Many e-commerce and retail companies are leveraging the power of data through artificial intelligence (AI) and its applications like machine learning to develop recommender systems. Put simply, these systems try to predict user preferences and recommend products that will be of interest to them. The most common in everyday life are Netflix for recommending shows, Amazon for products, Spotify for music and even Google for providing the best matching search results (a type of recommender system).

Recommender system illustration through ecomerce

However, there are other industries utilising the predictive technology that you may not be as familiar with. This post looks at some of the key industry use cases.

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Banks

Banks can recommend products to customers based on their transaction history and the types of products that other customers like them have purchased. For example, credit cards can be recommended based on financial history, credit limit, rewards and product features of customers in the same segment. A similar approach may also be used to help banking staff with automated lending decisions.

Usage of recommender systems in bank

Trading

Investments have traditionally required a lot of human resource with the need to act quickly when trying to trade. Recommendation systems can take all the market data and put forward the best investments to the client in real-time, taking the laborious work away from the trader. It also removes the potential for bias where the system will see everything without any emotional context. We are seeing faster and more preference-based trading.

Healthcare

Despite various ethical implications that are yet to be resolved, recommendation systems in healthcare could be able to provide suggested medication based on previous data. For example, trials have shown that AI is able to predict with a higher accuracy than humans whether patients have cancerous symptoms. This type of predictive diagnosis could be ground-breaking in healthcare.

Consumer Packaged Goods (CPG)

Recommendation systems can be used to help set prices through analysis of the product mix, market structure and pre-defined rules. As well as competitor analysis, Big Data platforms that analyse vast amounts of information can identify potential pockets of growth and recommend solutions and/or economies of scale to those in the industry.

Education

Where a lot of courses are now completed digitally, it gives the rise to vast amounts of data. A recommender system can point students towards the most relevant courses based on their performance and what others like them have selected. This can ensure students are given personalised study plans and offer the best journey for them to learn.

Agriculture

Farming is making use of Big Data to track the status of crops, weeds and potential threats. Technology such as driverless tractors and drones are being used to improve efficiency, covering a large area in a much quicker time than humans have ever been able to. This means they can continuously collect data. Algorithms can use the data to predict the best conditions for crops to grow and forecast output. These recommendations can be highly valuable in ensuring farmers achieve the correct yield and the right time.

Summary

Whilst recommendation systems are primarily associated with retail and on-demand type services, there are plenty of ways they can be utilised in other industries. This post covers some of those but, in all likelihood, you could probably come up with a reasonable use in in virtually any sector.

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