According to various sources, this technology, like everything related to Web3, accelerated with the pandemic. What was expected for 2025-26 we are seeing in 2022-23. What does this mean for the marketing and advertising industry? There is much to analyze! For starters, through the use of advanced algorithms and machine learning, businesses can now personalize and optimize their marketing campaigns more effectively than ever before.
One of the main benefits of using AI in our industry is the ability to segment and personalize content for different audiences. Algorithms can analyze vast amounts of data about consumer behavior, such as internet searches and social media interactions, to use that information to create highly personalized content for each user.
For example, a clothing company can use AI to analyze a user’s behavior data and show them personalized ads that match their style and preferences. This not only increases the probability that the person will click on the ad, but also improves the user experience (User XP) by showing them relevant and interesting content.
Another looming use of AI is chatbots and voice assistants. Intelligent chatbots can help companies serve customers in a more efficient and personalized way, as they can answer questions and solve problems automatically (and learn from each interaction). On the other hand, voice assistants like Alexa and Google Home are a great way to reach customers in a non-invasive way, that is, reaching them at times when they are most likely to be relaxed and willing to listen to your promotional message. or offer and even interact with it.
This all sounds fantastic, however, building an AI platform to deliver real-world marketing solutions is not an easy task and requires a combination of business and technical skills. Next, I want to list some of the essential skills required to have a proper AI platform:
- Knowledge in data science: It is essential to understand how to analyze and manipulate large amounts of data in order to train and improve algorithms. Skills in algebra, calculus, statistics, and machine learning are required to design and test AI models.
- Machine learning experience: Knowing the different machine learning techniques and algorithms is essential. This includes knowledge in neural networks, language processing, and clustering and classification algorithms.
- Knowledge of infrastructure and software architecture: You need to understand how to design and build a scalable and secure system that is capable of handling large amounts of data and user traffic. This includes skills in software development, database and storage, and information security.
- Marketing and advertising knowledge: It is important to have a good understanding of how marketing and advertising campaigns work in order to design an AI platform that is capable of improving them. A lot of performance analysis is required to design and measure the success of AI campaigns. That is, a great combination of knowledge in Performance and Brand Marketing.
- Communication skills: It is essential to be able to communicate effectively with the different members of the team, as well as with clients and users. This includes presentation, writing and negotiation skills.
The above, we could well define it as the characteristics of a marketer of the near future.
There is another very important point: data. I have participated in few data and artificial intelligence projects given the short time in which this is being developed, one of them as part of my Master’s Degree in Data Science and another that I could not complete in a previous job. The initial intention in both projects was to focus on consolidating the data and creating a platform to make it available so that the algorithms could do their homework later. The accessibility of the data, even if it comes from different sources, seemed essential to me in order to be able to create models that could solve problems that were not even known to exist.
They tell you this from day one: the quality and reliability of the data are essential for the development of an artificial intelligence platform. Algorithms need high-quality and accurate data in order to learn and improve, otherwise they can negatively affect model performance and results.
One of the main reasons data quality is important is that algorithms base their decisions on the patterns and trends they find in the training data. If these contain noise, outliers, or missing data, the algorithms can learn patterns that are not relevant or that are not general in relation to other data sets.
The reliability of data sources is also essential to ensure data quality. Data sources need to be reliable and accurate, and quality checks need to be carried out to ensure that the data is accurate.
Take, for example, a database of registered users. To ensure the quality of those users, data cleaning practices must be carried out, such as eliminating duplicates, correcting spelling errors and getting missing data, this is usually solved with promotions that invite users to complete their information and receive a benefit in return. It is also important to perform exploratory data analysis to detect unusual trends and patterns, and to assess the quality of the data relative to the end goal.
This technological revolution is demanding new skills from us. It is a new reality that we have to live with or, for some, face. And as we have been able to analyze, it is not something so simple. The use of AI will not be limited to the creation of articles, copy, visual content or customer service. It goes much further, it can positively impact businesses that know how to implement the necessary structure for it. This is only the beginning.
Maybe this article you are reading was written with Artificial Intelligence, maybe not. The time begins when it will begin to be difficult to see the difference.