The foodservice industry is one of the largest and most dynamic markets across the globe. It’s a key contributor to the economic output of many countries, and yet also highly fragmented and challenging to penetrate. From restaurants to catering services, delivery operators to manufacturers, consumer eating habits are changing constantly, forcing businesses to adapt and stay one step ahead of their competition.
Brizo offers comprehensive insights to help food-industry professionals make smarter, data-driven decisions. It provides a board and diverse set of data, allowing for highly targeted research and prospecting of the foodservice market. Its menu data and restaurant tech coverage offer unbeatable industry insight for salespeople, marketers and entrepreneurs.
In this article, we provide answers to some of the most frequently asked questions around consumer eating habits, so you can understand, strategize, and optimize your operations for success.
What factors influence consumer eating habits?
There are myriad factors that contribute to consumer eating habits. The most obvious is food itself—what’s currently trending, what’s healthy, what’s appealing, affordable or convenient. However, there are other factors that have an influence, such as social media. People of all ages are increasingly influenced in their dining decisions by online content and recommendations from their family, friends and colleagues.
Location is also an important factor when it comes to eating habits. Consumers in different parts of the world, such as the US, Europe, or Asia, will each prefer different types of food, as will people in different parts of the same country. Additionally, seasonal factors such as Summer or holidays will also shape customers’ diets in certain locations.
What data can be used to understand customer’s eating habits?
There are various data-driven strategies to understand customer’s eating habits. Restaurant orders, meal type, frequency of eating out, and location are all measurable factors that can be used to understand eating habits. Additionally, manufacturers, restaurant owners and marketers can use market research to understand changing tastes and preferences. This includes surveys, focus groups, social media sentiment and even observational studies.
Furthermore, technological developments in recent years have enabled more in-depth insights into consumer eating habits. The rise of mobile apps and electronic POS systems now give companies valuable data on what customers actually purchased, rather than what they say they did in surveys. Connecting POS data to product analytics, in particular, helps companies understand which products are most popular at different times of day, in different locations or with different demographics. Ultimately, understanding customer preferences helps operators and providers come up with creative, yet effective strategies to attract, convert, and retain customers.
What technologies are used to understand customer’s eating habits?
Data-driven technologies have revolutionized the food industry in recent years, giving restaurants, caterers and manufacturers greater insight than ever before into customer’s eating habits. Things such as location data (GPS and Wi-Fi), ordering data (mobile apps and electronic POS systems), analytics tools and artificial intelligence are all being used to gain insight into customer eating habits.
Location data can be gathered via GPS and Wi-Fi connections and shows a customer’s movement and behavior as they move in and around the area. This includes the frequency of visits, as well as the type of food they order and locations they choose to eat.
Ordering data, in the form of mobile apps and electronic POS systems, provides operators with valuable information on what customers actually purchased versus what they said they did in surveys. Connecting POS data to product analytics gives operators a full picture of what items are most popular at different times of the day, in different locations or with different demographics.
Analytics tools give operators a deep understanding of consumer preferences—by using machine learning and predictive analytics, companies can understand customer behaviors over time, come up with more targeted product strategies and be better prepared to meet customer needs.
Artificial intelligence can also be used to monitor and analyze consumer’s dining habits more precisely and accurately. It’s especially useful to discover survivors to particular events, such as natural disasters or pandemics, and can help create highly nuanced marketing campaigns for the foodservice industry.
