Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
McDonalds has more than 38000 restaurants worldwide with sales of 100 Billion Dollars.
Since 2017 he started a long journey to a drastic modernization of the company, using a new approach to the business. Artificial Intelligence.
The main problems of the business were declining customer visits , high competition, great difficulty in finding staff and significant changes in consumer preferences.
MacDonalds was urgently trying to increase revenue.
Because of the complex supply chain and concerns about many changes making service slower, McDonalds was particularly interested in solutions that make the existing menu more interesting.
With around 65% of revenue coming from driving through the store, it is obvious the part to focus on increasing efficiency.
But recent research shows that McDonalts is not doing well in other areas.
According to QSR magazine, it has an average order time of 273 seconds compared to an industry average of 234 seconds.
The QSR survey also showed that up to 58% of customers were not offered suggested sales by salespeople.
This happens when the seller encourages the customer to make additional purchases.
This is a key factor in profitability.
Human sales is a common challenge in many retail environments.
McDonalds identified Personalization and Recommendation Engineering as a solution for presenting customers with products relevant to their desires.
Serving some 68 million customers a day McDonalds is not short of sales, but as CEO Steve Easterbrook put it……
…..It’s about drawing insight and intelligence from it……
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https://mobiplus.co/ebooks/customer-rediction/
McDonalds is a global business and is based on consistency, but there is always the need for local adaptation.
Products that adopt local preferences.
With the Personal Recommendations method the company can show a different menu in a different location, and automatically change it according to the time, place and specific situation per restaurant.
The data McDonalds used to personalize the menu includes the weather , time of day and date.
The company decided to offer customers more ice cream if the weather is warm and make hot drinks more prominent when it’s cold.
Other data used in the Recommendation Engine is the real-time availability of stock in each restaurant, so that products that are not available do not appear on menus.
The Recommender also temporarily removes complex products from menu screens during peak periods , to encourage customers not to order products that take longer to prepare so as to reduce serving time and increase customer satisfaction.
Machine learning technology and Recommendation Engineering will allow McDonalds to test and launch new products with efficiencies comparable to an online business.
Additional benefits will be created as they apply Artificial Intelligence to optimize inventory management and reduce waste.
McDonalds is also testing the use of driver license plate recognition to add another layer of data to the recommendation engine.
The algorithms will take into account the history of previous orders and recommend even more personalised products and promotions to each customer.
Customers appreciate shorter lines and personalized food recommendation are relevant and tasty.
The same recommendation technology is adapted for use in ordering kiosks, mobile apps and Uber Eats (e-food equivalent).
Many other companies in the same industry follow McDonalds, such as Sonic-Drive , Taco Bell and others , which show customers personalized menus, promotions, and other content based on individual preferences, meal history, location, weather, product availability, prices, order times, etc.
Contact us now to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.
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Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.
