How Data-Driven Decisions Help Restaurants Stay Competitive

Restaurants today don’t look much different than they did two decades ago — tables and chairs in the front and a kitchen in the back. At first glance, you wouldn’t know that this enormous industry (nearly $937 billion in annual revenue in the U.S. alone in 2022) is in the middle of an exciting, data-driven transformation in adapting to changing customer expectations and intensified competition from new business propositions, such as cloud platforms. In fact, IT is an increasingly important part of how restaurants create value, from how consumers choose a place to eat, make a reservation, give their order, and pay their bill to how they keep their memory of their evening out and share it with their friends. Customers generate data in almost every step along their journey, ranging from their channel preferences and mode of reservations to valet parking, point-of-sale (POS) records, and feedback systems. On the supply side, detailed preparation and food resource–management records enable restaurants to optimize their inventory and reduce waste. Overall, the volume of useful data to manage customer experience along with profitability has multiplied. This rich buffet of data provides restaurant managers with a wide variety of novel opportunities and business models, such as “ghost kitchens” (industrial kitchen spaces that only offer delivery service) and customer data mining. “We use data to delight customers — leverage data to offer a personalized menu and reduce their wait times during peak hours through better labor and menu management,” the COO of one restaurant chain told us. While restaurants have jumped on the digital bandwagon to enhance customer convenience and manage operations, the opportunities to harness the potential of the captured data are limitless. Ignoring these opportunities can be dangerous. To remain competitive, restaurants need to change the way they approach business decisions; they need to shift focus from food cost to revenue management and exploit opportunities for scaling up. How can they make that happen? Based on our research on how restaurants could leverage smart technologies and data analytics, we offer six strategies to guide strategic and operational decisions: 1. Tap into publicly available “intelligence” to determine where to open your new restaurant. Location is the primary factor predicting restaurants’ success or failure. Big chains such as Starbucks already use business intelligence platforms to assess potential store sites based on consumer demographics, competitors, population density, income levels, car traffic patterns, credit card transaction histories, etc. Today, restaurants can go one step further and add data from social media platforms and menu search requests (e.g., using text mining, sentiment analysis, and other learning techniques) to the mix, enabling them to extract consumer preferences, predict competitors’ entry/exit, and decide on their next successful restaurant location. 2. “Cherry-pick” among reserving customers. Revenue management in restaurants has been far less developed than in other service sectors such as hotels and airlines. Restaurants typically simply accept reservations until the available capacity is full. Sometimes they actively discourage large parties from dining by quoting long waiting times or not allowing online reservations, because dining duration tends to be longer for larger groups while the spending per person is lower. Nowadays restaurants can use data from the reservation platforms and POS to gain more detailed customer insights, and better select which customers they want to accept in popular slots. For example, they can select the most loyal customers, customers with the most spending potential, or customers who are most likely to positively impact the restaurant’s reputation. 3. Smartly manage customer queues. All restaurants that accept walk-in customers face queues at the most popular times. A waiting line can serve as a signal of restaurant quality but also create customer dissatisfaction. Modern POS and queue-management systems offer aggregate and more fine-grained data on queue lengths and waiting times, which combined with sales and labor data can offer unique insights on the impact of waiting time on customer and staff behavior. Predictive analytics can now determine the potential tipping point, at which the potential positive impact of queues turns negative and informs capacity and labor decisions. Differential pricing based on prescriptive analytics can be used to reduce the wait for customers with the highest reservation price. 4. Forget the round-robin (RR) seating rule. Customers in a restaurant are traditionally seated by the host based on simple rules, such as the RR rule where parties are assigned to zones, each served by a team of waitstaff, in circular order without priority. Restaurants can now use historical data to estimate the impact of customer and staff characteristics on speed, spending, and customer satisfaction, and subsequently design targeted seating policies based on real-time information. For example, when the workload is high, hosts can prioritize smaller party sizes and assign them to waitstaff with higher speed skills to increase throughput. An analysis of a large casual dining restaurant chain in a major U.S. metropolis revealed that simple deviations from the RR rule, based on waiter’s workload and speed, could increase total sales by 9%. Alternatively, managers can assign waitstaff with more experience and cross-selling skills to increase sales and customer satisfaction when customers have waited long in line to be seated. Such practices create personalized experiences while also maximizing efficiency. 5. Create dynamic and personalized menus. Digital ordering — whether it’s through a website, an app, a tablet, or simply a QR code — is everywhere. With digital ordering, restaurants can capture richer sales data, such as order item timestamps at the customer level, menu navigation patterns, and real-time customer behavior (e.g., items already in the basket). Hence, digital menus offer a huge potential for personalized recommendations, similar to online retailing, based on customer dynamic micro-segmentation. For example, restaurants can combine customer data with real-time operations information (e.g., items in preparation) to provide personalized recommendations that increase both customer satisfaction and kitchen efficiency. Based on advanced data analytics and machine-learning algorithms, restaurants can also dynamically update their menus to increase sales while reducing food waste. For example, restaurants can update their menu bundles and offer discounts based on ingredient availability, kitchen workload, and external circumstances (such as weather and events). 6. Efficiently balance between multiple order channels. With increasing popularity of online delivery platforms, restaurants today need to balance between multiple customer streams — e.g., dine-in, takeout, and delivery at home. To do so successfully, restaurateurs need to identify how orders from different channels interact and affect capacity utilization and bottlenecks (e.g., kitchen versus seating area). For example, restaurants need to be able to estimate the effect of online orders and pricing on dine-in waiting times, customer experience, and revenues. Using past demand and service data in combination with simulation models and network queuing analysis, restaurateurs can decide when to accept/reject customer online orders for maximum profitability. Depending on the number of accepted orders, restaurateurs can also estimate the workload to further optimize the staffing levels and, in the case of chains, assign home delivery orders to a specific location based on their real-time delivery capacity. The special today is…you Beyond their usefulness for decisions about a restaurant’s operation, restaurant patron data and analytics are becoming valuable assets for other industries as well. Just as restaurateurs are finding outside data valuable to them, other industries are learning about their customers from the restaurants they choose. Theoretically, this should all be good news for restaurants, but no free lunch is served: If the reservations, queueing, table assignment, ordering, utilization, staffing, inventory, and payment management have already been outsourced to external platforms, restaurants may be left with very little leverage. “Restaurants have to think about data ownership,” one executive working in restaurant apps advised. “Restaurants do not care about their data, where it is stored, who owns the data, and who does the analytics.” If they aren’t careful, the executive continued, they could be giving away a lot of value, especially to POS providers who “are in the position to offer analytics as a service if they aggregate large amounts of data.” The restaurant business has always been tough, but the reasons why are evolving. Instead of the traditional uncertainties, such as inventory and location, tomorrow’s restaurateur’s biggest worries may have to do with whether they have priced their own data too cheaply, how to make the most of the data they have available, and what questions they should be asking the data about their customers. Best practices in the digital era, such as “cherry picking” reservations, managing virtual queues, and balancing between customer channels are all relevant for the broader service industry (such as hospitality and entertainment).

How Data-Driven Decisions Help Restaurants Stay Competitive

Summary.

Partner Center