What Is a Recommendation Algorithm?
A recommendation algorithm is a technology used to suggest products or services based on customer behavior data.
In the F&B industry—be it restaurants, coffee shops, catering services, or online food businesses—these algorithms help suggest the most relevant menu items based on order history, preferences, and ordering trends.
For example, food delivery apps use recommendation algorithms to show menu items frequently ordered by nearby customers. Similarly, in restaurants, a smart POS system can suggest add-ons like extra toppings or popular drinks often paired with a main dish.
How Do Recommendation Algorithms Work?
Recommendation algorithms in the F&B space work by analyzing customer data and purchase patterns to suggest the most relevant menu items. This process typically involves several key steps:
1. Data Collection
The system gathers a variety of customer data, such as:
- Order history → Menus frequently ordered by a customer.
- Ordering time → Ordering trends based on hour, day, or season.
- Customer location → Restaurants or stores they often visit.
- Payment method → Types of transactions used, such as cash, card, or e-wallet.
- Reviews and ratings → Dishes or drinks that receive high customer ratings.
2. Data Processing & Pattern Analysis
Once data is collected, the algorithm identifies patterns to generate recommendations. Here are the three main approaches used in the process:
Content-Based Filtering (Suggestions Based on Customer Preferences)
The algorithm compares items a customer has ordered with other menu items that have similar characteristics.
For instance, if a customer frequently orders matcha latte, the system might suggest a matcha frappuccino or matcha cake due to the shared base ingredient.
Collaborative Filtering (Suggestions Based on Other Customers’ Behavior)
The system looks for other customers with similar ordering patterns and recommends items they tend to like.
For example, if many customers who order burgers also tend to order milkshakes, then a new customer who orders a burger will be recommended to try the milkshake as well.
This model works in two ways:
- User-based filtering → Finds customers with similar preferences.
- Item-based filtering → Finds menu items that are often ordered together.
Hybrid Model (Combining Both Methods)
This approach blends content-based filtering and collaborative filtering to deliver more accurate recommendations.
For example, if a customer frequently orders coffee, and the system finds that other customers with similar tastes also enjoy croissants, it will suggest a croissant as an additional item.
3. Generating Recommendations & Personalization
Based on the analysis, the system displays menu suggestions tailored to each customer's preferences. These recommendations can appear in various forms, such as:
- Menu suggestions in food delivery apps → Like what you see on GoFood, GrabFood, or ShopeeFood.
- Upselling at POS-based counters → The system automatically suggests add-ons or extras during ordering.
- Email or push notifications → Businesses can send special promos based on a customer’s purchase history.
4. Evaluation & Continuous Improvement
The system keeps learning from customer interactions and updates its suggestions based on:
- Customer response to recommendations → Did they actually purchase the suggested item?
- Seasonal and trend shifts → For instance, iced coffee may be recommended more often in summer, while hot drinks are favored in the rainy season.
- Customer feedback → If many users give low ratings to a suggestion, the algorithm can adjust future recommendations.
Examples of Implementation in F&B
- Quick Service Restaurants → McDonald’s uses recommendation systems to suggest meal packages based on past customer orders.
- Coffee Shops → Starbucks relies on algorithms to recommend popular drinks to customers with similar preferences.
- Catering Services → Online catering platforms can suggest weekly meal plans based on customer consumption patterns.
- Online Ordering Platforms → GoFood and GrabFood recommend menus based on location and popular items in the area.
By continuously learning from customer data, F&B businesses can offer a more personalized experience and boost sales potential.
Benefits of Recommendation Algorithms for F&B Businesses
Boosting Sales through Upselling & Cross-Selling
The system can suggest additional menu items or value meals, encouraging customers to add more items to their order.
Personalizing Customer Experience
Customers feel more seen and valued when they receive menu suggestions that match their preferences, increasing loyalty toward the business.
Optimizing Inventory Management
By understanding ordering patterns, businesses can forecast demand and manage ingredients more efficiently—reducing waste in the process.
Improving Service Efficiency
In restaurants with self-order or online ordering systems, automated recommendations can speed up the ordering process without needing staff interaction.
How Can F&B Businesses Implement Recommendation Algorithms?
Use a POS or App with Smart Recommendation Features
Choose a system that can analyze sales data and automatically provide suggestions—such as a POS system that recommends menu items based on customer transactions.
Integrate with Online Ordering Services
If your business has a website or app, make sure it includes features that display product suggestions based on past orders.
Leverage Customer Data
Ethically collect data through loyalty programs or membership apps, then use that data to deliver more personalized menu recommendations.
Offer Promos Based on Customer Habits
For example, customers who frequently order certain dishes can receive discounts or special deals on similar products.
Conclusion
Recommendation algorithms aren’t just marketing tools—they’re smart strategies for enhancing customer experience and operational efficiency in F&B.
With this technology, restaurants, coffee shops, catering services, and even cloud kitchens can provide more relevant suggestions, increase sales, and build stronger customer loyalty.