Examples of machine learning application

You've probably heard the term "Machine learning" or "Artificial Intelligence" lately. It is going to turn our world completely upside down and 50% of today's jobs will become obsolete. Or will it? In any case, we at SevenLab find it a very interesting development. Hence this article!

Recently I have been on LinkedIn wrote an extensive article about how AI works with a focus on machine learning. In a nutshell, machine learning allows you to predict, discover and detect.

During my own research, I made a list of concrete and interesting examples. Below an overview. Leave your email to see the extended list.

PR and (video) content companies

Predicting:

• How much an online article will be read, even before you post it (relationship content keywords, time of year, historical readership numbers etc.);
• How many viewers will watch video content (perhaps related to media budget, broadcaster, target group, content, etc.)

Maintenance

Predicting:

• when you should do proactive maintenance
• Capacity utilised
• required fuel
• How many refreshments are to be taken on board (perhaps related to date, temperature/weather, number of people in flight/travel);
• budget per passenger
• the amount of tickets that will be booked in order to determine the best possible price (number of visitors to the site, time of year, price of destination, marketing budget)

E-commerce

• Marketing intelligence;
  • Predicting to whom you should send certain marketing campaigns -> to whom you have the most chance of success and/or who delivers the most;
• • Sentiment analysis for social media

• • Automated product tagging with image recognition

• • Predicting stock requirements

• Predict what a visitor is most likely to buy (history, demographics, price, region, roi)
  • Predicting the best price

• • Fraud detection with transactions
  • Shipping before a product is purchased (or partially)

Logistics

Predicting:

• volume/spending per sales channel;
• the crowds on the logistics channel;
• required capacity;
• Travel time (may be weight, region, average travel time, suply chain third party variables, etc.)

Accounting and administration

• Forecast financial data for customers, based on more than just accounting;
• Detecting fraud and/or errors in administration

Energy

Predicting:

• Energy consumption of individual houses / required capacity -> purchasing;
• efficiency of renewable energy;
• Fraud detection (where are the cannabis plantations, how big is the chance that a customer does not pay?)

Property

Predicting:

• required maintenance per object
• best object price;
• capacity of object/space;
• energy consumption per object

Service

Predicting:

• required stock
• necessary materials field service

HR

Predicting:

• required workforce on a specific day in the future;
• sickness reports per day, individual level;
• Customer demand (seasonal work, pipeline);
• required cash flow;
• staff availability

Gaming

• Predicting how people move through the game world
• Predicting number of subscriptions