We have already explained some of the most frequent uses and the advantages of machine learning applied to digital marketing.Let's see other tasks that this technology simplifies:
A recommendation system
uses predictions derived from machine learning to generate the products that a customer is most likely to buy. With this information, personalized offers of "recommended products" and "similar products" are sent to the public on the web.
The segmentations allow you to create that target audience
that is more likely to buy the products offered by the business.
Thus, we allocate the advertising budget
to generate content of interest aimed at those users who are more likely to buy our product or service.
Segmentation allows you to show ads to users after they perform a certain action or search.
To understand the revenue loss associated with customer churn, we use machine learning. This generates percentage or monetary terms
But these predictions also allow responding to a customer's intention to abandon your product or service through loyalty actions
, to prevent that from happening.
The system predicts when the time is right to do a specific task, based on the context data it analyzes. For example, it can warn us of the cessation of production of a certain product, the increase in sales, etc.
- Voice and text customer service
Through machine learning patterns, words or phrases can be identified, and then the appropriate response to them is generated.
- Information security and anti-fraud
Fundamentally in e-commerce sites, machine learning helps us detect fraudulent users
through their access, profile, historical data, etc. It also predicts cybersecurity breaches
and allows you to reduce the percentage of thefts through false credit cards.