Artificial intelligence in marketing: History, definition, types, examples and benefits

By Yahong Zhang | March 18, 2021

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This article has been updated in May 2024 by Sabrina KHADIVI

Artificial intelligence in marketing

According to research, global AI market size is said to reach $305.09 billion by 2024, while its market size is expected to reach $738.80 billion by 2030.

In addition, the state of AI by McKinsey shows that 50% of companies interviewed have adopted AI in at least one business function, mainly in service operations, product or service development, and marketing and sales. Actually, 17% of those using artificial intelligence are deploying this technology for customer-service analytics and 14% for customer segmentation.

Last but not least, more than two-thirds of respondents who report adopting each of those use cases say its adoption increased revenue. Within these functions, the largest shares of respondents report revenue increases for inventory and parts optimization, pricing and promotion, customer-service analytics, and sales and demand forecasting.

AI technology helps to not only increase revenues, but also improves conversion rates and customer experiences for online shopping. According to a research by Evergage, when it comes to the benefits of AI-powered personalization, 63% of respondents mentioned increased conversion rates, while 61% confirmed better customer experiences.

Definition of artificial intelligence

Artificial intelligence refers to the simulation of human intelligence in machines, which are designed and programmed to think like humans and mimic their actions, focusing on elements like speech recognition, problem-solving, learning and planning.

What artificial intelligence does is that it uses machines to rationalize and execute tasks the same way a human would, with the purpose of achieving a specific goal. This technology offers machines a high level of autonomy, resulting in the effective execution of iterative tasks.

The history of artificial intelligence

The emergence of artificial intelligence is attributed to scientists, mathematicians and philosophers who have explored the possibilities.

One of them is Alan Turing, the polymath who understood that computers are able to use available information to identify patterns which helps to solve problems and contribute to making decisions. He approached the topic in a 1950 paper, called “Computing Machinery and Intelligence”, where he discussed building intelligent machines and testing their intelligence.

The actual term “artificial intelligence” was coined in 1956, by John McCarthy at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI). Later in the 1960s, the US Department of Defense began to show interest in the topic and invested resources in it.

60 years later, artificial intelligence grew to unprecedented levels and the field is still evolving quickly. Many say that AI is the future. In fact, if we look around us, we may confirm that it is indeed the present. While it might not take over the world like in Hollywood movies, what it may do is still extremely impressive.

How does artificial intelligence work?

Artificial intelligence goes through high amounts of data in record time, using iterative processing and algorithms that allow software to learn from patterns and examples in the data. Of course, different subfields of artificial intelligence work differently. Here are some of them:

Machine learning is the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. It is the core of Artificial Intelligence and more importantly the fundamental way to make computers intelligent. Therefore the applications of machine learning span all fields of Artificial Intelligence.
Deep learning is a machine learning technique which, unlike machine learning, structures algorithms in layers and creates artificial neural networks that make decisions on their own. Deep learning needs a very big amount of data to deliver results.
Natural language processing, also known as NLP, is a science which breaks down languages, learns from them, and responds accordingly. It combines various technologies that help achieve the goal to enable natural language communication between humans and the computer. Applications include machine translation systems and human-machine dialogue systems
Computer vision refers to the ability of a computer to recognize objects, scenes, and activities from images. It has a wide range of applications, including imaging analysis in the medical field, face recognition, public security, security monitoring and so on.
Robots will be able to achieve self-determination, if successfully combined with computer vision and Artificial Intelligence, and to predict what is going to happen and act accordingly based on potential changes in the future.
Speech recognition is to convert speech into words and then identify, recognize and process the words. Main applications of this technology include medical dictation, voice writing, PC voice control and telephone customer service.
Cognitive computing mimics the human brain by analyzing imagery, texts, speech or objects like humans do and tries to offer similar output.
Neural networks imitate human neural cells and process data the way people do.

Types of artificial intelligence

Depending on capabilities, artificial intelligence is split into:

Narrow AI, also known as Weak AI, is limited to one narrow task and operates with predefined functions. This is the case of tools like Siri, Google Translate, image recognition etc.
General AI or strong AI understands, learns and performs the same sets of tasks that human beings can. Examples of general AI include self-driving cars.
Last but not least, Super AI is the one that surpasses human capabilities, and which may make rational decisions, make art or develop emotional relationships all by itself. Humanity has yet to achieve artificial super intelligence, but, once it does, this will make a breakthrough in the world as we know it.

Benefits of artificial intelligence

Of course, there are several benefits of artificial intelligence:

AI reduces the chances for errors and ensures greater precision, since unlike humans, computers don’t make mistakes unless they are programmed. It contributes to better decision-making, because it eliminates emotional factors and relies its choices solely on data and patterns.

AI can be used 24/7 without affecting efficiency and success rates. It has no downtime and it doesn’t need to rest. Moreover, it can successfully perform several tasks at once and takes on repetitive tasks that humans find unappealing.

The development and democratization of this technology among the general public has made it possible to highlight a powerful tool. It can be found in various forms, including chatbots, task automation, assistance and experience personalization.

Artificial intelligence can handle dangerous tasks that could harm humans. For example, they can defuse bombs, go to space etc.

How to use artificial intelligence in marketing?

1. Programmatic advertising that delivers conversions

Programmatic advertising uses AI to perform the following actions:

Analyze and narrow audiences for complete ad serving personalization.
Use predictive analytics from past campaigns to assess the performance of elements, from the creative direction to ad format, copywriting, fonts, colors, buttons etc.
Gather and process data to develop insights on audiences, bidding strategies for improvement.
Process on-page information and identify where ads should be placed.

According to eMarketer, ads in value of $81 billion (88% of all US digital display ads) will be transacted programmatically by 2021.

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Luxury brands are using programmatic advertising to increase the efficiency of ads and to compete with not only competitors, but also with resellers and wholesale stores that carry similar products or even their own brands.

According to Media Radar, programmatic spending among luxury brands increased to 42% of all online ad spending, some of the top luxury brands being BMW, Lexus, Mercedes, Swatch and the Kering group, which includes names like Gucci, Saint Laurent etc.

Even more, the luxury categories in which brands have heavily invested under the form of programmatic advertising, are imported luxury cars, women’s fashion, fine jewelry and fine watches.

2. AI helps to offer personalized digital experiences

A personalized approach is extremely useful in the luxury world where consumers need to feel unique, relevant and taken care of. While doing this in store is a habit, because each salesperson may address the shopper individually, providing a personalized digital experience requires a much more advanced approach to data.

By integrating artificial intelligence into digital shopping experiences, AI helps brands to adjust and respond in real time to what users are searching for and to offer them personalized recommendations, for example, a certain style or color or size, a recommended store nearby etc.

Luxury watch VTO by Hapticmedia

It’s the very same principle used by Amazon and Netflix, which take into account each user’s preferences and histories, when promoting products or shows to them. Indeed, AI enables marketers to maintain the same level of personalization that customers may receive in store.

According to a research by Evergage, when it comes to the benefits of AI-powered personalization, 63% of respondents mentioned increased conversion rates, while 61% confirmed better customer experiences.

In fact, personalization has proven to be an effective marketing technique to increase sales and conversions.

According to Forbes Insights, 40% of marketing executives report that personalization has a direct impact on maximizing sales, basket size and profits in direct-to-consumer channels, such as e-commerce, while another 37% point to increased sales and customer lifetime value through product or content recommendations. More than one-third of respondents have seen increases in their transaction frequency as a result of personalization strategies.

An example of using AI-powered personalization is beauty brand L’Occitane en Provence, which combines machine learning and user data to understand what consumers want. When visiting the brand’s website, users could benefit from a visual experience which was similar to a social media feed of products, that have been curated particularly for them.

Another example of the use of AI in experience creation is the presentation of the revival of the Riviera collection by Baume & Mercier. The House collaborated with Hapticmedia to present the different versions of the watch.

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Other techniques used include social proof which informs a shopper of how other online consumers are behaving in a similar customer journey. They could later share the product via social media or save their picks.

This strategy exhibited for the brand a 2.86% uplift in Revenue per Visitor and 3.55% increase in Revenue Per Click.

3. Image recognition helps consumers find their desired items

Luxury marketers need to ensure that they offer consumers exactly what they are searching for. To do so, direct-to-consumer channels make use of visual AI algorithms to break down images into elements with precise visual attributes, to browse inventories and finally to recommend products to shoppers. In another word, visual AI is able to to understand each shopper’s aesthetic preferences and offer the most desired products or services.

This technology contributes to increasing conversions and is used by many fashion retailers like Asos. Consumers of this UK-based giant can use its Style Match tool to upload photos and get product recommendations from 850+ brands and 85,000+ products.

Another example with results. After implementing visual search into their website, fast-fashion brand Boohoo reported that its conversion rate doubled and that the pages viewed in one session were 135% higher than usual.

4. Voice search ensures quick and effective shopping

According to Statista, by 2024 the number of digital voice assistants worldwide will reach 8.4 billion units by 2024 – a number higher than the world’s overall population. This number is no surprise, if we take into account that they have already become a key component of the smart device industry, being an integral part of smartphones, smart watches and other smart devices.

In this context, voice search is becoming a habit for consumers everywhere, as Accenture statistics show, 38% of Generation Z-ers are willing to shop via voice-activated ordering. Therefore, luxury brands need to jump on the bandwagon soon and start using the technique in their D2C channels to offer consumers quick and effective services.

For example, through the Estée Lauder Nighttime Expert, a beauty focused-app for Google Assistant on the Google Home device, Estée Lauder offers personalized skincare suggestions through voice activation. By saying “Hey, Google, can I talk to the Estée Lauder Nighttime Expert?”, consumers may ask questions and receive skincare routine recommendations.

5. AI Chatbots offer 24/7 customer services

Chatbots are extremely popular in today’s marketing landscape, because they respond to a stringent consumer need: 24/7 customer support with immediate and personalized responses.

AI chatbots use machine learning and natural language processing to collect input from users, learn from conversations and use these information to produce their own content and conduct conversations with consumers. What is even greater about chatbots is that, with the help of complex algorithms, they are able to respect a certain style and a specific tone-of-voice, which makes a consistent representation of the brand.

AI chatbot used by Apviz SaaS Software

Luxury marketers use AI chatbots to offer real-time support and shopping assistance to approach website visitors, offer proactive information, discounts, cross sell, upsell etc.

The benefits of using AI chatbots are enormous, with the most important being that they are cost-effective, costing significantly less than a team of employees, doing shifts around the clock, while ensuring standardized approaches that respect the brand guideline.

6. Artificial intelligence for identifying emotions

Emotion AI manages to detect and interpret human emotions by analyzing texts, audio, video or a combination of all of the above elements.

This technology uses tools like natural language processing and sentiment analysis, voice emotion AI, facial movement analysis, and physiological signals to understand how consumers respond to ads, products, or certain situations.

Therefore, it enables brands to respond quickly based on consumers’ emotions, adjust their digital strategy in real-time, as well as to modify a physical store’s product display or internal flows to ensure that visitors benefit from a smooth and personalized experience that drives sales.

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Hapticmedia has over 15 years of expertise in immersive technologies including 3D personalization, visualization and configuration, engraving, Augmented Reality, Virtual Try On, and is supported and covered by LVMH, Forbes, Les Echos, Le Point, BFMTV. Check here to see our client projects with Gerlain, Kenzo, Baume & Mercier, Baccarat, Edenly or contact us now to see the visible improvement we will bring to you.