3 types of machine learning leveraged by enterprises

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Many of our customers are starting to use machine learning to improve the performance of their company. Machine learning is an application of artificial intelligence (AI) which trains computer systems to learn on their own without human assistance. Recently, we have seen an explosion in machine learning successes across many industries. There are three key reasons; an increase in the amount of data available, algorithm improvements and increases in computer hardware speeds.

There are three broad categories of machine learning: supervised learning, unsupervised learning and reinforcement learning. Let’s review each of these types and explore how a company could potentially use one of these machine learning types.


Supervised Learning

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As the name implies, supervised learning starts with some direction. In this case, the machine is given example inputs and desired outputs. The goal is for the machine to map the inputs to the outputs or in other words, predict what inputs will have the highest probability of leading to the desired outputs. For example, if you are working at a bank and you are tasked with reducing the number of bank customers that decide to leave as customers before the first year. The machine would analyze the customer’s activities, e.g., services used, account balances, customer service calls, branch visits, etc., to see if there were any patterns with the current customers vs. ex-customers. If the machine says that customers who rarely visit a branch were the ones that left, the bank could use this information to offer non-branch visitors an opportunity to schedule an appointment at a branch at a convenient time.


Unsupervised Learning

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As a contrast to supervised learning, unsupervised learning means that the machine is given all the data and tries to structure from its inputs. The goal is to find hidden patterns in the data, much like trying to find a needle in a haystack. For example, a clothing store wants to understand its different customer segments in order to market to them more effectively. An example of unsupervised learning was done for a wholesale grocery distributor which found purchasing correlation of different products. Being able to segment customers is paramount for any enterprise; tailoring your marketing messages toward the needs of each group enables your company to better meet the customer’s needs.


Reinforcement Learning

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The final type of learning is reinforcement learning. The program interacts with a dynamic environment in which it must accomplish a certain task and receives immediate feedback on its performance. This iterative process allows the program to constantly get better. Popular examples of reinforcement learning include autonomous driving and game playing. The difference from the other types of machine learning is that the time-frame to make a decision is much shorter. The advantage of implementing reinforcement learning for an enterprise is to quickly decide on a course of action with limited current data. For enterprises, being able to quickly understand customer behavior with limited historical data could be advantage.


Conclusion

Enterprises using these types of machine learning can gain sophisticated insights about their customers, processes and partners. As more IoT devices come online, companies will be able to incorporate more data into their machine learning efforts. The increase of cloud and networking technologies can enable companies to tap into more data, further fueling their efforts. Companies can use leverage machine learning to better predict their customers’ behavior, spot patterns in the tremendous amount of data and be able to make better, faster decisions and reduce customer churn.

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