AI, Machine Learning, Personalization: A quick guide
Technology is like a foreign language: in order to master it, it is necessary to study it. The first problems arose with the use of tools that allowed the study and diffusion of technology (handling a keyboard or a mouse) or in understanding the basic functionality of a computer or any other technological tool (computer folders or sections of a website).
Today, this has changed: Technology is able to speak our language, to answer our questions, and obey our commands. As a result, technology has been given an essential role in the workings of businesses.
In fact, in today's marketing sector, the use of AI (Artificial Intelligence), with the practice of machine learning, is fundamental in order to be able to create increasingly precise and effective strategies through the use of Buyer Persona, modeled according to the needs of users, studied by collecting data and reworking them.
So by profiling people who might be interested in the product, you can further improve the quality of the product itself and receive positive feedback from buyers.
AI and Machine Learning: What are they? What are the differences?
The first traces of Artificial Intelligence (AI) date back to the early 1950s when the English mathematician Alan Turing wrote an article about a test that later became known as "Turing test."
According to this test, a machine could be considered intelligent if its behavior, as observed by a human, was considered indistinguishable from that of a person. With the work of Turing, the problem of AI was taken into account and studied, and it gave birth to new approaches: mathematical logic and neural networks.
What is AI?
The term AI refers to the ability of a computer system to perform all the operations characteristic of the human intellect, such as understanding language, learning, and solving problems. Unlike traditional software, AI is not based on programming, but on learning techniques (the system must be able to understand and reason about the data provided to it through algorithms).
As there is no universal definition of artificial intelligence, two types of AI are outlined (machinedesign.com):
Strong AI is the category that considers systems capable of becoming self-aware; over time, scientists think that systems in this category will be able to have their own intelligence, no longer like human intelligence, and superior to ours.
Weak AI represents the systems that are able to perform some human cognitive functions without reaching human intelligence; mainly it is about problem-solving or logical reasoning.
Many times Machine Learning is mistaken with AI; actually, we can define it as a subset of artificial intelligence, in fact, it is considered an application of AI.
The practice of Machine Learning consists in providing the right tools (in the form of mathematical models of data) for computer assimilation, without direct instructions.
Like AI, the practice of Machine Learning is also divided into two main branches (towardsdatascience.com):
Supervised Machine Learning is the most widely used model. In this practice, the data scientist provides the information suitable for machine learning through algorithms. The work done by the data scientist can be thought of as a parent who provides the information the child needs.
Unsupervised Machine Learning: in this method, a more independent approach is used where the computer learns to recognize processes and patterns without the help of a "parent" figure to guide it.
Where is AI applied in everyday life?
Everyday life is surrounded by objects that make use of artificial intelligence: the Osservatorio Artificial Intelligence of School of Management del Politecnico di Milano has outlined 8 classes of AI that can be found in everyday life.
Autonomous Vehicles: Vehicles with autonomous driving, without any human driver. Self-driving cars or any means of home package delivery refer to this class.
Autonomous Robots: Robots able to move or manipulate objects, drawing information from the environment around them and able to adapt to the needs required. A famous robot is that created by Boston Dynamics, which recently also developed a robot-dog.
Intelligent Objects: Objects that are able to make decisions autonomously, interacting with the surrounding environment and learning from it.
Virtual Assistants and Chatbots: Systems that are able to understand the tone and context of dialog in order to then reuse this information. This class of AI is increasingly used for a company’s first interaction with the customer (through touchpoints, i.e. advertising or communications related to the topic).
Recommendations: Strategies oriented to address the preferences of an individual, based on information extracted directly or indirectly from the user. The best-known examples are e-commerce sites (see Amazon) or even streaming sites (see Netflix or Spotify).
Image processes: Through images or videos, they identify people, animals, or things through biometric recognition. They are useful in monitoring the technical premises of utilities or even for evaluating damages to automobiles in accidents in the insurance industry.
Language Processes: They involve the ability to process a language, for text comprehension, translation, or even autonomous writing of passages, starting from input data.
Intelligent Data Processes: The last class includes all those processes that use artificial intelligence algorithms on structured data, such as, for example, systems for detecting financial fraud, for predictive analysis, or monitoring and control systems. This category is very important because it has the ability to predict future events, based on correlations, behaviors, and habits to prevent fraudulent activities.
As we can see, everyday life is surrounded by artificial intelligence. AIs have a learning mechanism that can take into account a very large number of case histories over a certain period of time and extracts personalized patterns (predictions about our tastes).
As the preferences expressed by the customer increase, it is therefore also possible to outline a profile of the customer, which is fundamental in the creation of personalized communications. The profile that is gradually outlined takes the name of Buyer Persona.
Buyer Persona: What is it?
The Buyer Persona is the representation of a typical buyer of a certain product.
BPs are fundamental for building a profile of the target. The model that is created is useful for the company to be able to identify the touchpoints (the contact points through communications or advertising) that each Buyer Persona needs.
How do you create a Buyer Persona profile?
It starts with two main pieces of information:
Demographic information: e.g., age, geographic location ( the city or place where they work and live), and income.
Psychographic (and ethnographic) information: interests, behaviors, reasons for buying, goals, and fears.
Information is retrieved primarily in two ways:
Qualitative research: through interviews focus groups or other forms of research.
Quantitative research: analysis of your own site traffic, multiple-choice surveys, or other internal company data.
These characteristics help shape the Buyer Persona, but from the product point of view, how do you give life to the perfect BP?
Marketer Adele Revella, CEO of the Buyer Persona Institute (BPI), who conceived the idea of "Five Rings of Buyer Insight," explains it to us by identifying 5 key points:
Prioritized Initiatives: determine the most compelling reasons why a prospective buyer would invest in your solution and develop strategies to trigger this process.
Success factors: the risk factors, which must always be specified, for achieving the end goal.
Perceived barriers: these perceptions depend on similar experiences or feedback from friends and family. With content marketing - a strategic marketing approach to increase sales - by sharing true stories, you can gain interest from the target customer.
Buyer's Journey: the journey the buyer takes and therefore who and what influences their choice. Understanding these influences allows a company to allocate its resources to meet customer needs.
Decision criteria: the criteria on services, products, and solutions that the consumer considers relevant and also serve companies to act accordingly and influence the buyer's choices.
This practice is adequately supported by the use of Artificial Intelligence and, consequently, the use of machine learning, which allows you to establish direct interaction with the buyer through personalized communications and videos.
To put it better, both communications and personalized videos refer to each recipient thanks to the use of data and statistics. For example, the personalized video, using an image or even an ad hoc avatar, is able to convey information about the proposed service in an engaging way and build loyalty.
How many times have you been able to customize a product to your liking on a website? Could this also be useful for marketing purposes?
In fact, product customization by the buyer is becoming a widespread practice. This process leads to the construction of a network of information and data that is useful for developing new ideas about the product.
This brings benefits both from the company's and the customer's point of view:
The Company is able to intuit the buyer's needs and preferences and then modify any strategies in order to conform to these requirements.
The customer instead has the possibility to express his ideas about the product, following the different layers provided by the company: Background, style, information, and transition.
The use of Artificial Intelligence in marketing will make communications with the customer (thanks to the continuous improvement of chatbots) smoother without staff involvement and without keeping customers waiting.
In fact, chatbots may eventually be able to carry out sales channeling seamlessly and quickly.
In addition, the use of AI in the marketing sector will make it easier to personalize products and communications towards customers in order to ensure a unique and personal experience for customers, thus giving them a more positive impression of the brand.