It’s 2018, and we’ve gained a level of advancement in machine learning technology that no one expected this soon. Images made using algorithms don’t look artificial, retailers’ smart recommendations raise sales, and the stock market has forever been changed by predictions based on machine learning. It’s a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage as their business competitors leverage ML and leave everyone else in the dust.
The competitive advantage of machine learning is obvious
You can monetise data you already own by using projective learning models and algorithms to interpret it. This will allow you to detect patterns of behaviour (of users, customers, or workers) and make more accurate predictions. This can help you sell more, make your team more productive, or improve user experience.
Using ML product recommendations will help with cross-selling or maintaining engagement
Amazon and any other service in which users’ purchasing behaviour can be predicted based on other users’ choices can benefit from machine learning. By making the right suggestions when users add an item to their basked or upon checkout, companies can cross-sell effectively and improve user experience at the same time. Platforms based on engagement, like Netflix, can keep users interested in their content by matching suggestions with individual preferences, thus making the users’ interaction with the platform more personalised.
Customers want curated, personalised experiences
This is true for any business, but retail in particular can benefit from applying machine learning solutions to personalise the buyer’s journey. Catering to customers’ exact and individual needs can be a huge competitive advantage. “Retailers that have implemented personalization strategies see sales gains of 6-10%, a rate two to three times faster than other retailers,” writes Daniel Keyes for the Business Insider. Users expect more and more personalisation in their interactions with companies online, and ML can be the key to providing it. One option is to allow users to search by using conventional language or images, which mimics the experience of talking to a human shop assistant.
Logistics is a field where machine learning could make huge strides
Certain activities, like verifying applicants’ resumes, or matching invoices in finance departments, are important but unengaging and repetitive. Implementing machine learning to deal with them can free up workers, whose time can then be better spent on more demanding and crucial tasks. It’s possible that soon, chatbots will be able to assist workers internally and customers externally, by providing information and data analysis on the spot.
Digital assistants like Alexa have machine learning and natural language processing at their core, and their popularity is growing. ML is what allows drones to fly autonomously, and it’s the magic behind tools like online translator apps and image recognition (e.g. this app that removes cranes from cityscapes). Soon enough, even more aspects of everyday life will become saturated with AI and machine learning solutions.
Machine learning is everywhere
There seems to be no limit to the number of areas in which machine learning can boost productivity, save money, improve customer experience, or leverage data. Current uses of ML, in order of popularity, include:
natural language processing,
text classification and mining,
emotion and behaviour analysis,
image recognition, classification and tagging,
localisation and mapping.
But researchers and innovators aren’t done yet. Machine learning is a young field and we can be sure that the next big development will happen soon. The top projected future uses of ML are:
sales and marketing targeting,
Which industries can gain the most from machine learning
The list of businesses that can’t benefit from ML is probably shorter than the list of those that can. Monetising data, smoothing and speeding up logistics, and improving business processes are universal benefits, applicable across industries. At this moment, however, there are some common situations in which ML can make the biggest difference. Retailers and engagement-based platforms can leverage recommendations to deliver a much better experience and boost sales. Companies whose success rests on online interactions with users can personalise them and deliver a curated journey for every user. Finally, recruitment is a field in which automation can make a huge difference to processes and employee satisfaction, so using ML to sort through applications and deploying chatbots to ease candidates into the recruitment process is a clear way to improve productivity.