How can Restaurants use Data Science?

Nowadays, many restaurants are deploying modern and advanced techniques to reduce operating costs, reduce food waste, optimize business operations, and increase overall business efficiency. But how can restaurants use data science to become more competitive? This article discusses 7 potential ways data science could be used by restaurants.

  • Forecasting Sales

Data Science techniques might be used to forecast the future restaurant sales based on historical sales data collected over the past years. A suitable data science technique will have to be determined as different sales data sets require different algorithms to forecast future sales with an acceptable level of accuracy. However, forecasting sales remains a crucial step because it helps the management of the restaurant determine the best time of the year to make investments in improving the restaurants as well as it helps determining high seasons and accordingly adjust staff numbers, food supplies, and many other variables related to a restaurant operations.

  • Determine a Good Location for a New Branch

Determining if a new location is suitable for opening a new restaurant branch can be a seriously complicated task. Many variables must be taken into consideration before opening a new restaurant branch. However, the usage of data science can significantly help determine the best location for a new branch by helping to analyze multiple variables.

  • Reduce the Waste of Perishable Products

Most restaurants try to minimize the amount of wasted raw materials. Because less wasted raw materials means less costs and leads to more profits. Nevertheless, determining the appropriate number of raw materials (Such as vegetables) to buy on every business day remains a huge issue for many restaurants. Buying too much would make them lose a big quantity of the perishable products, and buying not enough would cause shortages that would decrease customer satisfaction. This is where data science can play a big role. The sales forecasts mentioned earlier can be modified to provide information about the quantity of raw materials needed on a specific day.

  • Optimize Number of Staff

Having a lot of staff members in a restaurant, can be a necessity in some cases. Especially, in order to reduce the waiting time of a customer ordering food. However, having an excess in staff members might become problematic when it comes to wages. This is where data science can help in determining the appropriate number of staff members needed in each business day based on sales forecasts. Based on sales forecasts the management of the business might create a combination of staff members from full-time staff and part-time staff in order to maximize efficiency while keeping costs acceptable.

  • Improve Sales by Getting to Know the Customer

Many restaurants try to adapt their menu based on their customers. But to successfully adapt the menu based on their customers, the restaurant management needs to know its customers. One important thing is that the management needs to know their customers preferences, and to get this information many restaurants rely on a customer feedback form distributed to customers. This type of form can include many types of questions providing both qualitative and quantitative data based on which the management can work on improving the menu and adapting to the customers.

  • Assess the Performance of Each Staff Member

Similarly, based on customer feedback forms, the management can assess the performance of each staff member and accordingly provide rewards, and bonuses to the best performing employees in order to boost their motivation and further improve customer experience.

  • Boost Loyalty Programs

Data science can also be used by restaurants to establish loyalty programs for their most loyal customers. Data might be collected for each customer based on a loyalty program to determine how much discount is the customer eligible for on each bill or on specific items on the menu.

No-Responsibility Disclaimer: This blog provides general information related to Data Science and its applications in Businesses. The content provided in this blog and any linked material is NOT an advice, and NOT a recommendation. And the author is NOT liable for any problem or unexpected results that might result from the information provided in this blog post. Similarly, the author is NOT responsible for any wrong information mentioned in this blog post.