How can restaurants use data science to grow post-pandemic?

The restaurant industry is currently stranded on an island that is slowing sinking into the ocean. The ones who start building themselves a boat are the ones who have a chance at discovering new dry land before it is too late.

Many restaurants are evolving into cloud-kitchens whereas many are still operating as conventional brick and mortar shops. No matter the choice of boat, the restaurant industry will always need its customers to paddle in the right direction. Thus, this article talks about how a restaurant can elevate its business processes to better cater to its customers through the power of data science.

Set up a data collection and management pipeline

Data should be the underlying basis for decision-making in all businesses including restaurants. However, most restaurants fail to understand this distinction because they do not know where to begin.

The first step in being able to implement data science is to have a manageable system in place for storing and handling data collected from all possible channels of interaction. Some examples of data that can be collected by a restaurant are data obtained from restaurant orders, customer feedback, ratings, the waiting time for order fulfilment, tip received, billed amount, etc.

By performing an early data audit, restaurants can make sure that a proper data collection and management pipeline is in place and that they do not leave out any valuable information behind for their business. This also leads to the possibility of performing advance data analytics and training Machine Learning models in the future.

Use customer analytics to improve customer experience

Upon ensuring that a proper data collection channel is in place, it is now time to analyze the historical data points of customers to gain insight about them and their preferences. This is also known as Customer Analytics.

A general process of Customer Analytics applies an STP Framework that stands for Segmentation, Targeting, and Positioning.

Segmentation is the process of dividing a population of customers into groups based on the similarity of demographics or behaviour. The segmented groups are likely to have common purchasing behaviour and respond similarly to marketing and promotional activities.

Targeting involves analyzing the segmented group of customers to better understand their similarity pin-points in order to run personalized marketing campaigns, test the launch of a new dish, provide incentives, find up-sell and cross-sell opportunities, etc.

Positioning is the process of modelling a restaurant based on the customer’s needs. It defines the way that a restaurant should be presented to the customer such that it connects with their requirements.

In an ideal scenario, all employees of a restaurant would be equipped with real-time analytical reports of recurring as well as new customers while they walk in through the door in order to provide an elevated customer experience.

Understand how other restaurants are adopting Customer Analytics – A case study of McDonald’s

McDonald’s has established itself as a prominent leader of the fast-food industry over the duration of 80 years. The primary motive of the company has been to provide fast service and affordable meals to its customers. A strong focus on customer service and marketing techniques since the beginning has aided the significant growth of the company.McDonalds restaurantImage from Source

In 2019, in an interview with CNBC Television, McDonald’s CEO Steve Easterbrook described how the company has been working on building the technology infrastructure. With technological advancements, McDonald’s has introduced mobile apps, digital kiosks, and drive-thrus at their restaurants and malls. This meant that a lot of new customer data was being recorded along with the traditional data from their restaurants.

One of their major investments in data science has been on acquiring Dynamic Yield, an AI-based company that specializes in customer personalization software using predictive algorithms. The acquisition indicated how McDonald’s has been continuously looking into leveraging its data through customer analytics in order to personalize customer experience.

Customer Analytics at McDonald’s

The Customer Analytics at McDonald’s is focused on improving customer experience, getting higher sales, dealing with demands, and research purposes. Here are a list of things that they are doing with the help of data science:

1. Menu customization: The menu items, combinations, and prices at the restaurants are carefully decided by analyzing the behaviour of customers and their orders. The frequent customers are segmented and studied to make changes to the menu periodically such that it allows the company to provide faster service and generate more sales.

Similarly, the digital menu and displays at kiosks and drive-thrus, are personalized based on the time of the day, weather, season, and popular orders to give the customers a quicker and better experience. When dealing with high demand, the menu is tailored to recommend easier and faster-to-make items to serve all the customers on time.

2. Demand forecasting: Intelligent data prediction models learn from the previous customer traffic patterns and trends to predict when the demand can arise for orders. This allows the company to prepare and improve their efficiency ahead of time.

3. Locating high-density areas: A large proportion of customers prefer the comfort of getting food delivered whenever and wherever they are. The order data from such deliveries are represented as a heatmap to gain insights about the locations with high-density of their customer population. This information is used as an important metric for determining new locations for opening their restaurants.

Conclusion

Data Analytics is a growing field of research and has been positively impacting the growth of restaurants. With more and more transactions happening digitally, there is abundant data to be analyzed to make processes more efficient. More companies in non-tech industries are realizing the importance of data which has led to advanced implementations of data science all around the world.

If you are also looking to implement data science for your business, you might want to read a more detailed article on how can businesses use data science.

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