Personalization of the browsing and purchasing experience in. Cmni channel contexts, chatbots and virtual assistants, virtual and augmented. Reality: artificial intelligence is revolutionizing the relationship between brands and consumers, offering the former increasingly essential tools and applications to carry out customer-centric strategies .
Although data analysis has always been a relevant aspect of marketing, it took time and significant paradigm shifts for companies to realize its value, to the point of considering it a strategic asset .
It is precisely in this context that macedonia phone number data artificial intelligence and machine learning have revealed their full potential, especially in the analysis phase, allowing them to reach levels of precision and efficiency even in the predictive aspect .
But what do we mean by predictive analysis and what is its relevance in carrying out strategies aimed at personalizing the customer experience?
Let’s find out together!
Predictive analysis and artificial intelligence: the winning combination!
Market trends, pricing policies, customer base analysis: marketing and sales departments have always been looking for models and tools capable of offering something more than a purely descriptive analysis of the context.
This is precisely what artificial 50 ideas for smm content intelligence and machine learning make possible today: analytical activities that are no longer limited to drawing up a well-defined picture of the present or the past, including the characteristics, habits and browsing and purchasing behavior of users and customers, but which allow them to anticipate their needs, expectations and problems.
It is precisely in this ability to intercept and anticipate desires and needs, even latent ones, that brands strengthen their relationship with their consumers .
The goal remains loyalty: the more uab directory we know about the customer and the user we are dealing with, the more we are able to implement strategies and activities aimed at improving the customer experience.
More specifically, predictive analytics relies on the use of artificial intelligence and machine learning algorithms to process large amounts of data. The goal is to identify patterns that can predict future behaviors.
As can be easily understood, this type of approach goes through a series of well-defined phases such as:
- identification of the objectives to which the analysis must respond;
- data collection and standardization;
- data analysis and identification of recurring patterns and trends;
- predicting outcomes and developing models capable of predicting user and customer needs.
The use of predictive analysis thus makes it possible to develop ideal user/customer profiles and to identify, at the strategic stage, the most rewarding activities for each specific group and profile.