mix of sources, including OpenTable, restaurant partners, and POS collaborators, with a strong emphasis on non-confidential data.

Data Sharing Practices and Privacy Control

For diners who choose to opt-in, OpenTable shares their name, contact details, party size, and any special requests with the restaurant. Additionally, participating restaurants provide POS data to OpenTable, which can encompass items ordered, bill totals, and dining duration, transforming this information into aggregated insights.

It should be noted that OpenTable shares these insights exclusively with restaurants in its larger network, applying only where the feature is enabled and limited to those under the OpenTable Pro plan—a feature currently in beta testing.

For those seeking greater privacy, it is possible to disable the “Point of Sale Information” setting, which ceases the inclusion of order histories in future insights.

Navigating Technology and Personal Data

Dining experiences have evolved through technology, leading to new layers of complexity. With the introduction of AI-assisted tags, restaurants gain an enhanced understanding, yet diners may feel apprehensive about how their behavior is recorded behind the scenes. By scrutinizing privacy settings and opting out of POS data sharing, individuals can assert greater control over their dining data.

Being cognizant of what is being tracked empowers diners to enjoy their outings without unnecessary concern about how their data might be utilized. With minor adjustments to privacy settings, individuals are well-positioned to shape how restaurants perceive their dining preferences, preserving a personalized dining experience.

Would your dining habits change if you realized your ordering tendencies followed you to unfamiliar restaurants? Share your thoughts by writing to us at Cyberguy.com.

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