Definition: Machine learning

Machine Learning is a term used in the field of computer science related to artificial intelligence.

Machine Learning is a concept that relies on the fact that a computer is literally capable of learning autonomously according to the data it is fed and thus generating actions that go beyond those for which it is originally programmed.

These are specific actions that automatically adapt to given situations to facilitate the user’s experience.

This is how Google uses Machine Learning for its various services, including Google Search. When you type something in the search bar, for example, Google suggests a logical sequence of words that may correspond to your search.

The most advanced example of Machine Learning is probably Google’s driverless car, also known as Google Car.

This car is capable of driving autonomously for hundreds of thousands of kilometers, analyzing and adapting to all types of situations it might face on the road, even situations for which it has not been explicitly programmed. This is how it is able to avoid accidents.

Machine Learning in the omnichannel strategy of retailers

 

For a long time monopolized by web giants such as Google or Facebook, the use of Machine Learning is now widely spread in the retail world.

Its system of adaptive algorithms allows retailers to better know and understand their customers by implementing targeted sales and marketing actions both online and offline.

This includes, for example, the use of a conversational agent or personalized cross selling. All this tends to improve the customer experience and thus increase sales.

To be truly optimal and increasingly accurate, Machine Learning requires a large amount of customer data collected over several years. The more information a company has about its customers, the better.

Machine Learning and the Supply Chain

Beyond a marketing use focused only on the consumer, retailers are also increasingly using Machine Learning to optimize their supply chain management and more directly their inventory.

This also requires feeding the software with a large amount of data regarding inventory, logistics and sales history.

Thanks to Machine Learning, it is possible to anticipate the impact of seasonality, avoid stock-outs or estimate in advance the quantity of replenishment required with an extremely high degree of accuracy.

This algorithmic approach to management allows thousands of companies to boost their performance and achieve considerable savings by reducing costs on several levels, including transportation costs.