Marketing Automation and Artificial Intelligence. A few words of introduction.

Marketing Automation and Artificial Intelligence. A few words of introduction.

marketing automation and artificial intelligence. a few words of introduction

Machine Learning is already used by many of the world's renowned companies. We use its potential more and more often, buying services of entertainment platforms such as Netflix, Google but also business solutions like CRM, or Marketing Automation. On the one hand, we use the recommendations of films that are displayed while using the application on the other hand, and we receive lists of the most potential customers along with recommendations as to the methodology of conduct that approximates customers to the conversion.

All this is the effect of using Machine Learning technology. Facebook, Spotify, Google, Salesforce, Oracle or Uber are just a few examples among the huge number of brands that have applied Machine Learning in their daily practices.

New standards in Marketing Automation and Artificial Intelligence

Observing the direction of changes in today's business and analyzing the dynamically growing requirements of customers, manufacturers of all kinds of goods and services have started the race for customer's satisfaction with the cooperation of technologies based on AI algorithms. The suppliers of these solutions have already accepted that Machine Learning, Deep Learning and Artificial Intelligence supporting or even creating business processes have become not an advantage of the technology platform but a necessity and a standard.

Supplied systems, regardless of the manufacturer, implement actions in both inbound and outbound models, effectively supporting both types of activity. Mechanisms of e-commerce and behavioural analysis, process comprehensive information packages about visits, transactions, shopping paths, products that are in the interest of a potential customer. This process aims to analyze data through a self-learning mechanism to find correlations and dependencies between the occurrence of parameters.

Simple solutions

Put it simply, while working in direct sales, we observe the behaviour of individual customers and in our minds, we build specific models of consumers visiting our facility as well as their behaviour patterns. By heart, we know the grimace accompanying the customer when the price of the product is not satisfactory for him. We know that a well-dressed man is more likely to be tempted by expensive perfumes, and a woman buying folic acid can calmly offer a set of lovely rompers. However, these are elementary algorithms and simplifications that we create in our head, incomparably less complicated compared to what analyzes are made by artificial intelligence. So instead of blindly "shooting" based on our unreliable senses, maybe it is worth trusting them less susceptible to mistakes and subjectivism, the mechanism?

Machine Learning. What?

Generally speaking, Machine Learning (ML) can in turn be defined as processes consisting in searching for patterns, dependencies and immutability in large data sets (historically and in the future). The assumption is that the machine learns on its own based on the information collected and data sets. Based on found examples and examples, ML allows answering the question of how to solve a given problem and what steps should be taken to achieve it. Similar to the human being, his potential comes from experience, or more precisely, from information that is collected and analyzed from outside.

Machine Learning solutions, which are increasingly used in business, use collected information to create countless customer models, considering their general characteristics. To each of these models, they adjust the appropriate products and communication channels on a real-time basis. It is the most efficient way of acting against customer touching points within the Customer Journey.

The standard set of ML functionalities covers:

  • Big Data analysis supported by the most advanced algorithms and structures
  • Offering products tailored to the individual preferences of each customer
  • Omnichannel personalization in real time
  • Collecting and analyzing all data within one platform
  • Processing, analyzing and improving activities and results
  • Optimization of the use of resources dedicated to marketing activities
  • Knowledge of customer preferences and the ability to predict which products will be sold in the near future
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