HOW DO COMPANIES USE MACHINE LEARNING?
Machine learning has become the new trend, sweeping the business world. Machine learning is an aspect of Artificial Intelligence that enables the systems to automatically learn from an experience without being programmed. The hype of this technology is mainly driven due to an increase in data, availability of cheap storage, open-source libraries and greater horsepower. The growth of cloud-based tools and services like Amazon Redshift that reduce the cost of storing data have skyrocketed the data generation and data storage. Further, open-source libraries like Google’s tensor flow allow a larger audience to access these edge cutting algorithms, and the greater horsepower in terms of the evolution and growth of cloud-based platforms and optimization of custom hardware enables applications to run faster. Ultimately, these three factors make machine learning an attractive option for organizations to adopt.
Companies use machine learning mainly for business forecasting, to acquire customers, to provide excellent customer support, and for the management of people. Companies that use machine learning to expand their customer base by understanding the need of the prospect customers and providing them with tailored recommendations. At times, prices are adjusted and new deals are offered on the go if there is a good probability that a new customer can be acquired. Companies like Amazon and Target offer enticing brand deals for a limited time to lure new customers, and they keep track of the journey of their customers to provide customized recommendations through this technology. While selling products is important, retaining these customers is more important. In order to keep customers happy and maintain their trust in the company, customer support has to be top notch. Machine learning has revolutionised customer service by introducing chatbots that can efficiently handle most of the customer complaints without any human interference. Business forecasting, which means keeping a track of sales performance and being prepared for uncertain demands or outcomes, is made possible by machine learning. Being able to anticipate unplanned situations makes the company more robust. Further, hiring employees has been simplified with the help of machine learning. Candidates whose resumes match the job description are forwarded to the HR rather than the HR manually going through each resume. Companies have adopted machine learning to introduce efficiency and robustness in their system.
Many top IT companies use machine learning in very innovative ways. Some of the examples are the followings.
Twitter’s hype is driven by its ability to curate a timeline with its user’s interests. Machine learning enables Twitter to keep track of the interests of its users based on the photos they liked, the videos they watched, and their search history. The tweets that appear on the timeline are then ranked based on each individual’s preference. If in the past, the user has appreciated certain types of tweets their keywords are stored under their information, and any tweets that include these keywords in the future appears on the user’s feed. Twitter also provides the users with “This tweet’s not helpful” option to remove certain types of content from their feed, which they might not be interested in anymore. The massive heaps of information about each individual are processed with the help of machine learning to provide the most engaging content to its users.
Have you ever forgotten the name of the song and searched it with the lyrics that you barely remember on Youtube? The song name and its video magically appear on the search results. This magic is made possible by leveraging machine learning. In the past, Youtube used to index its videos by the title and description provided by the uploader. Machine learning has transformed Youtube’s search engine. It can identify the content of a video and assign keywords to it. That is how Youtube can search for the song with the lyrics. Youtube also has the feature of a thumbnail attached to the videos on its search result screen. As the users hover over the thumbnail, a snippet of the contents contained in the video is available to the user so that they do not have to open every video to check if it contains the content they were looking for. The recommendations on Youtube also utilize machine learning to provide suggestions that will pique the interest of the users. Like Twitter, they keep a track of the videos that were liked, commented and seen by the user. Using this dataset, they curate a suggestions list for each individual user if the user is logged in with their account. In case the user is not logged in with their account, Youtube provides a wide array of recommendation with the most likes or views to engage the user. The search results are also ranked based on most views. It is quite evident that the backbone of Youtube is machine learning.
Using machine learning and large heaps of tennis data, IBM has launched a new feature called the Keys to the Match for the Wimbledon mobile app, which provides predictions on the likely winner. There are three metrics set for each player that they must attain in order to win the match. In the 2019 Wimbledon quarter-final, Simona Halp was in trouble after losing the first four sets. However, after the first five games, the Keys to the Match predicted that Simona Halp had a 80% chance of winning the match because she passed the three thresholds. It turns out that the prediction of the app was accurate and Halp indeed won the match. The app takes into account how critical a certain game point is to win the match, the celebratory gesture of each player after the game point and captures the crowd noises to predict the winner. The tennis matches have become a showcase for IBM’s creative use of machine learning.
Salesforce is a software company, whose core business focuses on Customer Relationship Management (CRM) software solutions. The CRM software collects a tremendous amount of data on their customers, which is fed to train their machine learning algorithm. Via machine learning, Salesforce is able to efficiently track customer patterns and provide its clients with a report on specific potential customers to focus on as they will yield maximum benefit in the long term. Machine learning also prompts the clients about the method they should utilize to approach their client. For instance, whether a customer should be emailed, phone called or talked to in person are the decisions that machine learning can make based on the vast amount of data it stores for each customer. If these decisions are left on human intelligence, the process becomes very tedious and error-prone. Salesforce provides an analytical platform called Einstein, which with its machine learning capabilities provides its clients with custom recommendations and predictions to bolster their sales
Recently, Google has demonstrated using the Google Assistant for booking spots in salons and restaurants with conventional clarity. Machine learning will bring the human and computer closer in the interface and there exists a possibility that human intelligence could be taken over by machines for feats like booking spots, scheduling appointments, etc. Further, Google maps use real-time data to decide the least traffic route for commuters. Business intelligence has transformed the way commuters are using Google maps. Some of the navigational devices are using business intelligence to let you know in advance how much time it will take to travel to your regular destination in anticipation.
Additionally, Business intelligence, which combines the big data analytical work with machine learning, is an area where some of the sectors in the industry have seen maximum action. Here are few sectoral examples of the use of machine learning:
- Financial services: This sector is data rich. The customer transactions are being used to find customer satisfaction rate and for forecasting of the future demands for the products. Customer payment patterns for credit cards are enabling companies to predict possible defaults and take remedial measures in advance.
- Retail: This has enabled companies to manage cost, inventory and preferences of the customers on a seasonal basis. Retail sector has benefited most from machine learning and revolutionized the way costumes are brought and managed. This has enabled the retail companies to optimize the demand and supply logistics, resulting in huge savings for the companies.
- Health sector: Using deep learning, it has become possible to predict skin cancer. Based on images, computers are being trained to identify the abnormality and suggest possible remedies. Machine learning has made it possible to improvise the pharmaceutical development by integrating the lab, institutions and studies. Image-based diagnosis has radically improved and become more accurate due to machine learning.
- Weather forecasting and agriculture: Machine learning-based analysis has improved the weather forecasting, enabling higher productivity and reduced agriculture losses.
Machine learning is touching our lives in a crucial way. It has made unpredictable things predictable now. The substitution of the human race by machine for error-prone and inefficient tasks has begun, and it will further be accelerated with rising investment in the AI/ML space. The enhanced investment by the companies to develop capacity will further see corporate competition and will bring machines closer to human intelligence. It will be interesting to see how far machines are able to replace human intelligence.