Machine Learning is gaining popularity

Machine Learning is gaining popularity

Machine Learning has become indispensable in various areas. 

The systems sort out advertising messages or automatically answer user questions. Now the computers are preparing to do even more manual business for humans. 

The potential seems to be inexhaustible. When Netflix launched "House of Cards" in 2013, the series quickly became the company's most downloaded content, which in no way surprised those responsible for sales at Netflix. They searched a massive pool of data on their subscribers' consumption habits and found that House of Cards had an excellent chance of becoming a hit even before buying the series by the company.

When making this decision, Netflix did not rely on intuition but opted for machine learning (ML). The streaming service provider relied on the ability of machines to use algorithms to independently recognize patterns in unstructured databases such as images, text, or spoken language, to make predictions or to classify data. However, the possibilities of machine learning are not limited to identifying the next TV blockbuster. 

Several applications that are taken for granted today are based on ML: filtering spam, or the artificial voices that speak to us from our smartphone or other digital devices.

Self-learning algorithms

While these examples are useful, they are just harbingering the giant ML potential. A large number of business processes are now controlled by rigid, software-based rules. However, this approach has limited use when dealing with complex processes. Besides, these processes often require human intervention for repetitive tasks and manual interference. Checking the correctness of invoices and expenses is just as important as checking dozens of resumes to fill a vacancy.

Self-learning algorithms can take over such tasks and also show solutions that were previously impossible without them. With ML, you can improve recruiting, personalize customer service, uncover fraud, and take on quality control in manufacturing. The multitude of possible use cases and the associated benefit of such solutions make ML one of the central disciplines in artificial intelligence (AI). Therefore, it is not surprising that a vast majority of managers involve more and more technical intelligence in everyday business, as shown by a survey from the Harvard Business Review among around 1,800 executives. You can be happy: Machine learning is no longer just a topic from sci-fi novels, but real business practice today.

Let's discover the fields where ML can find the best application:


In accounting, solutions that use machine learning are currently able to assign transfers to the correct invoice automatically. The solution remembers which steps the processor has to take now to assign the transfer correctly. In this way, such solutions learn and can reduce costs through an increasing degree of automation.

A wide field for the use of ML can be tapped in marketing. Just think of all the data streams available to the marketing organizations: POS transactions, online purchases, click-through rates (CTR), browsing behavior, social media interactions, smartphone use, geolocation, and more. Using machine learning, marketers can use this data to categorize and segment customers with greater granularity or to set up campaigns that predict customer reactions more accurately.

Sponsorship and retail

An additional area of application in marketing is sponsorship. Marketing departments need to be able to back up their financial commitments with reliable ROI information. This is where ML-based solutions come into play, helping companies determine the influence of their sponsorship measures. To do this, they measure, for example, how often and for how long the company logo can be seen on the screen during live broadcasts of a sporting event or a music festival. Now algorithms are taking over what previously had to be determined manually. They identify the size of the logos, the position in the image, and the duration of the display, almost in real-time. In conjunction with comparative data from other brands and defined key figures, it can be determined whether sponsoring is worthwhile.

Criteria for the application of machine learning

1. The highest potential for machine learning lies in the automation of high-volume tasks with sophisticated algorithms and large amounts of unstructured data.

2. Machine learning works best for specific, clearly defined tasks in which the desired output and the relevant input can be specified.

3. Machine learning requires large amounts of data. Sufficient examples are required so that the machine can learn meaningful approaches to the desired decisions.

4. The data that serve as a learning basis must contain significant differences (for example, in the customer characteristics) so that the algorithm can fulfill its mission.