How is machine learning used in business?
The last couple of years show us that something which was thought to be quite far away from us is actually just around the corner. For example, Amazon has developed a new way of providing retail services by developing new cashless supermarkets with the leading motto “Just walk out”. The new supermarkets are called Amazon Go and they are packed with state-of-the-art Machine Learning technologies that track customer carts (virtual ones, the items don’t need to be in an actual cart) when items are taken off the shelves.
The technology out of the science fiction movies is already here, and is waiting to be adopted everywhere! But Amazon is not the only player in the game. For a few years now, Walmart has been working to adopt Machine Learning in all of its stores across the US - its supermarkets are heavily equipped with AI to take care of busy lines and replenish out-of-stock products on the shelves.
John Deere invested in Blue River Technology, which focuses on smart agriculture through computer vision, robotics, and machine learning. In a recent interview, they list autonomous combines and harvesters as well as efficient and precise sprayers as technologies they are working on. Such innovations could reduce food cost by automation and optimization of fertilizer and water use.
According to a Gartner survey, top performing enterprises are prioritizing digital innovation during the pandemic. The pandemic has only accelerated AI adoption in business, which will continue to pay off even when the pandemic ends. In other surveys, companies respond that they use ML and ML research for data analysis and insights first and to gain competitive advantage as a close second.
Monetize data with machine learning
You can monetize data you already own by using projective learning models and algorithms to interpret it. This will allow you to detect patterns of behavior (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.
Use ML product recommendations for cross-selling or to maintain engagement
Amazon, Walmart, 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 basket or upon checkout, companies can cross-sell effectively and improve user experience at the same time. Platforms based on engagement, like Netflix, Apple TV, HBO Max, and Amazon Prime can keep users interested in their content by matching suggestions with individual preferences, thus making the users’ interaction with the platform more personalized.
Create curated, personalised experiences for your customers
This is true for any business, but retail in particular can benefit from applying machine learning solutions to personalize 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 personalization 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.
Take care of logistics with ML
Amazon’s order fulfillment, for example, includes thousands of factors per user, and an AI “can reroute, change delivery arrival times, and make other adjustments accurately and efficiently.” A ML solution taking care of business logistics can help reduce costs and redirect human workers’ attention to more important tasks.
Automate repetitive and time-consuming tasks
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. And it’s already happening. For example, chatbots became a de facto standard when designing modern websites, as customers receive responses to their questions instantly. Customer Service assistants are still present, but they focus on more complex jobs, leaving the simple tasks to chatbots.
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 behavior analysis,
- Image recognition, classification, and tagging,
- Data security,
- Risk analysis,
- Online search,
- Localization 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:
- Automated agents/bots,
- Predictive planning,
- Sales and marketing targeting,
- Smart assistants.
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. Monetizing 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 personalize them and deliver a curated journey for every user. Large retail companies can optimize costs through smart planning. 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.
Machine learning is a cross-industry trend that’s expected to stay on the rise for the next few years. How do you intend to leverage it for your business? Share your thoughts in the comments!