Selling food for profit dates back to the evolution of human society itself: from outside eateries in ancient Rome and China to the evolution of fine dining in France to the mushrooming of the fast-food industry, drive thru, and takeaway inns in the last decade. Restaurants have changed along with the demands of the consumers. The blurring cultural and demographic boundaries—enabled through globalization and technological advancements—have brought together the culinary varieties of the world. This has also enabled gourmets and food lovers around the world to feast at their convenience—a convenience of time, place, and budget.

Over time, the restaurant industry has moved away from seeking paper feedback in the form of cash and checks to electronic bills and payment methods. Advancements in information and communication technology have also affected the way consumers approach a restaurant and its related activities. While the use of technology has greatly helped increase the reach and scale of restaurants, it has also generated newer areas for restaurateurs to explore and tap into. One such avenue is data analytics.

The landscape of data analytics across restaurant industry

Digital interactions and transactions have opened the floodgates for customer and restaurant data, which can be used in both macro and granular analyses across different levels: customer, restaurant, and market. For instance, restaurants are now anazlying menus to find the most popular dish, customizing coupons for repeat customers, refining discounts to attract new consumers, optimizing inventory planning and replenishment, and doing social media analytics to gauge customer sentiment. However, most restaurants still rely on Excel spreadsheets rather than big data analytics.

Forward-looking companies have tested the waters of big data analytics for overall business benefits. For example, Darden Restaurants uses data to detect fraud, optimize menu prices, and study duration of diner visits. It expects to save big bucks, to the tune of $20 million. The Cheesecake Factory, with more than 180 restaurants in 175-plus locations across the U.S. and three licenced locations in the Middle East, has also started leveraging big data analytics to provide better food and a better customer experience. Subway has also channeled its focus and attention on analytics.

Types of analytics performed or trending in the restaurant space

1. Testing and learning

Fast-food restaurant giant Subway is a staunch proponent of “testing and learning.” In an article by the Wall Street Journal, CMO Tony Pace discussed how marketing innovation is driven by a consistent process of experimentation and how staying ahead is all about coming up with hypotheses and testing them.

In practice, a restaurant can test a program in one location and then, depending on its success, choose to apply the program at other locations and across various levels, including menu, customer, and restaurant. This approach can also optimize operations, pricing strategy, and resource allocation. The test-and-learn approach can be employed to determine the optimal price hike of an item before people stop buying it.

The benefits of this approach can be multifold: It creates a true measure of the performance differential between two or more strategies, it helps control the cost and risks that arise when a new strategy is implemented, and it encourages continuous improvment through its circular process.

2. Menu analytics

Restaurant menus are fertile ground for analtyics. Menus can be evaluated by text appearance (fonts, colors, placement), complementary items, and layout using transaction and order history.

A recent study conducted by Cornell University finds that food items that were highlighted using bold or colored text or that was inside a text box drew more eyeballs and were more likely to be ordered. A few extra words in the description—“succulent Italian seafood filet” instead of “seafood filet”—translated into increased sales. Diners were even willing to pay more for that very dish.

Menu analytics can have far-reaching benefits, whether in identifying complementary menu pairings, creating discounts for winning combinations of such items, or identifying non-performing items and removing them.

3. Customer-level analytics

It is paramount that restaurateurs be able to segment customers using the right parameters and thus, truly understand their customers. Segmentation based on trip involves determining which customers come for just one item, a full meal, or a snack.

Segmentation based on visit pattern examines how customers visit the restaurant at different times of the day. Given these patterns, it is prudent to offer differentiation in menu or customize items for different parts of the day. While certain hot-selling dishes can be sold throughout the day, customers should be infused with the idea of ordering certain dish at certain times of the day.

Segmentation based on demographics encompasses a customer’s age, gender, marital status, income level, and even type of credit card. With these details, operators can easily tailor recommendations and special offers.

With better customer segmentation, designing marketing campaigns and putting customer loyalty programs in place become a piece of cake.

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4. Sales analytics

It is a given that all restaurants want to predict demand and sales. By analyzing historical sales trends and taking into account seasonal variances and other constraints, restaurants can effectively do that. These analyses empower restaurateurs to optimize their inventory and better forecast demand swings. The data can also optimize staffing by considering shift preferences and maximum-hour limits for crew members.

When combined with location-based analytics, sales analytics can be taken to a whole new level. By combining primary and secondary data on restaurant sales, companies can also analyze the sales of other restaurants in the same vicinity. This comparison can benchmark one’s own performance against competitors and identify the factors responsible for any sales dips.

5. Promotional and feedback analytics

By combining reviews, feedback, and social media data, restaurant managers can effectively evaluate and gauge their service levels. These analytics can also help them understand whether customers are receptive to special offers, and if so, which ones. Furthermore, managers can identify the key influencers by running algorithms. Instant feedback from analytics also allows for timely intervention in resolving customers’ complaints and preventing poor impressions. Analysis on cumulative data can also unearth hidden insights which otherwise could be missed, such as which food item is losing its popularity or which particular food chain is receiving negative remarks and for what reasons.

Given that the restaurant business has low margins but is cash-intensive, analytics can offer a definitive business advantage. While the aforementioned techniques are just a starting point, organizations stand to benefit by making analytics integral and pervasive in their ecosystem.

With the right ecosystem in place, restaurants can look to people with the right analytical tools and techniques to sieve through data and draw actionable insights. Across the spectrum, the analysis should be: descriptive, or telling them what happened or is happening; inquisitive, or helping them understand why it’s happening; predictive, or informing them of what’s going to happen; and prescriptive, or giving clear recommendations for next steps. These efforts all become futile if companies discount the importance of real-time decision-making on the basis of insights generated. This is perhaps the most critical part of the whole analytics equation.