Machine Learning & AI

In the past, the marketing role was focused only on creating communications and campaigns that promoted products and services on traditional media, such as television, radio, newspapers etc. Brands mostly controlled the discourse, and perception of consumers’ response was slow.

The internet changed this reality and the way we do business. The traditional model of marketing actions had to undergo major adjustments.

Nowadays, using technology is deeply rooted in society’s routine. The internet deeply influences the habits of consumers who search for more speed, ease of use and, above all, customisation. Every consumer interaction produces an enormous amount of data that generates insights about the user’s language, device, time of day and history of interaction with apps, in particular. This data must be mapped and used to create information-rich profiles.

As the buyer’s expectations for more personalised, relevant and assisted experiences increase, machine learning becomes a valuable tool to meet these demands. Since there is virtually no channel or tool without machine intelligence, it is already part of the marketer’s routine. These tools enable us to understand consumer behaviour, create smarter segmentations and offer more relevant creatives, which are delivered more efficiently to an audience that is likely to take the desired action.

The fields of Marketing and New Businesses have leveraged digital intelligence and benefited from this scenario. Artificial intelligence, once a topic for sci-fi novels, is now an everyday reality, allowing machines to learn from data and simulate human behaviour.

Some modern machine learning applications in digital communication

The increasing access to information and data collection combined with the evolution of technology provide increasingly fast and accurate analyses, expanding the ability of companies to find opportunities that are more results-oriented.

In this context, machine learning has been essential for productivity gains. Teams now can spend more time on strategies and less time on operational tasks.

These are some of the most important applications of machine learning in digital marketing:

• Lead Qualification: Leads undergo an automatic qualification process that highlights those who are most open to the sales approach.
• More Accurate Ads: Ads become increasingly personalised and compatible with each customer profile, generating immediate performance gains.
• Chatbot: The chatbot uses customer data to answer common questions and improve responses over time.
• Recommendation System: Suggestion of items related to the customer’s interest and profile based on previous purchases, viewing habits, etc.
• Less Churn: The algorithms are designed to analyse and learn from customer behaviour, alerting account managers of churn and abandonment.

• Better Sales Performance: Reach people at all stages of the buying cycle to gain more flexibility and find the best customers.

It is important to emphasise that these technologies need the expertise of a professional with a data-driven mindset who knows how to feed the system with relevant information. After all, as Google itself says, ‘a machine learning algorithm is only as good as the data it’s fed.’

At Cadastra, our teams rely on data scientists. In other words, we have teams of strategists and business analysts with the same training as the operation team, or with a career in media, BI, or other digital marketing area. With the client teams (according to their level of digital maturity), they are dedicated to creating commercial, product and communication strategies, as well as tracking all business KPIs in real time.

After all, it is extremely important to make data-based decisions, improve strategies, personalise the communication to users and provide better customer experiences. With a team of specialists like that of Cadastra’s, making these decisions is so much easier.