Strategies, planning, data analysis – these are essential activities in business today to develop and not fall behind the competition.
Processing the amount of information provided by the internet has long since surpassed human capabilities – we simply cannot analyze enough variables in a short time to maximize the use of information to increase corporate profits. Therefore, instead of laboriously tracking data, we are increasingly using machine learning in business.
Machine learning – what is it?
Machine learning is the field of research into algorithms that can learn by analyzing collected data. Self-learning machines can, for example, gather information about certain internet user behaviors, distill them into general patterns, and then predict with high probability how they will behave in the future. Therefore, one could say that algorithms learn from their own experiences.
Let's explain this a bit more simply. Suppose we have an algorithm that needs to distinguish between photos of dogs and photos of cats. To train it, we give it so-called test data—a database of photos of both animals, with an indication of which one is in the photo.
(The rest of the article can be found below the form)
The algorithm analyzes these photos and learns which characteristics are characteristic of a cat and which are characteristic of a dog. Once it knows this, it can receive a completely different photo, one not included in the test database, and still be able to identify which animal is in it – because experience will tell it so.
Of course, the algorithm needs a lot of information to avoid errors. If we give it 10 photos of Rottweilers and 10 photos of alley cats to analyze, it might not be able to correctly identify a Yorkie.
There is simply no data on which it could conclude that a dog might be smaller, have a different build, etc. However, if the self-learning machine's database contains millions of photos of various breeds of dogs and cats, its "experience" will be so extensive that it will be able to catalog subsequent photos almost flawlessly.
Machine learning in everyday life
Such algorithms surround us in many situations in our daily lives. Almost all of us use them, though we often don't even realize it.
Email is a good example. Every day, we receive important messages, notifications from apps and websites, and even completely useless emails. These machines, based on a wealth of data, determine which messages should be sent to the main inbox, which to the social media tab, and which simply to spam.
Machine learning is also used by companies like Uber. By analyzing previous trips within a given time period, day of the week, city, etc., the algorithm predicts where demand for the service is most likely to be highest in the near future.
The system then dispatches available drivers to these locations to reduce wait times and increase the number of rides. Uber also uses algorithms in other aspects, including estimating arrival times and service prices.
Another giant utilizing machine learning is Facebook. Based on our social media behavior, algorithms display ads and posts we're most likely to be interested in. They also take into account reactions we might not be aware of ourselves—even slower scrolling when viewing specific content.
Where else do self-learning algorithms work?
We also see machine learning in action every day on Netflix, for example, when the platform suggests movies and TV series for us to watch. Algorithms analyze video descriptions, grouping them into microcategories, and then, based on our previous choices, suggest subsequent titles that we're likely to enjoy most.
Recommendations are shown not only based on the productions we have watched, but also on our behavior on other websites – including Facebook (e.g. likes).
American Express, in turn, uses machine learning to ensure the security of its customers. Algorithms analyze transactions and detect anomalies, such as charges that differ in subject matter or amount. They report suspected fraud upon any suspicious account activity to prevent further abuse.
Automatic optimizations in Google Ads
The Google Ads system also uses machine learning to ensure clients maximize their campaign profits. How does it work? Once a campaign is set up, algorithms learn how its audience behaves, when they're active, and which ad texts generate the most conversions.
They extract patterns from the collected data and optimize ads for maximum impact. Their work manifests itself in the use of improved CPC, intelligent bidding strategies, and responsive advertising on the Search Network.
For example, if for some reason there's a sudden surge in interest in your product or service, they can almost instantly adjust their bids to maximize your profit. Similarly, in responsive advertising, algorithms select the best-performing headlines and ad text to maximize conversions.
Self-learning machines constantly monitor all ads and implement countless optimizations daily. Each one, even the smallest, improves budget utilization, allowing you to acquire more customers for the same amount. The more data a machine can collect, the better it can predict future events and, consequently, refine the campaign.
Such algorithms make decisions and make changes themselves, thus giving advertisers more time for other promotional activities unrelated to AdWords/Google Ads campaigns.
What can you use machine learning for?
Machine learning can be used in business to streamline many processes. Using algorithms, you can, for example, track business trends and develop operational strategies.
Based on the collected data, you'll learn when your products are gaining popularity and plan your advertising campaigns and other marketing activities accordingly. A suitable self-learning system also allows you to, for example, create a list of your company's best customers.
Based on interactions with the website or other communication channels, it is possible to determine which audience group is best to target with sales messages.
Machine learning can also be used to measure how effective a company's employees are and how changing conditions (for example, remote work) affect individual performance.
Some entrepreneurs are also using artificial intelligence to plan their business development. Based on collected data, machines can estimate the likelihood of a new initiative being beneficial to the company before it is launched.
Types of self-learning machines
The example described earlier with photos of dogs and cats demonstrates supervised machine learning. In this case, the algorithms know what the intended effect of their actions should be—for example, that they should distinguish a cat from a dog.
In this model, they examine which characteristics of both animals will be useful in comparisons to achieve this goal. This type of machine learning can be used for image classification, speech recognition, or even segmenting corporate customers.
Another type of machine learning is unsupervised learning. In this case, the algorithms are not provided with a "goal" by the human operator that they should achieve after processing the material. They analyze the collected information, looking for relationships and patterns between it, and then draw conclusions based on them.
The human operator of this system cannot predict the machine's outcome, as it simply resembles human observation of its surroundings. This machine learning system can be used, for example, to detect anomalies or irregularities.
An intermediate version is semi-supervised learning, which involves machines receiving both labeled data, with an assumption about what the machine should learn, and unlabeled data, where the machine itself must find common elements and draw conclusions from them.
Are algorithms wrong?
Self-learning algorithms can, of course, make mistakes in their predictions and make unfavorable decisions, just as a human can. However, this usually only happens at the beginning of the machine's operation or when the algorithm has very little data at its disposal.
Therefore, it's impossible to assume that the effects of a "young" algorithm will be spectacular in the first phase. The more time spent gathering information, the better the results, which is easily observed in the case of many applications, websites, and search engines available on the market.
Algorithms can be fallible, but less so than humans, who are subject to numerous limitations when analyzing data, such as subjectivity, fatigue, and distraction. Therefore, when thinking about business development today, the role played by modern self-learning machines cannot be underestimated – they are a key factor in determining a company's success or failure.
Summary
The above article covers the following topics:
- Machine learning is a technology based on algorithms that learn from huge data sets, which allows businesses to automatically analyze complex information and effectively predict future customer behavior.
- Machine learning algorithms accompany us every day, optimizing spam filters, streamlining logistics in services like Uber, and precisely personalizing content and ads on social media.
- Machine learning supports businesses by personalizing recommendations, detecting financial fraud, and automatically optimizing Google Ads campaigns, allowing you to use your budget more efficiently and save time.
- Machine learning in business allows for precise trend prediction, segmentation of the most profitable customers, and reliable assessment of employee efficiency and the profitability of new initiatives.
- Machine learning is divided into supervised learning, which is used to achieve specific classification goals, and unsupervised and semi-supervised learning, which allow algorithms to independently discover hidden patterns and anomalies in data.
- Although machine learning algorithms may initially make errors, over time they become much more effective and objective than humans, constituting a key element of the success of a modern enterprise.
