Although 2021 is not over yet, the use of machine learning and artificial intelligence already marks a considerable increase trend compared to 2020.
Companies around the world are leveraging these resources to meet goals such as:
- Get useful data about your customers.
- Improve the user experience.
- Fight fraud.
- Improve retention rate.
- Interact with customers.
This is possible thanks to the characteristics of machine learning, which allow create software applications capable of making predictions without being programmed to do so.
Penetration in Mexico and the world
Major companies have already introduced machine learning examples to everyday platforms like Google, Uber, Amazon, and Facebook. This has become an important differentiator when offering better services, since the precision, speed and efficiency exceed manual data processing methods.
This explains the projections for the global machine learning market between 2019-2025: a compound annual growth rate of 43.8%.
Now, in the specific case of Mexico, the implementation of machine learning is a project with many challenges. A study by everis and the MIT Tech Review found that Mexican companies have a very superficial knowledge of this technology, which does not allow them to take advantage of it.
Machine Learning Features
- It consists of three general stages: first, data is collected on a specific topic; They are then examined by choosing an algorithm to look for patterns, and finally predictions are made about possible future outcomes.
- There is different types of learning that can be applied to machine learning. For example, supervised learning involves feeding labeled data to an algorithm, while unsupervised learning provides unlabeled data for the machine to establish associations on its own.
- In order for the algorithm to deliver increasingly accurate predictions, an important part of the process is to keep training it. Over time, a level of precision is reached that allows automate repetitive tasks, increasing productivity and lowering costs.
Machine Learning Use Cases
But, out of everything that has been said, what can this type of technology offer to your company?
Reviewing some machine learning use cases will give you a clearer idea of how to use it.
- Customer analysis. It consists of collecting data from users (for example, their activity on social networks or their browsing history) and then converting them into actionable information. This is something that would take forever if you had to do it manually, but algorithms can sift through large stores of data in minutes and help you plan more effective marketing strategies.
- Recommendation systems. This is one of the examples of machine learning that has changed the way people interact with their devices. The opportunity to create an engine that learns from the tastes and preferences of your customers makes people feel confident, speeding up the process of buying or consuming content. Some examples are recommendations from Spotify, Netflix, and Amazon.
- Financial trade. Banking is one of the sectors that can benefit the most from machine learning. The algorithms are capable of analyzing historical data, finding performance patterns and predicting future market behavior, thus reducing investment risk.
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