Although many people think that machine learning is far from our reality, the truth is that this technology is applied in many routine situations. Below, we present the main ones for you to understand better.
Machine learning tells search engines what content to recommend. This is done due to data modeling and complex algorithms, which seek to stipulate a score to rank the user’s preference.
To carry out this process, there are three main types of algorithms:
- collaborative filtering: bases recommendations on content that has been interacted with by users with a similar profile;
- content-based filtering: uses content previously consumed by the user to make other indications;
- hybrid systems: combine these two previous models.
In addition, search engines use machine learning to improve natural language processing. In this way, they can offer more specific answers to some questions.
Data Collection And Analysis
The data generated daily by internet users is updated and used to discover insights and identify potential problems in real-time. Thus, it is possible to act in advance.
For example, they are verified when you make a purchase on the internet. When purchasing a product, other items are indicated by the recommendation system. Streaming services, GPS, etc., use the same rule.
The idea is to predict things you might like. Therefore, previous viewing habits and purchases are used, for example.
Cars move without the need for human intervention due to machine learning. Although they are still rare, autonomous vehicles already exist and are widely used in agriculture.
Through the patterns identified, machine learning finds suspicious and fraudulent transactions. Thus, scams are avoided by banks and credit card companies.
Google Translate tools use machine learning to ensure accurate text translation. After all, this process implies using expressions, verbs, pronouns, slang, grammar, etc. Therefore, the more machine learning techniques are used, the higher the percentage of success tends to be.
How To Apply Machine Learning In Business?
More than understanding this technology, it is essential to know how to apply it. After all, this is how you can achieve the benefits of machine learning for your digital business.
So, check out what you need to do to apply this technology to your startup.
Build A Quality Dataset
The dataset that will be worked on — called the dataset — is essential for extracting relevant data. It is recommended to start construction with many raw columns derived from source systems.
Thus, data is received and incorporated from different sources. For example, a company repository ( data warehouse ), enterprise management system (CRM), or repository with large amounts of raw data, structured or not ( data lake ).
Define Machine Learning Technique
To arrive at this choice, it is necessary to think about the most appropriate method for the problem. Consideration should also be given to the technique capable of using the available data and extracting them with the greatest possible benefit.
It is important to use AI to analyze available resources in this process. This way, achieving more agility and precision within a growth hacking strategy is possible.
Select Analysis Algorithms
The objective is to choose the algorithm or set that guarantees the best result in the predictive analysis. This process can be complex, as there is no single answer. However, one option is to use the confusion matrix.
This technique identifies machine learning performance from the weighting of errors and successes. It is possible to measure the true impact of machine learning performance according to the application context.
Enrich The Dataset
Raw data (raw variables) is essential for machine learning. However, it would help if you also had derived variables and business hypotheses. This is a way to enrich the dataset and increase the accuracy and applicability of the analyses.
In this scenario, it is worth distinguishing these elements. See what each one represents:
- raw variables: are the raw data, only organized and inserted in the system;
- derived variables: they are obtained from the raw ones. They add business knowledge and the predictive power of machine learning. Therefore, they avoid the apparent results and ensure more relevant insights ;
- business hypotheses: these are the typical corporate situations related to the problems that will be addressed and previously observed in other customers and industries. Especially when they are in the same industry as your company.
Have A Large Database
All questions that will be processed need a large volume of data for processing and analysis. To facilitate this process, it is necessary to use some specific software—for example, Microsoft Power BI.
From these steps, you start using machine learning for customer service, facial recognition, text translation, and more. So there are still some simple practices that are worth applying. Check out.
Have A Responsive Website
Your website must be responsive and responsive to ensure good service to users. Bots are developed through machine learning and assimilate behavior patterns to understand and answer the questions asked.
Optimize Local Search
Google already identifies local search. Therefore, optimizing your business if it serves a specific region is recommended. This is the case of a veterinary clinic in the Jardins neighborhood in São Paulo. This makes it easier for potential customers to find you.
Implement Voice Searches
Voice search is being increasingly adopted. Many depend on personal assistants such as Alexa, Siri, Cortana, or Google Assistant. Therefore, it is worth implementing in your business.