By Sarah Yung
Artificial intelligence is disrupting industries around the world in new and profound ways. Although movies depict AI as technology of the future, it is already behind the scenes in the entertainment and retail industries. Many companies are beginning to explore the world of deep learning and artificial intelligence and its potential to improve customer engagement. Take Netflix, an incredibly successful Internet entertainment service. Netflix utilizes algorithms rooted in machine learning to offer a personalized recommendations system. Much of their success can be attributed to their close engagement with modern technological developments.
Data Deep Learning = Insight = Value
To improve their recommendation system, Netflix must sift through vast amounts of data, using research in a field known as “big data.” Netflix trains their software by feeding massive amounts of information to neural networks, which mimic how the human brain identifies patterns. As one of Amazon Web Services’ largest customers, it is only fitting that Netflix takes advantage of Amazon’s powerful cloud infrastructure to train their machines. This technology can then be used to analyze movies and TV shows and determine how different users would respond.
Netflix’s Research and Engineering department develops software for all sorts of tasks from personalizing the Netflix homepage to choosing which artwork Netflix will use to present each movie or series. This team, in addition to developing algorithms, tests them on users to study their effectiveness. They track two user groups - one using the current service and one using a new recommending software - then analyze the long-term metrics. One of the most telling statistics is whether people stay subscribed over time. Since Netflix offers a monthly subscription, data is collected over a relatively long period of time.
Of course, Netflix isn’t the only player in the deep learning game. Facebook, IBM, and Google are also making headway into this cutting edge field. Spotify, in particular, utilizes developments in the fields of big data and artificial intelligence to create the personalized playlists that they have become renowned for. For example, their Discover Weekly playlist is described as a “best friend creating a personalized mixtape.” Spotify is a data-driven company, continually acquiring data points, and they have begun using machines to manage that data to find new connections. Connections found by computers sifting through massive amounts of data are key to creating playlists like Discover Weekly.
Recently, Francois Pachet, an expert on machine-composed music, joined the Spotify team. As we discussed in a previous post - “Machine Arts” - creative computers are far more prolific than human artists. For example, the Microsoft computer XiaoIce produced over 10,000 poems in 2,760 hours, far faster than any human author. However, Spotify says they want to “focus on making tools to help artists in their creative process.” In July of 2018, Pachet released Hello World through the label Flow Records, the first music album composed with artificial intelligence. Spotify has rolled out other tools like Spotify for Artists and Fans First to help artists better understand their fan base and adjust their online presence accordingly.
Spotify continues to humanize the massive amounts of data they collect in innovative ways, like in their global ad campaign highlighting bizarre user habits. Their creative use of machine learning will continue long into the future, strengthened by acquisitions of several companies in the deep learning field. Continued investment in these technologies will allow them to glean valuable insights from their massive amounts of data - not just odd user habits.
The Power of AI in Digital Marketing
AI is also a powerful tool for those seeking to market their product or service to the public. In the age of social media, there is no better way to reach a large customer base quickly. But good publicity requires an effective advertisement directed at the correct population. With such a massive amount of data available so quickly, companies must be able to analyze it quickly. Many companies are turning to machine learning in the era of big data. Insights from data analysis give companies many resources like customized buyer profiles and optimized content based on those personas. By 2021, there is an expected spending of about $57 billion on AI platforms that can perform this data analysis at ever-increasing rates.
Using artificial intelligence allows retailers to leverage the fast-growing Internet of Things. Both the Internet of Things - a network of computers embedded in everyday objects - and social media are fast becoming an important part of our lifestyle, and will soon be very important to businesses. Data collected from users’ social media habits and daily routines can be used to create personalized advertisements, powerful tools for attracting potential customers.
Once they’ve attracted customers, retailers can also use this technology to share personalized content in real-time. It can also analyze ad performance and create targeted ads draws consumers to the retailer. Once the consumer is on the site, the software can alter the site to keep the consumer engaged - offering discounts and pulling items to the front based on their personal interests. Machines are working 24/7, unlike humans, and can make many subtle changes to websites to increase the odds of making a sale, giving in-touch retailers and edge over their competitors. A personalized website experience, tailored to the consumer’s interests and with useful customer service - perhaps facilitated by a chatbot - can turn potential customers into loyal customers.
As in every generation, innovation will quickly become the difference between businesses that sink and businesses that float. Successful companies in today’s world almost all have a strong online presence, enabling them to attract a loyal customer base. Companies like Sears and Toys-”R”-Us were not able to adapt to new ways of interacting with customers failed to establish a strong online presence, and that led to their ultimate demise. However, companies like ASOS and North Face grew substantially due to their incorporation of new technology. Both incorporated a virtual assistant into their websites, which improved the customer experience by offering personalized recommendations.
The Machine Who Knew Too Much
But how much data are you willing to give these companies? Personalized recommendations are nice, but not at the expense of your privacy. Machines require data to make predictions from in order to make the targeted advertisements that are such a powerful tool.
Google leverages their prevalence in our lives to give their customers more visibility. My mom tells stories of researching softball gloves for my budding interest, then later receiving ads for softball equipment. No human was keeping track of her searches. Google picked up on keywords in her searches and used those to create targeted ads that they later displayed.
Many are leery of the “buying” and “selling” of personal data. But this isn’t a typical marketplace and cannot be interpreted as one. People don’t own their data, and big companies aren’t technically buying or selling it. They use data as an indirect revenue-generating strategy - selling ever-improving targeted advertising. Much of our daily lives has moved into the cloud, and we are still defining the boundaries of user privacy in this uncharted territory.
Tapping into IoT
Integrating into the age of technology is no easy feat, but will allow those pioneering companies to reap the benefits. In the era of connectivity, edge computing - sometimes called IoT - will be everywhere. Edge computing is the practice of processing data where the data is generated instead of sending it to a data center. The explosion of mobile phones and other smart devices (the so-called Internet of Things) producing massive amounts of data have made edge computing a powerful tool.
Machine learning and increased computing power makes these “edge devices” extraordinarily smart, and they are constantly improving. On-device AI can provide real-time insights and predictive analyses - enabling features that are incredibly attractive to consumers. Its reliability does not depend on network availability, since data is processed on the device, making data processing instantaneous. Consumers also benefit from increased security because sensitive data is kept on the device.
The rise of 5G networks make edge computing a very exciting development. 5G offers significantly higher data rates and system capacity while reducing time and cost of transferring data. This will enable edge devices to not only process their own data, but communicate and share data with other devices. The possibilities afforded by rapid and inexpensive connectivity are taking every industry by storm. Companies that embrace AI in this new age will disrupt their industries in profound ways, while those who don’t will be left behind.
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