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Data, AI & ML
Source: Yumi Skii Instagram.
Data, AI & ML
Would you let a virtual influencer be the face of your brand?
Influence is a big business.
There are some clear advantages to working with the influencer industry, not least the wide reach of above-the-line advertising coupled with authenticity.
There are risks too. Your influencer might attempt a safari in the infamous Aokigahara forest like YouTube influencer Logan Paul, or make a tone-deaf post of their standard morning routine surrounded by foil balloons and mouth wash like Scarlett London.
The International Data Corporation predicts worldwide spending on AI could rise to more than $35bn. With the influencer market already a billion-dollar industry, and the latest ambitious predictions seeing it rise to $15bn in the next two years, companies looking to use rapidly-scalable AI technology with its influencer strategy isn’t such a crazy idea.
Human characters – the authentic touch
There are already many examples of brands who have simulated virtual influencers to represent them online.
Japan’s been the big player, with Liam Nikuro by 1sec a jet-setting J-pop star said by his creator, Hirokuni Genie Miyaji, to be a digital heartthrob with “a face like Justin Bieber’s, but more Asian”. Imma is another virtual influencer model, currently with 247k Instagram followers, created by mapping the parameters of her face to a real model.
Businesses are using virtual influencers to get closer to customers. Fashion-forward digital avatar, Daisy who, for online luxury retailer, Yoox went from being the store’s virtual styling app to taking full presence on its Instagram account interacting with followers and customers.
Surprisingly, even though authenticity has been the watchword of the influencer world that arose this decade, virtual influencers, after all, are limited to the pithy or informational copy they’ve been programmed with. They are essentially collections of pixels that somehow manage to give people what they’re looking for.
What are virtual assistants made of?
Dudley Neville-Spencer heads up the Virtual Influencer Agency from London, an example of the growing industry of consultancies designing this new tech phenomenon. To create compelling virtual influences for brands, his team uses narrative design techniques, applied psychology and AI tools.
Listening techniques and machine-learning analysis are common ways virtual influencer consultancies conduct intensive research into the tastes and attitudes of their target markets. They may also use tools like IBM Watson’s Tone Analyzer to fashion a character with determinants based on age, gender, tone of voice and aesthetics fitting with whatever audience they are pursuing.
Natural language processing tools – software that simulates written human language – is used to generate the virtual influencer’s responses to followers or the captions in their social media posts.
The ‘personality’ behind the influencer is built upon this data-derived foundation. Virtual influencer ‘scriptwriters’ dream up a fictional basis and backstories to provide motivation for the character’s actions.
Neville-Spencer advocates for this emerging field, saying virtual influencers can go far beyond merely acting as brand ambassadors. He predicts, “Within four years, the majority of those under 30 will be engaging with machines in a personal, emotionally connected way through multiple touch points…. [And] Any squeamishness towards being “emotionally connected” to a machine will be become redundant, as virtual characters become part of our lives.”
Many types: Virtual influencers and assistants
Virtual influencers don’t live forever, and not all are designed to be social media influencers.
They might be given a life cycle’ which is a series of story arcs the team builds on to develop their engagement with followers.
Virtual assistants, typically more interactive, fielding basic customer enquiries, have been created, by retailers, like Yoox’s Daisy, or as customer assistants at banks.
New Zealand’s Soul Machines designed Yumi as the “first digital face” of premium skincare brand SK-II.
Yumi is the world’s first ‘autonomously animated’ digital influencer – that is, a character animated in real-time.
Why would business use them?
New Zealand-based virtual assistant originator, Soul Machines, reports a great amount of interest in digital assistants across many functional industries not just health and beauty, such as automotive, education, finance.
Virtual influencers are predicted to play a role in business strategising too. Liam Nikuro, for example, was developed from a composite personality drawing on market research and modelled on real people in Japan and the US. 1Sec are using Liam Nikuro in business partnerships, including recently with the NBA to produce content such warm-ups and behind-the-scenes views of the teams.
Soon, virtual influencers like Liam Nikuro won’t just be pixels. Miyaji has confirmed there is a big possibility 1Sec will use AI for Liam’s voice in the near future.
Data, AI, BI & ML
Artificial Intelligence and Machine Learning are the terms of computer science. Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore it is an intelligence where we want to add all the capabilities to machine that human contain. Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
