7 Real-World ML Applications (That Make Money)

 7 Real-World ML Applications (That Make Money)

INTRODUCTION

Netflix makes thirty billion dollars every year.

Do you know how money Netflix gets from the movie and show suggestions it gives to people who use it?

Some people think that seventy to eighty percent of the things people watch on Netflix are things that Netflix suggested to them using machine learning.

That is around twenty to twenty four billion dollars that Netflix gets from machine learning suggestions.

Amazon has a way of suggesting things to people who use it.

The systems that banks use to catch people who are trying to cheat them are also saving them a lot of money every year.

The thing is, machine learning is not an idea anymore.

It is not something that will happen in the future.

Machine learning is being used now and it is making a lot of money for real companies that are doing real business.

Most people do not know how much machine learning is being used.

They think that machine learning is about computers that can talk to people and cars that can drive themselves.

The truth is, machine learning is used in a lot of places.

It is making a lot of money.

In the eight minutes we will show you seven real world examples of machine learning.

We will show you:

- What problems each of these examples solves

- How companies use machine learning

- How money it makes for them

- What you can learn from these examples

Are you ready to see where the money is really coming from?

Let us show you where machine learning is making money for Netflix and other companies, like Amazon.

Machine learning is making money for these companies. It is making money right now.

Let us go and see how machine learning is used by these companies and how much money it makes for them.

APPLICATION 1: Healthcare - Disease Prediction & Diagnosis

THE PROBLEM:

Every year a lot of people. 10 Million. Get cancer. Finding it early really helps them survive.. Radiologists are really busy. They can miss cancers.. Mistakes can happen.

THE SOLUTION:

Computers can be trained to look at millions of pictures. They can find tumors earlier and more accurately than radiologists in some cases. This is really helpful.

REAL EXAMPLES:

Example 1: Finding Cancer

Googles computer program can find breast cancer 5% better than radiologists. It can also find cancers that humans miss. This saves lives.

Example 2: Predicting Heart Disease

IBMs computer system looks at information. It can predict if someone might get heart disease before they have symptoms. Doctors can then help them early. Patients can get treated before they have a heart attack.

 Example 3: Finding New Medicines

Pharma companies use computers to predict which medicines might work. Of testing 10,000 medicines computers narrow it down to 50. This helps find medicines much faster.

REVENUE IMPACT:

The healthcare computer market is worth $15 billion now. It will grow to over $70 billion by 2030. Companies that sell healthcare computer solutions are getting a lot of money from investors.

WHY IT'S VALUABLE:

Healthcare has a lot of money. Peoples lives are at stake, which's very important. Getting approval from regulators is hard which means there is competition.

This combination means there is a lot of potential, for making money.

Next we will look at banks using computers to prevent fraud.

APPLICATION 2: Finance - Fraud Detection & Risk Management

THE PROBLEM:

Credit card cheating costs a lot. $28 Billion every year in the US. Banks lose money. Customers get charged extra. Crooks make a profit.

Traditional rules are not working well anymore. Cheaters are too smart. They keep changing their tricks.

THE SOLUTION:

Machine learning systems learn about cheating patterns in time. When you use your card computers

Look at the transaction details

Compare it to your past transactions

Check over 200 risk factors

Decide: Is it okay or cheating?

All of this happens in a milliseconds.

REAL EXAMPLES:

Example 1: Visas AI System

Visa handles 190 million transactions every day. Their machine learning system catches cheating with 99.9% accuracy. They stop over $25 billion in cheating every year using machine learning.

Example 2: Chase Banks Algorithms

Chase uses machine learning to find patterns. They catch cheaters before they hurt customers. They invest hundreds of millions in machine learning to stop cheating.

Example 3: PayPal & Square

PayPals machine learning system decides if a payment is okay or not in 0.5 seconds. It looks at over 500 things. The result is complaints, faster processing and happier customers.

REVENUE IMPACT:

Banks save a lot of money every year with machine learning to stop cheating. Some people think it saves over $40 billion globally every year.

Companies that sell AI to stop cheating are worth billions.

Why? Because if you can save a bank $1 million every month they'll pay you $200,000. Every month for your service. That's a choice.

SCOPE:

Cheating detection is one thing machine learning is used for in finance. Credit scoring, trading, loan approval and insurance pricing all use machine learning.

Total: The finance industry spends over $50 billion, on machine learning every year.

Next: Online stores making money by personalizing things.

APPLICATION 3: E-Commerce - Recommendation Systems

The Problem:

You have a number of products ten million to be exact.. When a customer visits your site they only see a tiny fraction of them about 0.001 percent. This means that a lot of your valuable products remain unseen by customers. As a result you are leaving a lot of revenue on the table.

The question is, how do you show customers the products that they are actually going to buy?

The Solution:

Machine learning recommendation engines are the answer. These engines analyze a lot of things including:

The products that customers have viewed in the

The products that they have actually bought

The products that similar customers have loved

What is currently trending

The time of year and how that affects sales

The prices of products

Then the engine uses this information to predict exactly what this particular customer is going to buy.

Real Examples:

Lets take a look at some examples of how machine learning recommendations have worked for other companies.

Example 1: Amazons Recommendation System

Amazon is a company that makes six hundred billion dollars in revenue every year.. Machine learning recommendations are responsible for thirty-five percent of that revenue which is two hundred ten billion dollars.

That is a lot of money two hundred ten billion dollars. It is not a typo. This is why Amazon has than five hundred PhDs working on machine learning recommendations.

Example 2: Netflix Recommendations

Netflix is another company that makes a lot of money from machine learning recommendations. They make thirty-five billion dollars in revenue every year and seventy to eighty percent of the things that people watch on Netflix are recommended by the machine learning engine.

That is twenty-five to twenty-eight billion dollars, which's almost the entire business. This is why Netflixs entire business basically runs on machine learning recommendations.

Example 3: Shopify Stores

The average Shopify store makes three hundred fifty thousand dollars in revenue every year.. When they use machine learning recommendations they can make an additional fifty to one hundred fifty thousand dollars per year.

That is a fifteen to forty percent increase in revenue from having better recommendations.

The Economics:

If your average customer spends one hundred dollars per order and machine learning recommendations increase the number of orders by twenty percent that is twenty dollars more per customer.

If you have one million customers that is twenty million dollars in revenue.. If you pay five million dollars for a machine learning recommendation system it will pay for itself in just three months.

That is why every e-commerce company invests heavily in machine learning recommendations.

Conversion Impact:

Companies that use machine learning recommendations see a lot of benefits including:

A twenty to thirty percent increase in the number of people who click on things

A fifteen to twenty-five percent increase in the number of people who buy things

A thirty to fifty percent increase in the amount that people spend

A two to three times increase in how much a customer is worth, over their lifetime

These are not small improvements they are huge.

Next social media companies are going to start personalizing your feed, which will be really interesting.

APPLICATION 4: Social Media - Content Personalization

THE PROBLEM:

Billions of users. Trillions of possible content combinations. 

How do you show EACH person what keeps them engaged longest?

THE SOLUTION:

ML algorithms predict what content will make you scroll, like, share, 

and stay on the platform longest.

Instagram's algorithm: "This user likes travel content. Show them 

more."

TikTok's algorithm: "This user watched 10 cooking videos. Here's 20 

cooking videos."

Facebook's algorithm: "This user engages with political content. Here 

are more political posts."

REAL EXAMPLES:

Example 1: TikTok's Algorithm

TikTok: Most viral app in history. Average session: 52 minutes.

Why? ML figures out what you want to watch... before you know.

TikTok's recommendation algorithm is so good that it predicts user 

interest better than users predict it themselves.

Example 2: Instagram Recommendations

Instagram: $100+ billion revenue (Meta's second largest)

ML recommendations: Core to engagement

Engagement drives ad inventory drives revenue.

Example 3: Facebook Newsfeed

Facebook: $115 billion revenue.

Newsfeed ML: Shows you content that keeps you scrolling.

More scrolling = more seen = more revenue.

REVENUE IMPACT:

These companies don't sell products. They sell attention.

ML recommendation systems maximize engagement, which maximizes ad 

impressions, which maximizes revenue.

Facebook's entire business model: Get users to stay engaged → show 

them → charge advertisers.

ML powers step 1 (stay engaged).

WITHOUT ML: Average user engagement would drop 40-60%.

Revenue would follow.

Why Instagram is worth $1 trillion to Meta? Mostly the recommendation 

algorithm.

Next: Autonomous vehicles and transportation.

APPLICATION 5: Autonomous Vehicles - Self-Driving Technology

The Problem

Driving is really boring at times. People get tired. Lose focus while driving or they drive when they are not sober.

Sadly a lot of people die in car crashes every year around 1.35 million people.

What if machines could drive safer than people?

The Solution

Machines that learn from lots of driving data can drive safer. They learn from millions of miles of driving data.

They can do things like:

Recognize stop signs and people walking and bikes

Figure out what other cars will do, like if they will turn

Handle situations like driving in the rain

React really fast in a few milliseconds

Real Examples

Example 1: Tesla Autopilot

Tesla has a lot of Autopilot data over 5 billion miles. Their machine learning model gets better with every mile driven.

The latest version can handle most of the driving in conditions.

Teslas big advantage is that they have data than anyone else.

Example 2: Waymo (Googles Self-Driving Car)

Waymo is already running robot taxi services in some cities.

Their machines have learned from over 20 billion miles of simulation and real-world driving.

They are not an idea for the future they are already here.

Example 3: Cruise (GMs Robot Taxi)

Cruise is operating in San Francisco, Austin and Phoenix.

They give thousands of rides every day.

Their machines get better with every ride.

Economic Impact

The transportation industry is really big worth $2 trillion globally.

If self-driving cars can capture 10% of that market that is $200 billion.

There are a lot of taxi drivers, in the US around 3.5 million.

Self-driving cars could really change their jobs.

The insurance industry will also be affected, with fewer accidents meaning claims.

It will take some time for self-driving cars to become common around 5-10 years.

It will take longer around 15-20 years for them to be everywhere.

Companies are investing a lot in self-driving cars because the opportunity is huge.

Next we need to keep computers from threats.

APPLICATION 6: Cybersecurity - Threat Detection

The Problem:

Hackers try to attack us millions of times every day. Traditional firewalls are not good enough to stop them. New ways of attacking come out all the time. People cannot react enough to stop them.

The Solution:

Machine learning systems figure out what is behavior on a network. When something strange happens:

They detect the anomaly away

They block the suspicious activity

They alert the security team

They prevent security breaches before they happen

Real Examples:

Example 1: Enterprise Cybersecurity

Big companies like banks spend than 15 billion dollars every year on cybersecurity. 40 Percent of that money is spent on machine learning systems that detect threats.

Machine learning systems can detect threats from inside the company attacks on the network and different kinds of malware that other systems miss.

Example 2: Cloud Provider Security

Big cloud providers like Amazon Web Services and Microsoft Azure spend billions of dollars every day on infrastructure. Machine learning systems help keep it safe.

They cannot watch over trillions of transactions by themselves. Machine learning systems do that job.

Example 3: Email Filtering

Gmail blocks all spam, phishing and malware emails. How do they do it? With machine learning.

Impact:

Cybercrime costs a lot of money. 6 Trillion dollars every year. Machine learning security systems can prevent some of that loss.

Companies pay between 500,000 and 5 million dollars or more for machine learning security systems.

Why do they do that? Because one security breach can cost a company 4 to 5 million dollars on average.

If a security system costs 2 million dollars and stops one breach every three years it is an investment.

Next: Using analytics, for marketing.

APPLICATION 7: Marketing - Predictive Customer Analytics

THE PROBLEM:

You have 1 million customers. Budget for 100K. Which 100K should you 

target? Who will respond? Who'll buy?

Traditional marketing: Spray and pray. Hope something sticks.

THE SOLUTION:

ML analyzes customer data and predicts:

Who will respond to your offer?

What offer will work for each person?

When should you contact them?

Which channel (email, SMS, push)?

Then: Target ONLY high-probability customers.

REAL EXAMPLES:

Example 1: Predictive Churn

A telecom company can predict which customers will leave in the next 

30 days with 90% accuracy using ML.

They offer retention deals to only those customers.

Saves company $50M+ annually by targeting smart instead of broad.

Example 2: Next Best Offer

A retail bank uses ML to recommend which product each customer needs.

Instead of showing everyone everything, they show each person their 

most likely product.

Result: 3x higher conversion on recommendations.

Example 3: Optimal Timing

ML figures out WHEN each customer is most likely to respond.

Contact someone at 2 AM? Bad idea.

Contact them at 6 PM when they check email? 2x better response.

REVENUE IMPACT:

Companies using ML marketing see:

20-40% improvement in conversion

30-50% reduction in customer acquisition cost

2-3x increase in lifetime value

A company with $100M in marketing budget that improves conversion by 

20%? That's $20M in additional revenue.

Marketing department goes from $100M → $120M output with no budget 

increase.

Next: The numbers. How much money are we talking?

The Money - How Much Do These Generate?

Let us add this up.

INDIVIDUAL APPLICATION REVENUE:

Healthcare Machine Learning: the market is fifteen billion dollars. It is going to grow to seventy billion dollars by the year 2030

Finance Machine Learning: it is fifty billion dollars or more every year because of things like fraud and trading and lending

E-commerce Machine Learning: this includes two hundred ten billion dollars from Amazon recommendations and twenty five billion dollars from Netflix and some other companies

Social Media Machine Learning: this is one hundred fifteen billion dollars from Facebook and one hundred billion dollars or more from other platforms

Autonomous Vehicles: people think this will be a two hundred billion dollar market by the year 2035

Cybersecurity Machine Learning: this is a six billion dollar market and it helps prevent six trillion dollars in damages

Marketing Machine Learning: people spend one hundred billion dollars or more every year on this. It is driven by Machine Learning optimization

CONSERVATIVE ESTIMATE: Machine Learning generates five hundred billion dollars or more every year

This is these seven categories and there are many more.

TOTAL MARKET:

The global Artificial Intelligence and Machine Learning market is two hundred billion dollars today

It is going to be one point eight trillion dollars or more by the year 2030

This means the market will grow ten times in five years.

JOB CREATION:

McKinsey thinks that seventy million new jobs will be created by Artificial Intelligence and Machine Learning

Two hundred sixty million jobs will be changed because of automation

So people will need to learn skills and new jobs will be created

INVESTMENT OPPORTUNITY:

If you can build a Machine Learning system that saves a company one million dollars every year

they will pay you two hundred thousand dollars to five hundred thousand dollars, per year for it

If you create ten such systems you can make two million dollars to five million dollars every year

If you create one hundred such systems you can make twenty million dollars to fifty million dollars every year

This is the economics of Machine Learning

What is the main point to remember? Machine Learning is not an idea it is making companies billions of dollars today and the opportunity is just getting started

Now which of these things interests you?

MY RECOMMENDATION

WHAT SHOULD YOU DO?

If you are a business owner:

Look at your industry in the list. Ask yourself can machine learning improve our business?

The answer is probably yes. You should contact a machine learning consultant.

If you are an engineer:

Do you see an application that interests you? That is your specialization.

First learn the basics. Then go in your chosen area.

If you are job hunting:

These are the industries with demand. Get ready, for one of them.

If you are just curious:

You now understand the scope. Machine learning is not science fiction.

It is making money right now. In your city. In your industry.

Line:

Machine learning is a big deal. Understanding it is not optional anymore.

It is time to learn more.


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