How do you leverage data and AI to create personalized menu offers?

In the same way that e-commerce brands and supermarkets via loyalty cards track and learn more about customer preferences, capturing your customer's orders over time will allow you to build an understanding of what they like. Inferences can then be made once data has been accumulated and different items can be suggested based on past trends and analysis of similar type customers.

# Use cloud based software solution (we use GoFrugal) # On daily basis, generate sales reports and understand the quantities sold for each food items. # Import data on to smart spreadsheets visualize it using PowerBI . # Use the power of AI algorithms to analyze the pattern of food consumed by most customers, amount spent during the time and demand on special occasion # Based on the dataset at hand, Use AI's predictive analysis to provide insights to Central kitchen for food production and reduce wastage.

my trajectory is marked by a comprehensive approach rooted in data analysis, technological integration, and customer-centric strategies. I use my analytical skills to gather and analyze customer data, identifying patterns and trends that inform personalized menu recommendations. My technological expertise comes into play as I integrate AI algorithms that continuously learn and adapt to evolving customer preferences. The result is personalized menu displays that dynamically adjust based on real-time insights, ensuring relevance and appeal. With my knowledge of POS systems, I seamlessly integrate personalized offers into the ordering process, providing a smooth and engaging customer experience.

To create personalized menu offers, collect data from key sources like your POS system, online ordering platforms, loyalty programs, social media, and customer feedback. Use tools like spreadsheets, dashboards, or analytics software to process and visualize this data effectively. Many analytics tools come with AI features like predictive modeling, customer segmentation, and natural language processing to uncover patterns and suggest actionable insights, making it easier to craft targeted offers that resonate with your audience.

Encuentro útil la reflexión sobre cómo aprovechar los datos y la inteligencia artificial para crear ofertas de menú personalizadas. Un ejemplo en la vida laboral donde esto puede ocurrir es en el rol de un gerente de restaurante que busca mejorar la experiencia del cliente y aumentar la satisfacción. Ejemplo: Imaginemos que un gerente de un restaurante de comida rápida desea personalizar las ofertas de menú para atraer a clientes específicos y mejorar la retención de clientes. Aquí se aplicaría el uso de datos y herramientas de inteligencia artificial. Fuentes de datos: Sistema de punto de venta (POS): Recopilar datos de ventas para identificar los artículos más populares, horas pico y patrones de compra.

Lets look at this two ways: optimizing your existing menu and developing new items. Optimizing Your Existing Menu - You should understand your food and labor costs - If possible, you should understand the operational impact of these items. For example, number of steps involved, speed of delivery, etc. Sources for this could come from time-motion studies, etc. - You should have mechanisms to understand satisfaction with the product. This could come through social media scraping, concept testing, VOC studies, etc. For New Menu Development - Your products and categories are there to serve a role for your customer. You need to understand the occasions and needs you want to serve. You can do this through JTBD, concept testing, etc.

Leveraging data and AI for personalized menu offers involves collecting customer data, segmenting via machine learning, and using predictive analytics to anticipate preferences. AI personalization engines then suggest tailored dishes based on behavior, context, and sentiment analysis from customer feedback. Continuous learning and A/B testing refine these suggestions, which are delivered through digital platforms, enhancing customer satisfaction and driving sales, while ensuring responsible data use.

Identifying available data sources is a good start. These can be quantitative (order details, cost, margin) and qualitative (surveys, guest feedback) to guide decisions. AI tools used by data analysts/DS and automated insights (e.g., IBM Watson, Azure ML) help find patterns, trends, and correlations between dishes, location, seasonality, profitability, and preferences. Build multiple menus, run tests, iterate.

Para personalizar el menú de un restaurante con IA, se usan los datos como el historial de pedidos, las preferencias de cada comensal y momentos de consumo para sugerir platos ajustados a cada cliente. Los algoritmos de recomendación predicen tendencias y adaptan los menús en tiempo real según la disponibilidad de ingredientes e incluso el clima. Además, utiliza chatbots para guiar a los clientes y se crean campañas de marketing personalizadas basadas en comportamiento. La IA también optimiza inventarios, reduce desperdicios y analiza reseñas para innovar los platos. Esto aumenta la satisfacción del cliente y por supuesto las ventas.

By detecting patterns in your order tickets in product mixes, you could discover new combos or ways to upsell or cross-sell that not only could help you increase your average ticket value but also offer a more relevant suggestion. When the cashier (for QSR) or waiter (for restaurants) is in front of a client, they have more tools to make better suggestions, which is a win-win-win situation. The client wins because it suggests something more relevant. The waiter wins because it is likely for him/her to receive a bigger tip, and the business could make a higher ticket value.

Encuentro útil la reflexión sobre utilizar datos y la inteligencia artificial para optimizar precios y la mezcla del menú. En un restaurante, los datos sobre ventas y preferencias se pueden aprovechar con IA para sugerir precios estratégicos, maximizando ingresos y satisfacción del cliente. Además, la IA puede recomendar combinaciones de platos y diseños de menú personalizados para diferentes segmentos de clientes, mejorando la experiencia gastronómica y la rentabilidad. Este enfoque integrado optimiza la oferta del menú de manera eficiente.

Optimizing menu pricing and mix with data and AI involves tailoring your menu based on demand, seasonality, costs, margins, and customer preferences. By analyzing sales data, you can pinpoint your best-selling and most profitable dishes, while AI can suggest pricing strategies to boost revenue and customer satisfaction. AI can also test various menu layouts and variations, recommending the ideal combination of dishes, categories, and descriptions for different customer segments. This ensures your menu appeals to diverse audiences while maximizing profitability.

Here are the factors I consider when evaluating pricing on existing menus: 1. What is the role of the product and category (this matters as I may choose to strategically set a price based on its role independent of what follows) 2. While prime costs matter (a lot), its also important to understand what other costs in your operation are most variable (if a multi unit concept) and to then factor that into that units pricing level 3. Keep an eye on competition--especially for key crossover items 4. Find some type of solution (there are companies like RMS that do this) that monitors how demand changes based on pricing changes--use this to inform elasticity 5. Test new items with non-customers to ensure its appropriate for this segment as well.

Menu engineering is definitely a thing: refine the menu by removing items that don't sell well, are difficult to prepare, or eat into the profit margin. Then, conduct tests to determine the sweet spot in terms of price elasticity. Also try different menu layout, organization and colors using AI tools to be creative and analyze the results.

Remember, AI is a tool. AI doesn't not have taste buds. It can't smell the difference of your dumpster and your onion soup (hopefully there is a difference). AI can only do what you tell it to do. AI is not intelligent. It is a complex abacus. It can't personalize anything. It looks at what is already there. Your menu price should be linked directly to you food cost. That's restaurant 101. Food cost should be 1 to 4. Anything different and you'll loose money. There are other schemes, but one leads to another... and then all the dominoes are falling. However one suggestion rings true. Know your competition. So many places have their menus with prices online. That is were AI works. Locating data and spitting it out.

If you analyze consumer behaviour with AI, you will see what they usually mix; for example, every time a client purchases a chicken sandwich, he/she also takes an ice-cream. Knowing these patterns (patterns, not individual actions that do not explain a consistent behaviour), you can use it to the client's advantage to offer what is going to be more relevant. Also, it's a way for them to feel considered, plus, helps you with the upselling or cross-selling.

Encuentro útil la reflexión sobre adaptar promociones y recomendaciones del menú mediante datos y AI. En un comercio, segmentar clientes con datos permite personalizar ofertas, como enviar descuentos basados en historiales de compras. La IA puede potenciar esto al analizar patrones de navegación, sugiriendo platos complementarios según preferencias. Este enfoque aumenta la relevancia de las promociones, mejorando la experiencia del cliente y fomentando compras adicionales. La combinación de datos y AI potencia estrategias de marketing personalizadas.

In addition to traditional promotions with corresponding segmentation tools, AI can help with intelligent surprise and delight offers that will reward the guest on the spot creating memorable experiences that will have the guest coming back.

Using data and AI for personalized promotions and recommendations involves aligning offers with customers' preferences and behaviors. By analyzing purchase history, frequency, and value, you can segment customers and send tailored discounts, rewards, or coupons through personalized messages or notifications. AI can track browsing and ordering patterns in real time, suggesting dishes or add-ons that align with each customer’s tastes. This approach not only increases sales but also enhances the dining experience by making every interaction feel relevant and thoughtful.

En el ámbito laboral, un ejemplo concreto sería un restaurante que utiliza datos de opiniones y calificaciones en plataformas en línea para entender las preferencias y expectativas de los clientes. Aplicando la inteligencia artificial, podrían identificar patrones comunes en los comentarios, como menciones recurrentes de ciertos platos o áreas de mejora en el servicio. Utilizando esta información, podrían personalizar el menú, destacando los platos más elogiados, y ajustar aspectos específicos del servicio. Además, podrían implementar un chatbot en su sitio web o aplicación para brindar respuestas rápidas a preguntas frecuentes y ofrecer recomendaciones personalizadas.

Two ways to talk about this: optimizing your existing experience and building a compelling customer journey. Optimizing Your Existing Experience Through tools like VOC and social media scraping, you should understand how your designed customer experience is/is not delivering- typically, quantitative and qualitative elements. AI can be used in both to find relationships; both are penalties and rewards. For Building a Compelling Customer Journey Engage target and seek to understand problems or jobs they are trying to get done. Figure out touchpoints and how you want to deliver at those touchpoints. How do you want them to feel at those touchpoints? Then, work through solutions your team can deliver to make the customer feel that way.

Enhancing customer experience with data and AI involves making interactions more engaging, convenient, and memorable. By analyzing customer feedback, ratings, and reviews, AI can pinpoint areas for improvement or opportunities to surprise and delight guests. Data can also personalize website or app interfaces, tailoring them to each user’s preferences. Additionally, AI-powered chatbots or voice assistants can handle inquiries, take orders, and offer personalized tips, creating a seamless and enjoyable experience both online and in-person.

When implementing personalized menu offers with data and AI, consider the importance of transparency and ethics. Customers value privacy, so be clear about how their data is used and ensure it’s protected. Additionally, look to success stories for inspiration—such as restaurants that have used AI-driven insights to boost sales or loyalty. For instance, a pizza chain might use AI to predict peak order times and offer targeted discounts, leading to higher customer retention. Finally, don’t forget the human touch; while AI provides the insights, it’s the creativity and hospitality of your team that make the experience unforgettable.

The process is iterative, guided by A/B testing and optimization. I continuously assess the effectiveness of personalized menu offers, refining AI algorithms based on customer responses to ensure ongoing improvement and relevance. Data privacy considerations are paramount. I ensure that all practices align with legal and ethical standards, implementing transparent communication with customers about data usage. This comprehensive and customer-focused approach not only leverages data and AI but does so in a way that respects individual preferences and privacy, creating a personalized menu experience that enhances customer satisfaction and loyalty

Encuentro útil la reflexión sobre la importancia de compartir ejemplos y experiencias laborales significativas. En el ámbito empresarial, consideremos la implementación de un programa de bienestar en una empresa tecnológica. Observando datos de salud y satisfacción laboral, la dirección identificó áreas de mejora. Introdujeron horarios flexibles, espacios de descanso y programas de apoyo emocional, usando inteligencia artificial para evaluar el impacto en la productividad y el bienestar. Esto no solo mejoró el ambiente laboral, sino que también destacó la atención de la empresa hacia el bienestar de sus empleados.

Beyond using data related to what a guest orders, AI can help creating on the spot surprise and delight offers during quiet time to incite a guest to order just one more thing and increase the check size.