Persuading people to take action, purchase a product, or access a service (in other words, responding to a “call to action”) makes Programmatic Advertising, Marketing Automation, and Customer Care even more effective. This is what Artificial Intelligence in Marketing is and what it is used for.
How is Artificial Intelligence used in Marketing and, more generally, in relationship processes with the end customer, i.e., Sales and Customer Service? From the evidence of the latest census carried out by the Observatories of the Polytechnic University of Milan, it emerges that, to date, most of the projects linked to the use of AI algorithms in companies concern the areas of customer assistance operated through virtual assistants and chatbots.
In particular, chatbots are used by as many as 81% of organizations and are, therefore, quite widespread, as are voice assistants (83%). However, interest in recommendation systems for eCommerce is growing because of the effectiveness demonstrated “in the field” – one user in four, according to what was declared by those interviewed, has finalized a new online purchase after receiving targeted advice.
What Is Artificial Intelligence In Marketing
Artificial Intelligence is a set of hardware and software systems equipped with typical human capabilities (interaction with the environment, learning and adaptation, reasoning and planning), capable of autonomously pursuing a defined purpose by making decisions. Which, up until that point, were usually given to humans.
Instead, Artificial Intelligence Marketing (AI Marketing) is called Marketing, which uses Artificial Intelligence to interact with customers, improve understanding of the market and people, and suggest – more quickly than humans – the actions to be taken to refine marketing techniques. Persuasion.
Artificial Intelligence in Marketing exploits the most modern technologies that fall within the scope of AI, such as Machine Learning and NLP – Natural Language Processing, integrated with mathematical/statistical techniques (such as those of Bayesian networks, a probabilistic graphical model that represents a set of variables with their conditional dependencies) and behavioral marketing (behavioral targeting).
All with an evident and direct objective: to improve the ability of persuasion to lead users to ” convert ” the corporate “call to action,” i.e., to carry out an action that generates value for the user himself but which has an impact also positive for the company.
From The “Perception-Reasoning-Action” Cycle To “Collection-Reasoning-Action”
In other words, AIM provides CMOs (Chief Marketing Officers) with tools and techniques that allow them to direct the behavior of target users, those to whom a company intends to address.
The principle on which this new branch of Marketing is based takes up the “perception-reasoning-action” cycle typical of cognitive sciences, which in the field of Marketing becomes “collection-reasoning-action.”
The first pillar of the cycle refers to all those activities that aim to capture data from customers, potential customers, and, more generally, people “on target” concerning the company’s objectives or a marketing campaign.
This is where data is transformed into information and, finally, into intelligence or intuition, the central part where Machine Learning and Artificial Intelligence play a significant technological role.
The intelligence and knowledge achieved through the reasoning phase are what then allow us to act; in the context of Marketing, the action can translate into a communication or a campaign with a higher probability of persuading the target users (and therefore with superior results in terms of effectiveness for the company). A cycle that, as a whole, could be completely automated through a more widespread use of Artificial Intelligence technologies in all phases of the process, including the actions to be implemented.
The 4 Advantages Of AI Applied To Marketing And Customer Experience
Adopting an approach that integrates artificial intelligence into the marketing funnel and the user’s Customer Experience, therefore, would substantially impact the effectiveness of the strategies implemented. The strength of new enabling technologies, applied to marketing, materializes in 4 competitive advantages on multiple levels and various areas of action:
- AI is essential in the data collection phase since by integrating with the leading marketing campaign management platforms and with CRM and tools for mapping interactions with customers, also from a commercial and post-sales point of view, it is possible to extrapolate the data promptly. Also, the data collected thus obtained can generate reports highlighting patterns, new opportunities, and real actionable insights.
- It becomes possible to create predictive models, starting from the data lake mentioned in the previous point. By feeding an intelligent algorithm, all the details relating to customer behavior can be translated into predictive marketing actions, intercepting new interests and needs before they appear.
- The ROI is maximized and diversified because analyzing actions in real-time makes it possible to adjust the aim and adapt the campaigns towards new opportunities and market targets.
- The integration of human intelligence with artificial intelligence gives extraordinary results. We must not think, as many fear, that AI will replace the human factor. There is a two-way union between the two bits of intelligence, necessary and mutually positive, because, on the one hand, algorithmic intelligence has a very high degree of precision and objectivity – not being influenced by bias and opinions – but on the other, The human element has a degree of creativity and non-linearity that are essential for the success of marketing activities.
Artificial Intelligence In Marketing: Technologies And Areas Of Application
Aggregation and analysis of data (even unstructured data based on natural language) in a continuous process of learning and improvement to identify from time to time the most probabilistically practical actions, strategies, and communication and sales techniques (those that have the potential to higher effectiveness/success for individual user objectives). This is, in essence, what AIM does.
Therefore, by identifying the technological field of Artificial Intelligence Marketing, we can recognize the specific reference applications and technologies and how they can be used. The most widespread applications in marketing are the following.
Virtual Assistant And Chatbot
This is software capable of carrying out actions or providing services to people based on commands or requests received through natural language interaction (written or spoken). The most advanced systems can understand the tone and context of the dialogue, memorizing and reusing the information collected, and demonstrating resourcefulness during the conversation. These systems are increasingly used as the first level of contact with the customer for assistance through the company’s Customer Care.
Artificial Intelligence In Marketing: Recommendation Systems
Widely used, for example, in eCommerce or video and music services, they are solutions aimed at addressing the user’s preferences, interests, or, more generally, decisions based on information provided by the user, indirectly or directly. They are personalized recommendations based on tastes, such as previous purchases. These can be located at different points of the customer journey or, more generally, in the decision-making process. Looking instead at the technologies, a classification is as follows
Content Creation And Curation
The automatic creation of contents (articles, news, but also “simple” messages) and their presentation to the correct audience at the optimal time (i.e., the one where there are the highest possibilities of persuasion and, therefore, conversion of the call to action ), it is one of the most promising areas of Artificial Intelligence Marketing.
In this case, advanced data analysis systems, event correlation, natural language understanding, analysis and recognition of images, video, voice, and, transversally, self-learning techniques (based on Machine Learning systems and algorithms) come into play, which allows systems that create and propose content (reading recommendations, related images, personalized ads, etc.) to improve their proposal capacity continuously; all in a dynamic way depending on how people use those contents and the potential of each of them in terms of persuasion and conversion.
Voice search is one of those technologies that has now entered collective public appreciation thanks to systems such as Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, and Google’s Google Now. CMOs need to know that these technologies are changing SEO – Search Engine Optimization techniques and strategies and that voice will be one of the most impactful elements on organic content traffic. From a Marketing point of view, this trend should be “ridden” by exploiting chatbots and virtual assistants based on Artificial Intelligence to “guide,” advise and persuade users.
Machine Learning algorithms represent the technological basis through which to model and analyze the purchasing (or action) propensities of target people to distribute advertising and communication advertisements in a more targeted way.
Not only that, but it is also through Machine Learning that companies will be able to have more careful control over the purchase and distribution of their adverts on automated platforms such as those, for example, of Google (for example, by more accurately identifying the sites and the types of users who browse them to verify their reliability or alignment with the positioning, strategy, and reputation of their company).
Target And propensity Modeling
The success of a campaign, a strategy, or a Marketing action depends first of all on the correct identification of the target audience and on the analysis of the propensity of the target people to carry out a specific action (object of the Marketing proposal). And it is precisely on these aspects that Artificial Intelligence Marketing can maximize its business potential.
Machine Learning algorithms, in this case, represent a turning point compared to the past because they enable continuous improvement (based on the analysis of large amounts of data and persistent learning) in times and with precision unimaginable for human beings.
Propensity modeling then opens the door to further analytics specific to Marketing actions, such as real-time pricing and rating of activities with the highest probability of success. Activities that translate into faster Marketing actions/activities are cheaper for the internal organization and more effective for the business.
Marketing Automation usually incorporates a series of rules and (automated) activities that marketers and CMOs need to manage and optimize demand generation, i.e., the process of acquiring and managing potential customers (until their “transition” into actual customers ), which includes lead generation activities ( acquisition of potential customers), lead nurturing (care and management of these prospects) and sales conversion (the transformation of these users into actual customers for the company).
Also, in this case, the reference technological base, remaining in the area of Artificial Intelligence, is Machine Learning, which analyzes all user data (coming from any touchpoint and channel) and estimates the most effective lead generation and nurturing activities. Suitable and with the highest probability, once again, of being translated into effective sales conversations.