What strategies are behind artificial intelligence? Check out the examples of these companies

If you still think artificial intelligence is a thing of the future, it’s time to take a closer look. Several companies that are part of our routines, especially the internet giants, already use AI technologies in their strategies.

When accessing an e-commerce, searching on Google or receiving service from a company, you may already be interacting with intelligent machines. But it doesn’t even seem like it, because robots behave more and more closely to humans. The intention, with this, is to make the user experience increasingly efficient, agile and relevant in the digital age .

But, as we do not always notice artificial intelligence in our daily lives, we prepared this article to show how large companies are using these technologies. Now you will see how they are present in your life! Follow:

What is artificial intelligence?

Artificial intelligence is a concept in the field of technology that refers to the ability of machines to think very similarly to human beings. Often, the acronyms IA or AI (artificial intelligence) are used to refer to this type of technology.

This ability allows machines to understand human behavior, analyze the environment, reason, learn and make decisions — all autonomously, without human intervention. For this, they need to receive and analyze large volumes of data , which expand their knowledge and make their actions more intelligent.

Artificial intelligence is one of the main elements of the fourth industrial revolution, Industry 4.0 , which is also marked by the Internet of Things (IoT), big data, cloud computing and other concepts.

Companies are undergoing digital transformation , which places technology at the center of their strategies and promotes digitization and process automation. In this scenario, machine intelligence is essential to create smarter products and services and make companies more competitive. 

This type of technology is already applied today in several companies that have revolutionized the market for goods and services. The strategies behind artificial intelligence range from recommendation systems for the purchase of products to the predictive price offer of a service based on its demand. Later on, we’ll look at examples of practical applications.

Main artificial intelligence technologies for marketing

Artificial intelligence is not just a technology. It brings together a series of technologies, involving algorithms, codes and data that can perform different functions.

Now, let’s better understand what are the main AI technologies that can be used in business marketing. They are often used together to develop better products, services and strategies.

Machine Learning

Machine learning is machine learning . According to this concept, machines process large volumes of data and identify patterns that generate knowledge about user behavior.

In this way, they can continuously learn and improve decision making, even without any human intervention.

Deep Learning

Deep learning is the deep learning of machines , that is, a deepening of machine learning. Deep learning is based on neural networks, which use more complex algorithms to approximate the functioning of neurons and the human brain.

In combination with machine learning, which works in a more linear way, this concept enhances the ability to process data and generate intelligence.

Natural Language Processing

Natural language processing or natural language processing (NLP) is the ability of machines to communicate with people in a human language.

This is the area of ​​artificial intelligence that approaches linguistics to understand the expressions, idioms, slang, syntactic rules, semantic relations and everyday errors that make human language so complex — that is, an unstructured language.

Because computers use a structured language, they need algorithms and systems to understand human beings and respond to them with natural language as well.

Computer vision

Computer vision is the ability of machines to see like human beings . The intention is to model the human vision, which is capable of capturing the light reflected from an object, identifying the environment around it, analyzing the information and storing it in memory.

In computer vision, machines are also capable of doing this and can make intelligent decisions based on what they see.

2 practical applications and examples of artificial intelligence in companies

Let’s now see how these technologies can be applied in practice and how companies are taking advantage of the potential of artificial intelligence. Check out:

1. Product and service recommendation

Spotify and Netflix are experts in personalized recommendations . Both platforms seek to understand the behaviors and interests of users to make suggestions that they really enjoy. And artificial intelligence is behind it.

Both Spotify and Netflix work with big data. The huge volume of data—both internal and external to the platform—is used to nurture the algorithms, which enhance your knowledge and make better recommendations. In this way, the huge catalog of platforms becomes more interesting and accessible.

On Spotify , the highlight is the Discoveries of the Week playlist, which, by suggesting a personalized list of 30 songs, almost always hits your tastes, right? These recommendations are based on a cross between three models:

  • Collaborative filtering models: Process data about user behavior in relation to other similar platform users.
  • Natural Language Processing Models: Process data about what internet users say about the Spotify catalog.
  • Audio Templates: Process raw audio files from the Spotify catalog.

At Netflix, the personalized homepage is the primary way subscribers interact with platform recommendations. The Netflix strategy is to recommend titles of interest, but also make exploring the catalog.

For this, the homepage is organized in lines, classified by genres or subgenres of movies and series. The lines and order of titles consider the user’s interests in relation to other similar users of the platform (collaborative filtering) and a series of rules. In general, the most relevant titles tend to be closer to the lower right corner, which tends to receive more attention from users (as shown in the image below).

But machine learning on Netflix goes even further. Algorithms continually learn about how users interact with your home page. Thus, it is possible to understand how each person consumes the contents and add, remove or reorder titles in order to create a specific page for each user .

2. Automation of service via chatbots 

Chatbots are one of the main uses of artificial intelligence by companies. For interactions between robots and customers to be relevant, machines need to understand what people are talking about and deliver answers and solutions to them.

Several companies are investing in this type of application to optimize customer service . Among them, banks stand out in the power of investment in technology.

Bradesco, for example, created BIA , the AI ​​assistant that interacts with customers, clears doubts, informs balances and performs transactions. The more users interact with the BIA, the more it learns about them, interacts better, and even anticipates needs.

The solution uses a cognitive computing system from IBM Watson, which has a reasoning very close to that of a human being. Natural language processing is one of the main elements of virtual assistant AI. 

In 2018, in the year following its implementation of the BIA, Bradesco had satisfaction rates above 85%, with 94% of customer queries answered by the virtual assistant. Previously, call center service could exceed 15-20 minutes; now interactions are real-time.

3. Voice recognition

Amazon’s Alexa and Apple’s Siri aren’t just virtual assistants you can ask for the day’s weather forecast. With more and more interactions, they can learn about your interests and make the conversation much deeper.

The two platforms are voice user interfaces (voice user interface , or VUI), using AI technology conversation.

This represents a breakthrough in human-computer interaction. Instead of menus, clicks or taps, we use voice, which is the most natural way humans interact with the world. So, artificial intelligence has the task of understanding what people say in order to also talk to them and perform the tasks they want.

To do this, Amazon and Apple systems rely on natural language processing, which not only understands what people say, but also responds, interacts, and learns more and more. But VUIs go further: they not only understand what we say, but also how we say it, which allows us to capture the emotional nuances of a speech .

Alexa’s number of skills, for example, grows year by year. According to the website Voicebot.ai , there are about 5,000 new skills every 100 days, such as making payments at banks, ordering food for delivery or calling an Uber.

4. Image recognition

Are you also shocked when the Google Photos app recognizes everyone in your family in the photos on your phone? Yeah, artificial intelligence is behind this.

Computers don’t read images. If you see a picture of a dog, for example, Google only sees codes. Hence, they need to learn what the characteristics of a dog photograph are to understand when they are there.

That’s where computer vision comes in. This technology allows you to train your computer to recognize patterns of colors and shapes in images . In this way, machines are closer to the human vision and can make decisions based on what they see.

Thus, the app not only recognizes dog photos, but also recognizes your dog’s photos . It not only recognizes photos of people, but it also recognizes photos of your mom, dad, or anyone else. And the more users tell robots who or what appears in the images, the more they learn.

This way, Google Photos can organize and group the photos you save, so you can find them with a simple search. In this article , Google explains how this technology works.

5. Product pricing

Who hasn’t been scared off by the price of an Uber on a late afternoon of heavy traffic? Yes, artificial intelligence has to do with it too! The dynamic pricing based on demand and supply for a product, is another possibility of practical application of machine learning.

For example, when a lot of people are leaving a football game, Uber rates go up. At the same time, more drivers tend to come to the place because prices are better. But once the event is over, fares return to normal, often cheaper than a taxi.

The same goes for Airbnb, which offers the Smart Pricing feature for hosts who want to adopt. Thus, prices vary according to the demand for accommodations such as the host, as well as data such as location, season, accommodation classification, proximity to check-in, among other factors.

Okay, dynamic pricing is nothing new. Hotels and airlines have been using this strategy for years — as demand increases, the price goes up. However, before AI, this dynamic depended on user-defined rules.

Machine learning, on the other hand, allows algorithms to recognize patterns that humans do not notice, forecast future situations and update prices in real time. In other words, pricing becomes much more granular and agile .

Dynamic pricing with AI considers the demand for a product at the moment and the behavior of users, as well as external data such as news, weather, local events, time, traffic, etc. Therefore, if a show is advertised in a certain city, the algorithms can capture this information and instantly adjust prices, which would be difficult with human intervention.

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