5 tips: Så kan detaljhandeln dra nytta av Machine Learning

Machine Learning helps you predict the future. Here are five tips on how to take the lead in retail using Machine Learning.

Who doesn’t want to see the future? To be at the forefront and make the right decision that will help your business take the lead in the industry.

Here Machine Learning can be very helpful. By feeding the Machine Learning algorithms with accurate and up-to-date data, it is possible to predict the future. Therefore, many companies in several different industries see great value in using Machine Learning.

One of these is the retail industry. Here, technology can streamline operations, increase productivity and provide customers with personalized and relevant offers that can both increase sales and customer satisfaction.

But this is far from the only thing that Machine Learning can use in the industry. Data on behaviors can also be a valuable resource in sales work. Without insight into and knowledge of the customers, it is a headless quest to create additional sales and to strengthen customer loyalty. Machine Learning also helps the staff to use this data, as the technology can predict with great certainty the customer’s next purchase.

Below are five areas where Machine Learning can be helpful in retail.

1. Increase sales with personal recommendations

Machine Learning plays a major role in analyzing customer data and predicting customers’ future behavior. Retailers can use this data to better understand customer needs by examining prices for past purchases and providing customers with personalized product recommendations. In this way, the personalized recommendations arouse customers’ interest in products they might never have found themselves, which can increase sales.

One of the best examples of personalized recommendations is found at Amazon. Here, 35 percent of sales are driven by recommendations . The algorithm behind the recommendations does not only take into account the customer’s behavior such as what products the customer has clicked on, how often the customer buys and what the customer is looking for. It also makes recommendations based on what other comparable customers are interested in. It creates a broader, but still relevant, range of products for the customer.

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2. Ensure correct bearing balances

A successful business is very much about the ability to have correct stock balances. And many unsuccessful attempts in the past have been required to find the right balance on the stock, as there are so many unknown factors associated with this. How many customers will shop next month? Are there unexpected situations that change purchasing behavior? Does the price suddenly fall on a popular product? This is avoided if you use Machine Learning. Here you get quick and automated help with calculating. This is done through a combination of historical data and real-time data that allows you to make informed decisions about inventory balance. You can still break down the data into different segments, such as weekday and season. This eliminates the risk of guessing.

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3. Optimal and dynamic pricing

Every company knows that the difference between right and wrong pricing on a product can be crucial to the business. But it can be overwhelming for the human mind to take into account all the parameters when making optimal pricing. Therefore, it is a great help with Machine Learning that analyzes all information and predicts what the optimal price is. It can be useful for retailers who want to study the potential impact of sales activities.

It is also possible to use Machine Learning for dynamic pricing. For example, the price of some products may change over time through an algorithm that takes into account various variables such as seasonality, supply and demand. Machine Learning simply gives retailers greater flexibility when they get the right price at the right time. By learning the product’s development over time, Machine Learning can quickly adapt to changes in the market and improve business returns.

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4. Proper placement of products in the store

Machine Learning can also be used to determine how well a product sells in relation to its location in the physical store.

One way of predicting how customers respond to particular products is by using cameras that record and examine customers’ patterns of movement as they walk around the store. The cameras then convert the movements into data that measure the interest in different products. This information can, for example, be used to assess whether a product is incorrectly placed in the store and should instead be placed further in the store. This can also be used to test new products or to determine if products with declining sales should be phased out.

5. Add the best delivery route

Machine Learning and data analytics also benefit retailers in planning the perfect route for deliveries. Namely, it is possible to get the Machine Learning algorithms to process data in real time, make adjustments and suggest the best route for the supplier based on current conditions such as traffic, weather and customer location.

It requires feeding the algorithms with data. And here it is logical to use the Internet of Things. The Internet-connected sensors (IoT) collect data on the weather and other real-time factors, giving the best picture of the circumstances of the individual delivery. The algorithms process the data and calculate the best and most cost-effective route much better than both GPS software and people.

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