Brands Using AI For Marketing
Traditional marketing strategies don’t have the flexibility, scalability, or comprehensiveness to address many contemporary organisations’ issues. More marketing teams are turning to artificial intelligence (AI) to stay competitive due to rising digitalization and an always-on audience.
It would be best to look at the available third-party AI solutions before rushing to hire a data science team. However, even though many vendors use the word “AI” in their sales pitches, they lack recognised research and engineering teams that can commercialise and apply state-of-the-art AI research.
This article will tell you about businesses that have established themselves as AI and ML experts and are redefining marketing strategies with cutting-edge AI-driven solutions. However, before we commence, you must understand what AI is in marketing.
What is AI in Marketing?
Artificial intelligence marketing (AIM) uses consumer data and AI concepts like machine learning to forecast your client’s next move and enhance the customer journey.
Advances in AI make it possible for corporations to do so more successfully. AI may help businesses attract, nurture, and convert more prospects, improve the customer journey, and design more effective marketing tactics.
AI in marketing is frequently perceived as science fiction rather than realism. However, it is now a fact. Only 30% of marketing professionals used AI in 2018, according to Salesforce. IDC projects that by the end of 2022, global spending on artificial intelligence hardware, software, and services will surpass $500 billion.
The time is now if you haven’t looked at how AI may be used in marketing. We’ve chosen eight top-notch artificial intelligence marketing examples to get you started.
Five brands that uses AI for Marketing
1. Alibaba
The biggest retailer in the world, Alibaba, has opened a physical “FashionAI” store in Hong Kong to use artificial intelligence to enhance the fashion retail experience. Alibaba equipped its stores with smart mirrors that display clothing information and suggest complementary products, as well as intelligent garment tags that recognise when an item is touched. Additionally, Alibaba plans to link its physical store with a virtual wardrobe app so customers can view the clothes they tried in-store.
Alibaba is using technology in response to shifting customer expectations. A National Retail Federation survey found that while 66 per cent of consumers say brick-and-mortar retail has improved, 80 percent of consumers believe that shopping technologies and advances have enhanced their online purchase experience.
Since then, they’ve produced quite the app. It records all purchases, including when and where they were made. Starbucks uses predictive analytics to examine this data and provide customers with personalised marketing messages. These notifications give tips and discounts to raise customers’ average order values when they visit a nearby store.
2. Unilever
To synthesise information from many sources, such as social listening, CRM, and conventional marketing research, Unilever uses AI data centres throughout the globe. Using this technology, Unilever discovered a connection between ice cream and breakfast: at least 50 songs in the public domain mention eating “ice cream for breakfast,” and businesses like Dunkin’ Donuts already provide ice cream in the morning.
3. Amazon
Amazon was a pioneer in utilising machine learning to provide personalised product recommendations. However, it has been challenging for the brand to develop comparable capabilities to businesses that use Amazon Web Services to host their websites.
In June 2019, Amazon announced the general availability of Amazon Personalise, which offers AWS clients access to the similar machine learning technology used by Amazon.com for use in their apps.
The Amazon team has enhanced Personalise’s capabilities since its first release to the point where it can now provide up to 50% better suggestions across a wide variety of quickly evolving product kinds, including books, movies, music, and news articles.
Brands like Domino’s, Yamaha, Subway, and the wedding planning service Zola already utilise personalise to highlight musical instruments, highlight products in retail catalogues, provide ingredient and flavour suggestions, and come up with original style combinations.
4. Quantcast
To allow AI-driven audience targeting and measurement, Quantcast has developed Q, which it claims to be the largest audience behaviour platform for the open Internet. Every day, Q actively counts over 100 million online and mobile locations. Real-time audience changes can be reflected upon, and invisible human patterns can be found.
How does it work? The customer tells Quantcast the characteristics of their intended audience or uses tagging to identify their current audience. Quantcast builds a customised model for these customers using millions of data points (e.g., demographics, pre-search behaviours, past purchases). Finally, it locates audiences that fit this description and communicates the client’s message to them at the ideal time.
Numerous positive case studies demonstrate the potency of Quantcast’s method. Because of their precise target selection, Quantcast, for instance, sent prospects to the IKEA website who were 16 times more likely to purchase typical IKEA site visitors. The world’s largest privately held cruise operator, MSC Cruises, utilised Quantcast’s solution to increase paid and organic search traffic by 167 per cent while also receiving important insights into potential audiences that the company had never before explored.
5. Affectiva
Affectiva uses voice analytics and computer vision to recognise human emotions. One of the many applications of their technology is market research.
Affectiva Media Analytics can help you enhance your brand content and media spending by analysing unfiltered viewer reactions to advertisements, videos, and TV episodes. Customer emotion data is essential for predicting important advertising success metrics, including sales growth, buy intent, brand recall, and the likelihood of sharing.
As viewers watch your video, the Affectiva Emotion AI technology tracks their emotional responses in real-time to provide you with this data. The results are shown on an intuitive dashboard.
Affectiva helped the Mars brand optimise its advertising strategy by conducting a significant research study that connected facial expressions of emotion and emotional reactions to sales effectiveness. Over 1,500 people from France, Germany, the United Kingdom, and the United States had their faces captured as they watched more than 200 adverts. After evaluating each participant’s emotional responses and combining this information with data from a self-report survey, Affectiva could predict short-term sales with a 75 percent accuracy.
Concluding Words
Insightful analytics, customised product recommendations, and technology-enabled advice are just a few of the critical advances in the overall customer experience that AI in marketing is currently allowing. This article provided examples of how well-known MNCs use AI in marketing worldwide.
