Machine Learning | What it is and why you should care

There is a new technological wave that is helping retailers stay ahead of trends and its name is machine learning. A method of data analysis, it works on the premise that the more a company uses informative data to make decisions, the more it will lead to both productivity and profitability advantages.

However, just what is machine learning and why is it worth taking note of? We explain.

What is machine learning | with machine learning examples

There are many data analysis methods out there, so what is special about machine learning? Well, it automates analytical model building by making the most of algorithms that actually learn from the data and give computers the chance to discover unusual and hidden insight even though they have not been specifically programmed to do so.

Perhaps it is the iterative element of machine learning that is most important: when models meet new data they can adapt independently. They will actually learn from earlier computations and will be able to form repeatable and reliable results.

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In fact, the concept of machine learning is nothing new – but with today’s computer technologies now at a different level to what they were previously, the results are much more impressive. Indeed there is a host of machine learning examples that you may have heard of already. For example, the Google car, the self-driving autonomous vehicle, is focused on machine learning. Recommendation offers from the likes of Netflix and Amazon also rely on machine learning; while learning about what your customers say about you through Twitter also focuses on machine learning.

Why is machine learning so important?

The resurgence in machine learning has been prompted by the same interest that has made Bayesian analysis and data mining so important. Being able to enjoy cheaper computer processes; data storage that is more affordable; and the ability to grow both the varieties and volumes of data are all important factors in our ability to analyse more data, faster: and deliver increasingly accurate results.
By taking high level predictions we can use these to guide our actions in real time and make smarter decisions.

How this impacts the retail sector

Perhaps the biggest impact of machine learning on the retail sector revolves around the fact that so many customers now carry out their shopping online.

There are now a host of ways in which retailers can learn new things about their customers and their shopping habits: including their purchase history, market trends, social media and more. All of this can be used to both maximise spending and encourage repeat purchases. Retailers have quickly adopted the technology and tried to integrate it into their businesses processes in an effort to discover buying trends and a deeper understanding of consumers.

In the past, forecasting trends was like a guessing game, with a little intelligence involved. However, with machine learning they have the chance to make sense of new data in real time. It becomes possible to blend unstructured data, such as that from a call centre or social networks, with more traditional trends and profiles. Machine learning can take all of the historical information available and make predictions while also taking into account variables such as seasonality. If it is combined with natural language processing then it becomes possible to note shifts in fashion trends and consumer sentiment and use that as part of your planning process.

How we are using machine learning

There are a host of ways in which machine learning is being used, currently. For example, have you ever wondered how an internet retailer can instantly produce offers on products that might be of interest to you? This is machine learning. Or what about how lenders can produce real time answers on loan requests? Again, this is machine learning.

Some of the other examples of machine learning include: web search; credit scoring; equipment failure predictions; fraud detection; pricing models; image and pattern recognition; and spam filtering. Another popular example of machine learning is the recognition of different types of images, using pattern recognition. For example, many postal services rely on machine learning as a way of recognising signatures.
What machine learning method is right for your business?

By now you may have decided that machine learning sounds like a great step for your business. However, how do you actually implement it: what are the machine learning methods available?

  • Supervised learning | Around 70 per cent of machine learning is supervised learning. This makes use of algorithms that are effectively trained through labelled examples. So if equipment had data points labelled “fail” and “run”, the algorithm receives inputs based on the correct outputs and learns by comparison in order to find errors. It can then modify accordingly. In general, supervised learning is used when historical data makes predictions about future events. This is how, for example, an insurer can predict your likelihood of making a claim; and how card companies can understand if a transaction is likely to be fraudulent.
  • Unsupervised learning | This has no historical labels and the system is never told a right answer. Instead the algorithm must discover for itself what it is being shown. The idea here is that the data is explored and some patterns are found within that data. This could be used, for example, to find customers that have similar attributes: they can then be subject to the same marketing campaigns.
  • Semi-supervised learning | Less common is semi-supervised learning, which uses both labelled and unlabelled data. It is used with prediction, regression and classification. One example would be face recognition on a webcam.
  • Reinforcement learning | Generally used with navigation, gaming and robotics, reinforcement learning allows the algorithm to uncover which actions lead to the highest rewards via trial and error.

Whichever machine learning process you opt for, it’s clear there are massive advantages to be gained for your organisation: clearer data analysis invariably leads to faster and more profitable results.

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