Improved ad targeting and e-commerce site design, personalised customer service and lead acquisition... Artificial Intelligence is transforming the marketing industry. Much more than just a tool for saving time and automating thankless tasks, because of its ability to process large volumes of data, it contributes to considerably improving the customer experience, standing out from the competition and, ultimately, promoting the growth of companies. Here are some explanations!
AI: an overview of artificial intelligence techniques applied to marketing
Having made significant progress, artificial intelligence deployment has been accelerating for several years, shaking up the worlds of marketing and customer relations.
1. Artificial Intelligence: what is it exactly?
Artificial Intelligence (AI) is a group of techniques and theories that aim to enable machines to simulate certain human intelligence traits, such as reasoning or learning. Now applied to many fields, AI emerged in the 1950s with the creation of the first neural network computer. However, it was not until 1956 and the Darthmouth Summer Research Project on Artificial Intelligence conference that the notion of artificial Intelligence as we know it today appeared.
As a reminder: AI is not the strict equivalent of automation! If one branch of AI, decision trees, its stone age, can be assimilated to simple automation of rules pre-defined by humans, machine learning technologies developed since the late 1950s, and more recently, deep learning or inductive AI technologies have allowed it to go further.
Thanks to its ability to process increasingly large data sets, AI has evolved considerably to become a real expert capable of advising and making decisions. This is an unprecedented technological advance that is encouraging tech giants such as Google, Microsoft, Apple, IBM, and Facebook to work hard to apply AI to specific fields.
2. Machine learning
For many newcomers, AI and Machine Learning mean the same thing. However, Machine Learning is a technology that allows machines to learn by testing or collecting data. Machine Learning - and its cousin Deep Learning - is, therefore, one of the AI technologies that have become widespread. Based on statistical probability, they are used when there is a large amount of data to analyse and a probability to formulate.
How does Machine Learning work? This sub-category of AI analyses large amounts of data to discover "patterns", or recurring motifs, to make predictions. In practical terms, this technology applied to marketing allows you to better understand your customers by identifying trends.
They are mainly used in acquisition or remarketing features that are characterised by the absence of data that can be collected in real-time and the absence of an immediate need to use insight.
Their main disadvantages:
- they can only account for broad trends
- they "average" profiles and are not able to individualise
- they create models whose obsolescence depends on the recency of their formulation,
- unlike humans, they are unable to generalise from one or two experiments and can only give a probability, the level of reliability of which will depend essentially on the greater or lesser volume of data available (cf. political surveys)
3. Inductive AI and situational analysis
First characteristic: unlike machine and deep learning, which operate in deductive logic, also known as "logical deduction", which relies on rules established based on probabilities to explain a phenomenon or a hypothesis about a phenomenon, the situational analysis depends on observed facts and concrete situations in real-time. It does not start from statistically pre-defined segments but defines a situation for each data received and then simply looks for equivalents in the past to obtain a reference base.
Second characteristic: it aims to generalise from a single experience or observation of a result, giving it a relative value. Machine learning will establish a model by processing its entire learning base.
In everyday life, humans use induction rather than deduction. Indeed, we most often rely on experience to make a decision. For example, when Mr Martin goes shopping, he buys Golden apples because he likes their taste. It doesn't matter if 75% of consumers prefer Granny Smiths.
In marketing, inductive situational analysis is used to better advise the visitor, not through identifying trends (as in machine learning) but through individual knowledge of each visitor, just like the salesperson in a shop.
AI: what are the marketing opportunities?
According to a study conducted by the McKinsey Global Institute, 45% of the work done by human hands could be done by AI by 2040. An impressive figure that proves that AI is now part of our landscape and that it is transforming our relationship to work and in the world of marketing. In this respect, AI presents many opportunities. It allows us to :
1. Generate leads and promote conversion
In fierce competition, behavioural data is a powerful ally in standing out and accelerating consumer conversion. Thanks to AI, Predictive marketing has become a reality! It is now possible to anticipate needs and behaviours by assuming them on a probabilistic basis, even before starting the purchasing process, and to propose a personalised offer or service according to each consumer's profile. Based on Machine Learning, this approach allows you to strengthen customer relations, create intelligent sales tunnels and reach Internet users before competitors. As a result, you improve your ROI and increase your revenues!
Another possible technique with the emergence of an increasingly powerful AI: Remarketing with the use of advertisements or targeted emails that redirect the visitor to a site in which he has expressed an interest, for example, by visiting your e-commerce site or by putting a product in his basket without buying it. A simple technique at first sight that offers the possibility of converting visitors to other sites into real customers on your website!
To facilitate the efficiency of marketing departments, marketing automation also provides practical tools for analysing and performing repetitive tasks. For example, emailing, setting up a chatbot or monitoring competitor websites are automated.
2. Offer an innovative shopping experience
AI is revolutionising the shopping experience. It is now possible to order a product or service from the comfort of your sofa using voice-activated computer applications such as Siri or Google Assistant. Connected speakers such as Alexa or Google Home also make it possible to order a pizza or buy an item of clothing without moving a finger. And to take it a step further, Virtual commerce intends to offer a new shopping experience in augmented reality!
3. Personalise customer relationship
The main asset of AI in marketing is its ability to simulate empathy. By measuring the needs and analysing visitors' feelings, AI makes it possible to personalise the customer experience and provide relevant responses.
Previously, online sales sites relied on a minimal suggestion principle. Today, AI has become a virtual assistant capable of analysing the profile of each customer according to their budget, moods, and habits. Better still, it can identify needs even before the customer can formulate them!
Read also: Artificial intelligence and marketing: five trends to boost sales
AI marketing: which technology?
Are you a bit confused between Machine learning, Deep learning, and Inductive AI?
Let's keep it simple: Machine Learning and Deep Learning will be helpful when you must generate interaction with your customer or prospect without being able to understand their situation in real-time. You will need a model that can only come from a statistic. This approach has its limitations. Predictive models need a lot of data to give you reasonable reliability, and frequent updating will be necessary to avoid obsolescence. These technologies will provide you with a working basis. Still, you will have to accept the constraints: average relevance due to their ability to reliably only bring out significant events and native obsolescence.
Conversely, Inductive AI will be relevant to address needs where :
- you can have real-time access to the data needed to understand the situation of your client or prospect
- you need to interact immediately.
Identifying in real time the expectations and motivations of visitors, inductive AI will be able to suggest products or services that have worked best with the most recent visitors in the same situation. A solution allows you to finally offer a truly individualised service, simultaneously maximising your chances of conversion.
As AI revolutionises marketing and e-commerce, Netware's solution is designed to offer high-performance, real-time, individualised marketing! Based on inductive AI technology, Netwave interprets a maximum of weak signals to reproduce what has worked best with visitors who are most like the current visitor. With its 232 trackers that consider visitors' context, behaviour, and psychology, it supports e-commerce sites in growing their turnover. Want to know more about our Netware solution? Contact us now!
