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.