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 across many industries. There are three key reasons why: an increase in the amount of data available, algorithm improvements, and increases in computer hardware speeds.
There are three broad categories of machine learning:
Let’s review each, and explore how a company could use them.
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.
Here's an example: Imagine you work at a bank and are tasked with reducing the number of customers that decide to leave within their first year of service. 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 focus on attracting consumers to visit branches.
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.
Here's an example: You work at a clothing store and want to understand your different customer segments so you can 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.
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.
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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