The Business Impact of Machine Learning: Real-world Case Studies

Last updated by Rishabh Dev Choudhary on Oct 28, 2024 at 01:30 PM | Reading time: 10 minutes

Machine learning (ML) is the new trend these days. It’s the tool everyone wants in their arsenal. And businesses are taking notice because machine learning isn’t just a tech trend; it can transform everything from marketing and sales to supply chain management. In this article, we’ll provide a detailed overview of the impact of machine learning, why it matters, and how some of the biggest companies out there are reinventing themselves with this new technology.

What Is Machine Learning?

Impact of Machine Learning: What is Machine Learning?

Let’s start with the basics — what exactly is machine learning? Imagine you are trying to teach a kid how to ride a bike. Obviously, for the first few tries, he would fall, but every time he does fall, he doesn’t repeat the same mistake, and over time, he improves and learns to balance himself and move forward. That’s the idea behind Machine Learning, except that instead of a kid, it’s a computer that learns from data.

Machine learning is a part of artificial intelligence (AI). With machine learning, you don’t need to give explicit instructions to a computer. You just show lots of the possible behavior you want and let an algorithm figure out how to do the task you want done.

Here are the different types of machine learning:

Supervised Learning: Think of this as having a teacher who helps you. The algorithm is exposed to the labeled training dataset, which means we already have the data that act as a guide or provide output. For each new input, it tries to predict the output, which is pretty close to the actual one. Spam detection in emails and Fraud detection in financial transactions are some examples.
Unsupervised Learning: This is similar to if you were given a puzzle to solve but didn’t have a picture of what it should look like. The algorithm is given a dataset without known output data (hence “un” supervised) and asked to find patterns or groupings in the dataset independently. It is used for clustering, like when we want to group all the customers with similar behavior together for targeted marketing.
Reinforcement Learning: This type of learning is all about trial and error. The algorithm interacts with the environment and learns from what happens due to its actions, getting rewards for right decisions and penalties for wrong ones. It is one of the most popular types in robotics and gaming, which require an online decision-making system.
Semi-Supervised Learning: In Semi-supervised Learning, the algorithm is trained on a small amount of labeled data and a larger amount of unlabeled data, which combines supervised and unsupervised learning. This approach is often used when labeling data is costly or time-consuming.
Deep Learning: A specially designed subset of machine learning that involves neural networks with deep layers. Deep learning, in particular, is quite strong in image and speech recognition tasks, where it can identify complex patterns that are impossible for humans to program manually.

Also read: What is Machine Learning? A Comprehensive Guide

Impact of Machine Learning: What Are Its Benefits? <h2>

So, why are businesses so crazy about machine learning? Well, the reasons are pretty good and the benefits are huge. Here’s why the impact of machine learning is changing the game for businesses everywhere:

Benefit	Explanation
Automation	Machine learning can automate the normal, freeing up people to do the abnormal. Think of a chatbot handling customer service questions.
Predictive Analytics	ML algorithms can use and analyze historical data to predict future actuals, helping businesses take actions proactively. It’s very important in finance and healthcare.
Personalization	Machine learning analyzes user data to personalize products, services and experiences based on individual preferences, which greatly improves customer satisfaction.
Cost Reduction	Automation and increased efficiency lead to reduced costs of operation. Predictive maintenance in the manufacturing industry, for example, reduces costly equipment's failure.
Improved Decision-Making	By the use of AI in business, large datasets can be analyzed swiftly which in turn leads to insights requiring data-driven decisions. This is particularly useful in fast moving markets.
Scalability	Machine learning models can process more and more complex data as your enterprise scales, so they can help grow your business.
Fraud Detection	Machine learning aids in tracking down the unusual transactional path for preliminary fraud detection.
Enhanced Customer Service	Based on machine learning algorithms, chatbots and virtual assistants give prompt answers to solve customer issues and inquiries, promoting improved satisfaction and response time reductions.
Supply Chain Optimization	Planning demand and setting the right inventory levels enable AI in supply chain management to offer better service at reduced costs.
Competitive Advantage	Early adopters of machine learning with a strategy in place will create unmatched advantages over their competition through using insights and efficiencies no one else has access to.

Being able to predict trends, personalize experiences, and automate processes can significantly impact a business’s bottom line. And with machine learning becoming more advanced over time, these impacts will only get larger and the opportunities to be creative with them will as well.

Also read: Machine Learning vs. Data Science — Which Has a Better Future?

The Business Impact of Machine Learning: Real-world Case Studies

If you want to understand the real impact of machine learning, you must analyze where and how it is used. Here are some examples of companies using the impact of Machine Learning and seeing amazing transformations.

1. Impact of Machine Learning for Amazon

Amazon, the e-commerce giant, has been a front-runner in leveraging the impact of machine learning to improve customer experience forever! Do you remember how Amazon’s recommendation engine used to be the best example everyone gave about machine learning?

The engine processes massive data points such as your browsing history, items you have previously purchased, those in your ‘Wish list,’ and even what other customers similar to you are buying.

A report states that product recommendations can drive conversion rates 5.5 times higher than if customers are left to make their own decisions.

The numbers are mind-boggling. Amazon’s recommendation engine influences 35% of the e-commerce giant’s revenue. That’s billions of dollars annually, garnered through a process that understands its customers perhaps better than they know themselves.

Personalized recommendations leads to greater customer retention apart from increased sales as users repeatedly come back to the platform.

2. Impact of Machine Learning for Netflix

Another company that’s nailing machine learning is Netflix. With a user base of millions spread worldwide, Netflix has to make sure its users can find content they’ll love and fast. That's where they use their content recommendation engine.

Netflix analyzes what you watch, the time of day you watch, and what has similar themes or actors - then uses that to find shows/movies on Netflix you are likely to enjoy. Also, Netflix uses what they know about you and users in general viewing habits/choices and positive reviews for other shows/movies, etc.

Netflix also uses how other viewers with a similar viewing history to yours have rated and enjoyed other shows, movies, etc. And it’s good at suggesting new things you will like.

The impact of this technology is enormous. Netflix quantifies that its recommendation engine is worth $1 billion annually, reducing customer churn. And around 80% of the content that gets watched on there is discovered through recommendations. Hence, their machine learning algorithms are working well.

Netflix has over 277.65 million subscribers globally, and most of these users find what to watch through its recommendation algorithms. Such a large audience can only be retained with a personalized approach.

Also Read: Google Machine Learning Engineer Interview Prep

3. Impact of Machine Learning for Walmart

Walmart is the world’s biggest retailer and faces the major challenge of managing inventory that flows between thousands of stores and distribution centers. To solve this problem, machine learning comes into play to improve shopping practices within Walmart.

By analyzing historical sales data, weather patterns, local events, and examining sales during previous holidays, Walmart's machine learning algorithms can predict product demand with a striking degree of accuracy. This allows Walmart to have the right product in the right place at the right time.

Machine learning in inventory management has helped bring down 30% of stock on the shelf at Walmart. This ensures zero frustration for customers due to the non-availability of products and lots of savings in excess inventory costs.

Walmart has leveraged machine learning to stay ahead of the retail curve, earning more than $648 billion in annual revenue by 2024.

4. Impact of Machine Learning for Spotify

Spotify has used the impact of machine learning since its early days. With more than 626 million monthly active users, Spotify leverages ML performers to curate playlists like your favorites, recommend new artists, and even create custom mixes.

The songs you listen to, the artists you are interested in, and even the time of day that these music are played are some data points used by Spotify’s recommendation engine. It also considers things such as what genres you like most and the tempo of the songs you find enjoyable. All this information is employed so a user would get a suggestion specifically tuned for his tastes.

70% of the music on Spotify follows from the recommendation engine. It not only makes the users stay engaged and return for more but also helps Spotify have the upper hand in the competitive music streaming market.

According to BBC, i the first quarter of 2024, the number of Spotify premium subscribers rose by 14% to 239 million, increasingly attracted by the personalized music that Spotify can deliver.

5. Impact of Machine Learning for Tesla

Tesla goes beyond the impact of machine learning practices with their autonomous driving technology. Tesla integrates their vehicles with a suite of sensors and cameras collecting data in real time. This data is then used by a machine learning algorithm to make the car sense its environment, understand and recognize objects like pedestrians, and other cars, and make decisions on how it should move.

Every car Tesla makes is automatically and continuously improving. The moment one car learns something, every other car learns it too. Every mile driven by a Tesla gathers data that helps improve the performance of every other Tesla on the road. In short, Teslas are both cars and computers on wheels.

Tesla reported that they have seen a 40% reduction in crash rates with the Autopilot system that runs on all Teslas. That’s massive since you’re saving so many lives on the road. Tesla is one of the leaders in the autonomous driving revolution and all Tesla vehicles are powered by machine learning.

Also read: How to Become a Machine Learning Engineer in 2024?

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FAQs: Impact of Machine Learning

1. What industries benefit the most from the impact of machine learning?

Machine learning has a significant impact across various industries, including retail, healthcare, finance, automotive, and entertainment.

2. How does machine learning improve customer experiences?

Machine learning allows businesses to analyze vast amounts of customer data for personalized recommendations, targeted marketing, and more relevant customer interactions.

3. Is the impact of machine learning expensive to implement?

The cost of implementing machine learning can vary widely depending on the scale and complexity of the project.

4. Can small businesses benefit from the impact of machine learning?

Absolutely! While large companies often make headlines for their ML innovations, small businesses can also leverage machine learning to profit from them.