Understanding Top 3 Use Cases of Machine Learning for Business

Given its ability to solve complex problems, machine learning has garnered significant attention in the past few years. Implementation of machine learning to solve real-life situations can be observed across scores of industries today.

For instance, email providers leverage machine learning for spam detection while Facebook uses it for image tagging.

In this blog post, we have identified three prominent areas where machine learning is making waves, allowing businesses to accurately utilise their data.

But first, let us understand machine learning in a bit more detail:

What is Machine Learning?

It is a subset of artificial intelligence used to find solutions to complicated problems independently. Machine learning algorithms can predict future outcomes based on patterns in large databases.

It can find patterns in words, numbers, images, etc. Machine learning for business is different from traditional programming in that the latter uses manually created programs.

The input data is fed to this manual program to produce the output. On the other hand, both output and input data are provided to a machine-learning algorithm to create a program.

That program is capable of learning and adapting on its own.

Real-world Problems That Can Be Solved with Machine Learning
To learn how to apply machine learning to business problems, you first need to identify all the time-consuming processes and require manual labour.

It is usually implemented in business areas that necessitate continuous improvement post-deployment.

Here are the top three use cases:

1. Identifying Spam

If it weren't for machine learning, our email inboxes would be filled with spam or unsolicited emails. Imagine clearing those bulk, unwanted emails manually? Nobody has that kind of time.

Not to mention the security risks inherent in such emails. This is why email providers utilise machine learning to filter spam automatically.

The neural networks can pick out spam emails successfully based on common characteristics identified in sender content and subject.

2. Making Product Recommendations

The recommender system is a universal application of machine learning for business. It's used by mobile & web applications, entertainment platforms (such as Netflix and Google Play), e-commerce websites (such as Amazon and eBay), and search engines.

Machine learning algorithms record various parameters and behavioural data, including browsing history, contextual data (device, language, and location), item details (category, price), purchases, page views, item views, clicks, etc., to make recommendations.

This, in turn, enables businesses to inflate profits, increase user engagement, boost traffic, and reduce churn rate.

3. Customer Segmentation

The implementation of machine learning to solve real-life problems is also found in marketing. Customer lifetime value (LTV), churn prediction, and customer segmentation are common challenges in this domain.

Using machine learning, marketers can make data-driven decisions in their campaigns while eliminating any guesswork involved. Moreover, it encourages customers to engage with the brand, thereby increasing the conversion rate early on.

Want to learn about more use cases of machine learning for business?

Read the full blog about machine learning problems and solutions.