The latest developments in artificial intelligence (AI) are influencing almost all aspects of business operations. One of the most significant areas in this respect is marketing, where artificial intelligence can have a hand in all stages of the customer’s journey, from initial consumer awareness through to lead generation and sales.
AI and Marketing: Hyper-Personalization
A business’s engagement with customers, whether these customers are other businesses or individual consumers, can be greatly improved by hyper-personalization, where clients get personally meaningful experiences at every touchpoint (this means fewer generic landing pages or email templates). AI can be the key to making such hyper-personalization happen.
For example, Netflix uses AI to support content creation and viewing suggestions. Its recommendation engine matches a piece of content to potential viewers based on their online behaviors, whether or not they have Netflix profiles.
Approximately 75-80% of viewer activity on Netflix is affected by the recommendation algorithm.
Sephora is another example of how companies use AI to influence marketing. The company’s AR tool, Sephora Virtual Artist, enables customers to try on shades of lipstick, eyeshadow, and other makeup products sold at Sephora. A new feature, called Color Match, uses AI to help customers find the right color shade for their skin tone by means of an uploaded photo. The system highlights specific brands to address the customer’s needs based on the photo and other input. Since its launch, the Virtual Artist app has seen 200 million shades tried on, and over 8.5 million visits (Tech Republic).
AI’s Limits & Marketing Science
While the potential for AI as a marketing tool is immense, a recent working paper on AI out of Harvard Business School indicates that AI and machine learning also have substantial limitations. According to lead researcher, Tomomichi Amano:
“While we can query our machines, our machine representations do not yet encode the ‘soul’ embedded in the marketing researcher.”
For example, consumers generate massive amounts of unstructured data that is shared online through social media and review platforms. AI, using machine learning, can process this data and identify patterns that humans would miss, to distinguish certain characteristics of consumer desires and behaviours. However, this picture of consumers is too limited on its own. AI cannot fully represent consumers’ reasoning, biases, perceptions, and social relationships, all of which profoundly influence buying choices.
To make sense of machine learning and marketing, you need human insight. Marketing team members use experience and marketing science to augment AI’s results, predict consumer preferences, and develop strategies to influence consumer decisions.
For example, in the Netflix example above, AI helps provide recommendations as to what programs a consumer might enjoy, but the number of choices can still be overwhelming for viewers. Marketing teams, Amano and his team suggest, can leverage behavioural theories to help determine how many choices consumers should be shown and in what order.
AI & Privacy: Is Hyper-Personalization Too Much?
Another issue with AI and hyper-personalization is that of privacy, or what Amano calls the “creepiness factor.” Consumers understand that, while products like Facebook are free in a financial sense, Facebook users are paying through the sharing of their personal data. Highly personalized recommendations can create the feeling of being spied on, which can alienate consumers rather than draw them in. It is up to marketing experts, therefore, to find the right balance between individualizing offerings and maintaining consumers’ privacy.
The Right AI Marketing Equation: Machine + Team
Ultimately, Amano and his fellow researchers recommend a hybrid scenario that combines the best of human insight with the best capabilities of artificial intelligence. In order to stay on top of the latest trends in machine learning and marketing, businesses need to ensure they are hiring the right marketing teams to leverage new technologies and analyze their output. This combination will help firms secure new business and continue to grow.
Resources to Support Tech and Innovation Development
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