All You Need To Know About Applications Of Machine Learning
We’re giving mindful information on applications and uses of machine learning. We love to keep up to date with all these technologies, adapt to them as quickly as possible and find a way to take advantage of them on a day-to-day basis, so today we bring you 8 techniques and applications for using Machine Learning in the field of digital marketing, so that companies of any size can use them and their use is not only relegated to technology giants.
As we discussed in a previous post, artificial intelligence (AI) is a topic that is the order of the day, however, the AI is a broad term covering different technologies such as voice recognition and image, machine learning techniques and semantic search.
Uses And Applications Of Machine Learning:
AI is also being used to increase conversion rates, AI-based tools are more effective than more traditional A / B testing tools, allowing us to simultaneously test a variety of page elements and variations with less traffic than usual. which is normally required for an A / B Test to be statistically significant.
This process is also known as Machine Learning and involves evolutionary algorithms that try to find the most optimal combinations and stay with the set of solutions that provide the best conversion rates.
Smart content allows users to be more engaged by showing them relevant content. This is something very common to find in sections of some pages that show us “Customers who have bought product X have also bought product Y”, but it can also be applied to blog content and message personalization.
The use of this technique is very useful for businesses based on the user’s subscription, which the more you use the service, the more data it generates so that Machine Learning algorithms can offer content recommendations based on historical data. We just have to think about Netflix and its recommendation system that continuously shows series or movies that the user might be interested in.
The programmatic buying media can use propensity models generated by machine learning algorithms to more effectively target your ads to more relevant users. In addition, AI can help us recognize sites where the ad may have less impact and directly remove them from the list of site ads that can be placed.
Propensity models can be applied to predict when a user is most likely to convert, predict the price at which a customer is most likely to convert, or even predict which customers are most likely to buy on a recurring basis. This technique is called predictive analytics, as it uses analytical data to make predictions of how customers behave.
The key and most important thing about this model is that it depends on the data you have used to create it, therefore, if we have a data set with errors or with high levels of randomness, the predictions will lack precision.
Continuing with the propensity models generated by Machine Learning, they can also be trained to qualify leads based on different criteria, so the sales team can establish the potential that a lead has and whether it is worth dedicating efforts and resources to it.
This technique can be interesting in B2B businesses with sales processes in which a previous consulting phase operates and in which each sale takes a considerable amount of time for the team. Therefore, by reaching out to the most relevant customers, the sales team can save time and focus their efforts where they are potentially most effective.
Machine learning algorithms can use large amounts of historical data to establish which ads work best, on which people, and at what stage of the buying process. Using this data we can serve the most effective content and at the right time. By using machine learning to constantly optimize thousands of variables, we can achieve more effective ad content and placement than traditional methods, yet we will always need people for the more creative parts.
Similar to ad targeting, we can use Machine Learning to establish which content is most likely to make users visit your website again based on historical data.
Marketing automation techniques involve a series of rule flows and workflows that are triggered by the interaction of a user, but who decides these rules? With what criteria?
Most of the time it is the optimizer himself who tries to guess what he thinks is most effective, however, relying on Machine Learning again , we can really establish which are those most effective moments to establish a point of contact with the user , the most effective words on the subject and much more. These insights could be applied to increase the efficiency of automation efforts.