Technologies that mimic human characteristics seem to be part of the distant future. However, Machine Learning in Marketing is already strongly present worldwide and its effects must be considered so that your company is not at a disadvantage.
Also, machine learning in marketing has had some effects on companies’ customer attraction strategies. So, get to know this technology now and learn how to implement it in your business!
What is Machine Learning?
Machine Learning refers to the ability to learn machines, without human intervention, from a sizeable amount of data. And machine learning in marketing is exactly the learning of data and information about a business’s marketing and sales strategies.
Through algorithms and data (big data), the machines identify standard information and create connections between them to act. And all of this is done without human help or intervention, which is just the machine.
Based on statistical analysis, the algorithms can expect responses, delivering the best results available. With that, we reduce the chances of errors!
We divide machine learning technology into two major groups:
- Supervised Algorithms
- Unsupervised Algorithms
Also, there are other types of learning - which are not as used as these but are still essential for your knowledge. Among them are:
- Semi-Supervised Algorithms
- Reinforcement Algorithms
Therefore, all categories must be understood for the Machine Learning investment process to be effective!
Supervised algorithms are algorithms in which there is human interference. In this group, someone must control the entry and exit of data and information.
Besides, interference in the preparation and training of the machine is necessary to optimize the response predictions. From there, the machines perform this learning for the next analyzes!
Unlike supervised ones, unsupervised algorithms are algorithms that use Deep Learning (we will present this term later).
Deep Learning is a learning without interference or human training. Therefore, unsupervised algorithms are applied from the execution of the machines themselves!
We use this learning for the same functionalities as supervised learning. However, the difference is that this algorithm uses labeled and unlabeled data and information. The semi-supervised algorithm does not exclude data that is not classified.
We generally use reinforcement algorithms are generally for games and navigation. This is since we base this learning on trial and error.
Besides, there are a rewards factor. From trial and error, it is possible to find out the correct element and thus have access to the rewards!
Therefore, based on that, this learning has 3 essential elements:
- The agent (who makes the decisions)
- The environment (object of the agent’s interaction)
- Actions (actions that can be performed by the agent)
Therefore, there is an objective that must be fulfilled! The agent must choose correct actions that enhance the rewards. Thus, through a strategy, he will be able to achieve his goals more quickly and effectively!
What is the impact of Machine Learning on Digital Marketing?
Digital Marketing, as well as Content Marketing, are strategies that are constantly changing and innovating from new technologies.
And both strategies take time if done correctly and effectively. However, with machine learning in marketing, these tasks have become faster and more accurate!
Based on hard data, errors are reduced and your marketing strategies are optimized!
Therefore, it is essential to know the impact of machine learning on your Digital Marketing strategy!
3 major trends that show how artificial intelligence will irrigate the consumer journey in 2020
Trend 1 - Predicting our needs: when artificial intelligence strengthens the role of search in the consumer journey
Internet research is becoming more and more “ predictive “ and provides tailor-made recommendations throughout the consumer journey to fuel both consideration and conversation.
In 2020, search engines will integrate behavioral factors into their recommendations thanks to artificial intelligence. Improving their predictive capacity should offer great opportunities for brands because anticipating consumers’ needs allows them to be better targeted before purchasing or even cross-selling after purchasing.
Trend 2 - Insights collected in real-time thanks to passive user interfaces
Passive user interfaces (or IUPs) constantly collect behavioral data thanks to our connected objects.
By applying machine learning techniques, they can provide valuable lessons to brands to create unique consumer experiences.
Some companies already use passive user interfaces, such as Spotify, which uses data from sports performance trackers to offer tailor-made playlists.
Such practices should make it possible to create content and services adapted to each individual, as well as to evolve pricing strategies according to profiles. Data from passive interfaces can even be shared between brands and between categories to improve the consumer experience more broadly at all points of contact.
Trend 3 - Smart VR: the move from virtual reality to smartphones offers new opportunities for brands
Virtual reality is moving from the solitary world of “ gamers ” to that of mainstream consumers who experience virtual reality through their smartphones. Facebook and Twitter have already set up live streams accessible from headsets connected directly to smartphones.
The shift from virtual reality to smartphones and traditional applications will allow brands to experiment with new marketing opportunities. For example, distributors already can transform the way people buy - by allowing trying products without having to go to the store.
SEO - Search Engine Optimization
If you work with digital marketing, you probably already know that search engines are looking for the best results to provide to users.
Based on the SEO work of a business, the number of keywords in the content may become less relevant. In the meantime, the quality of the content will be strongly considered to be presented to users.
So, don’t worry about filling your text with the key terms you’ve analyzed. Of course, it is important to have a considerable amount of these terms.
However, worry about creating relevant and complete content so you don’t miss out on ranking improvement opportunities!
As you know, the content on your website, blog, or landing page must be of quality and relevant to your readers.
Machine learning in marketing increases your productivity, as it reduces the time spent on tracking and identifying data. Besides, machine learning also allows you to get to know your customers and potential customers better.
From there, you will get to know the interests of your customers and how leads reach your content!
The Link Building strategy aims to optimize the brand recognition of a business.
As you already know, machine learning identifies the relevance of a product or brand to a user. Thus, machine learning in marketing can optimize the visibility of a brand based on that user’s interest.
How to adapt to Machine Learning?
Now that you know machine learning in marketing and what are its advantages, it’s time to know how to adapt to this technology!
The voice search in Google already handles 50% of all searches made in the search engine.
Therefore, it is essential to optimize your content for this type of research so that your website has a greater opportunity to be presented to users!
Increasingly, local searches will be carried out more frequently by users.
So, optimize your website and content with your business address and URL to increase your business’s visibility from a local Google search.
Your company must create a responsive website, which adapts to several different devices. With that, your ranking chances are optimized!
Therefore, perform design tests to check the best structure for your website on different devices!