Machine Learning – a branch of AI, is changing the way companies utilise their Big Data reserve, which allows them to reshape customers’ behaviour. Machine Learning based on the idea that machines can learn from data and recognise patterns, which result in decisions and predictions with a degree of certainty.
Though the term Machine Learning has been around for a long time, it has proven to be valuable and profitable over the past few years. According to a report by Research and Markets, Machine Learning has brought in over $1 billion in 2016 and is still going strong until 2025, hitting the $39.98 mark, predictably. The technology is expected to be adopted by everyone from Fortune 500 firms to SMEs, and even small retail shops.
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How machine learning can benefit world-leading companies?
Presently, every sector from retail to hardware, to manufacturing and media is reaping the benefits of Machine Learning. Don’t count yourself out of the game; take a look at how top companies are doing it and are also winning it.
1. Target: Deep understanding of customers’ behaviour and future affections
The US retail giant Target has been applying machine learning in order to predict not only customers’ purchase behaviour and buying process, but also what’s happening in our lives, including pregnancy. One of their most renowned incidents is when Target implied to a father that his 16-year-old daughter was pregnant through its relentless advertisement for pregnancy. The result was that they had to dial back their initiative by running less specific ads.
It is certain that when a customer is going through a life event, for example, having a child, their buying patterns and decisions are most likely to change and be influenced. With the gathered insights, Target’s model can accurately predict which trimester a pregnant woman is in based on her purchases.
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Machine Learning’s ability to pick up and learn from humans’ routines helps to ignite perfect timing for promotions, plus planning is another benefit of machine learning since it can predict future actions and decisions based on current data.
2. Twitter: Aligning your virtual view
Introduced the new thumbnail optimisation feature, Twitter is aiming to increase users’ engagement on the platform. The social platform knows that we tend to ignore the entire photo if the thumbnail is unappealing, thereby missing the opportunity for interaction. Therefore, Twitter integrated machine learning to crop users’ photos into captivating, low-resolution preview images. Hence, increases user interaction and enhances engagement on the social media platform.
Marketing personnel can also make use of this by letting Twitter decide your image showcase. Since their analysis tools are fully developed, your marketing campaign will achieve maximum reach with attractive thumbnails. Free and effective, couldn’t be any better!
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3. Apple: Connecting devices & combining experiences
The high-tech giant is known for its close attention to device connectivity. With the help of Machine Learning, Apple is prioritising cross-device personalisation to deliver customers a smooth experience. For instance, Apple promises a future where a user’s Apple Watch might suggest an iTunes playlist to match their heartbeat goal in another app.
If your company works with smart devices, you may be able to connect multiple models to a single shared database. The application allows you to gain quality insights thus improve customers’ overall experiences. Because devices have a mutual database, Apple is able to enhance performance thanks to different approaches to analysing data.
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4. Alibaba: Personalising customer’s experiences
Another retail giant, based in China – Alibaba, has more than 500 million customers that generate an equivalent number of transactions per day. But did you know that each of those customers receives a uniquely designed shopping journey, from choosing to buying products?
Alibaba has applied machine learning to its virtual storefronts. Their AI bot, Ali Xiaomi, takes care of most spoken and written customer service inquiries. Therefore, each search result suggests relevant and custom-made recommendations to satisfy customer’s needs and encourages more transactions from shoppers. It is a constant learning process as every action the shopper takes is recorded and analysed in their database to further understand the human mindset.
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5. Spotify: Custom-made “Music taste” and smart suggestions
Spotify has been quietly implementing Machine Learning into its fan-favourite music recommendation service. Started from the “like” and “dislike” buttons in the Discover Weekly feed. To deeply integrate the Natural Language Processing (NLP) to “read” blogs, articles, and then classify artists and group them together. Combined with the Audio Analysis tool, Spotify can gather all the information about songs and artists and come up with the most suitable songs on the user’s Discover Weekly.
It is said that even Spotify is surprised by Discover Weekly’s success since Machine Learning is not their strength and wasn’t part of their plan when they first launched in 2007. Although the competition gets harder when Apple’s New Music Mix adopted the same technology, Spotify’s still winning customer’s favour with their recommendations service.
Machine learning is making disruptive innovation all over different industries. Companies like Target, Apple and Spotify are defining a future where smart services and virtual platforms become the norm.
Source: https://www.entrepreneur.com/article/310708
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