We’re in the age of exponential growth.
The digital revolution is overwhelming companies in a pervasive way, impacting on operational and organizational models, and it does so in such a rapid progression that it eludes human comprehension.
They call it disruption and it is becoming a norm that companies need to comply with, if they want to survive. Artificial Intelligence (AI) will have us rethink the way we live and work: we should be aware that it is not only a technology but a business driver, and we must prepare to face digital change.
The first important concept is Data is the new oil: in fact, it is going to be the empirical basis on which to structure business decisions; with the support of AI, it will be finally possible to clearly understand and predict the context in which the company is moving in.
Deming said: Without data, you are just another person with an opinion. That's why it's important to organize Big Data: there are data lakes, which are places where structured and unstructured data are stored. But what has changed over the last years, given that AI is not a new term?
Alessio Semoli, author of the book AI Marketing, in his speech at SMAU Padua explained how AI’s success was decreed by the intersection of three ingredients:
- Big Data
- Computational power
- Data models (algorithms)
Artificial intelligence, the big unknown
There has been a lot of talk about AI, as well as Machine Learning and Deep Learning, sometimes improperly used as synonyms. Let's clear that out: the word AI was first coined in the 1950s, and it indicates computers that have the ability to respond and reason like humans.
We can talk about two theories: strong AI’s aim is to design machines with a human intelligence, that are able to act and think as if they had a brain, while in the case of weak AI, systems are able to successfully carry out some complex human tasks, automating them.
Machine Learning and Deep Learning are two sub-sets of AI: the former refers to computers able to learn without being programmed, the latter to artificial neural networks that can understand data autonomously.
AI&Marketing, a successful combination
How do AI technologies apply to Marketing? The steps of the process are:
- Listening: the machine is able to perceive what is around it through the acquisition of input
- Understanding: it is able to analyze and understand the data it has acquired
- Learning: it is able to perform a function
- Interaction: is able to make a decision and interact with the human being.
As a result, by applying these technologies we can automate various activities and so, relieved of repetitive processes with low added value, we can concentrate on the creative parts. Work smarter, not harder!
However, AI is impacting on several areas of marketing. Let's see some:
- Chatbots based on Natural Language Processing (NLP): studies have shown that they have higher opening rates and click rates than e-mails. Messenger has an average open rate of 90% and a CTR of 30%, while emails don’t go beyond 23% and 3.3%.
They’re an excellent way of obtaining information about website visitors via pop-ups, and they are used to:
- segment the public and improve the sales of products and services,
- make content more interactive and personalized,
- develop a more personal relationship with users, increasing their loyalty and trust.
- Content Marketing: AI can support the creation of content in areas such as keyword search, topic planning, content optimization and customization, etc. But especially with Content Intelligence, algorithms matching tags of content topics with the profiles of the users who have viewed content. Among the solutions that natively integrate it, there’s the Saas DAM THRON.
- SEO optimization: AI can help identify keywords and the main topics of interest, so that a website can be structured in relevant subgroups and content can be optimized.
- Search Marketing: the way people search online is changing, just think about voice search: it is an AI application. By 2020, 50% of searches will be voice searches. Machine Learning algorithms, in order to be more and more precise, need to be taught like a child. It starts with the analysis of the words used, which indicate the user's intent (e.g. what/who, how, when and where).
- Autonomos Media buying: the purchase of online advertising space can use AI to select the right place and the right time to convey information.
- Marketing Attribution: AI solutions can help companies collect data on the brand’s perception and positioning against competitors, tracking down what drives user interest and engagement.
And that's not all, there are many other use cases that I invite you to read about in the book AI Marketing. The greatest resistance comes from the fear that AI may become "evil": in reality, technology has no feelings, so it's up to us to give it a direction with our intentions. This awareness will make us part of the change.