ML and AI Case Study : McDonald’s Goes All-In on Machine Learning

What is Machine learning ?

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

Deep Learning and Modern Developments in Neural Networks

Deep learning involves the study and design of machine algorithms for learning good representation of data at multiple levels of abstraction (ways of arranging computer systems). Recent publicity of deep learning through DeepMind, Facebook, and other institutions has highlighted it as the “next frontier” of machine learning.

Types of ML

What is Artificial Intelligence ?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

🍟 How McDonald’s is using AI & ML :

Over the last twelve months, the fast-food chain has spent hundreds of millions of dollars to acquire technology companies that specialize in artificial intelligence and machine learning. McDonald’s has even established a new tech hub in the heart of Silicon Valley — the McD Tech Labs — where a team of engineers and data scientists is working on voice-recognition software.

Central to McDonald’s and really any fast-food business is the need to keep costs low and efficiencies high — something big data, artificial intelligence and robotics can support.

The acquisition is the latest data and analytics venture from McDonald’s, which has been investing heavily in digital transformation since 2015. The company paid $300 million for Dynamic Yield — which may sound big, but it’s nothing compared to the fast food chain’s market capital of $143.5 billion.

Data Volume and Augmentation

The sheer volume of McDonald’s data is amazing. Every day, 68 million customers visit one of McDonalds’ 38,000 retail locations — and the majority of them do not get out of the car. So, the question becomes: How does one service these drive-in customers with more AI-driven personalization?

Consider the data McDonald’s can use to improve personalization:

• Historical sales data at each of their franchises
• External augment data such as weather, traffic, nearby events or activities, and Census data
• Day of the week/time of day stats
• Customers’ past purchases
• Trending items
• Location information

What can McDonald’s do with all this information?

Personalised and improved customer experience

Not only can customers order and pay through the McDonald’s mobile app and get access to exclusive deals, but when customers use the app, McDonald’s gets vital customer intelligence about where and when they go to the restaurant, how often, if they use the drive thru or go into the restaurant, and what they purchase. The company can recommend complementary products and promote deals to help increase sales when customers use the app.

Customized menu for each patron:

There might be a way to identify the customer who is driving in in order to provide a custom personalized menu for that patron. Maybe it’s based on geo-fencing in their app, or on identifying the license plate number using image-based deep learning algorithms like Convolutional Neural Networks (or CNNs).

Dynamic menu changes based on demand:

If the line is moving fast, maybe change the menu accordingly. If the checkout line is long, maybe change the menu to only items that are faster to prepare.

Kiosks and interactive terminals

As one solution to the increasing costs of labour, McDonald’s is replacing cashiers in some locations with kiosks where customers can place their order on a digital screen. Not only are labour costs reduced, but the error rates go down.

Packaging the End-to-End Solution Is the Key

The hard part isn’t building the above predictive models. It’s taking all the individual pieces — from raw data to cleanup, to building out the model, predicting, and distributing the predictions to user interfaces, and letting them take action. How the company packages the end-to-end workflow to make use of data and create intelligent predictions that drive actions — that is the value they can create.

Once McDonald’s starts rolling out this personalized behavior, and assuming it works well, it’s going to make customer ordering so easy. Customers will understand how to use AI-enabled systems and see the value of machine learning — and soon, they’ll start demanding similar systems from other brands. With more and more retail chains beginning to look for such solutions, this move is an early indication of how AI will revolutionize the retail space.

Thank You for Reading .