Machine Learning has gained huge prominence in the current time because of its ability to be applied across different industries to solve complex issues quickly and effectively. There are many applications of this technology, including internally, to assist businesses in speeding up the manual and time-consuming processes or improving products. And external (client-centric) applications include demand forecasts, customer service, and product recommendations.
Machine Learning solutions and algorithms are usually used in areas where the solution needs continuous improvement post-deployment. Adaptable ML solutions are dynamic and are adopted by organizations across verticals. Following are the top 7 real-world problems that ML helps to solve:
Marketers commonly face the challenges of customer segmentation, customer lifetime value (LTV) prediction, and churn prediction. Organizations deal with a huge amount of marketing relevant data on a daily basis from different sources such as lead data, website visitors, and email campaigns.
Using Machine Learning and data mining, businesses can achieve a precise prediction for individual incentives and marketing offers. With the technology, marketers can eliminate the guesswork involved in data-driven marketing.
Spam identification is one of the most valuable applications of ML. Most of our email inboxes have spam, unsolicited, or bulk inboxes, where the email provider automatically filters out the unwanted spam emails. But the question is, how do they know that the email is spam?
The answer is the trained Machine Learning model that identifies all the spam emails according to the common characteristics such as sender content, subject, and email. It is not much hard to pick out spam emails if you look keenly, as they look quite different from real emails. ML techniques automatically filter these spam emails in a very successful way.
Fraudulent bank transactions are very common these days. It is not an easy thing to investigate every transaction for fraud because of the cost involved and efficiency, which translates to a poor customer service experience.
However, using Machine Learning in finance helps to build super-accurate predictive maintenance models to prioritize and identify all types of possible fraudulent activities. Businesses can then investigate the high-priority incidents by creating a data-based queue.
It helps you optimize customer satisfaction by not challenging valid transactions and protecting their accounts. Fraud detection using AI ML technology helps banks and financial organizations save money on chargebacks or disputes as to the technology flags transactions that appear fraudulent based on some particular characteristics.
Making Product Recommendations
Recommender systems are one of the most ubiquitous Machine Learning solutions and use cases in routine life. Entertainment platforms like Netflix and Google Play, different mobile and web applications, E-commerce websites like Amazon, and search engines use these recommender systems everywhere.
The recommendations are usually based on the customer behavioral data and various parameters such as browsing history, item details (category, price), previous purchases, contextual data (device, language, location), item views, form fill-ins, page views, and clicks.
The recommender systems enable businesses to boost profits, enhance customer engagement, deliver relevant content, drive more traffic, and reduce churn rate. It is the perfect way for online retailers to enjoy numerous upselling opportunities and offer extra value using ML.
The demand forecasting concept is used in various industries, from manufacturing and transportation to e-commerce and retail. Demand forecasting feeds historical information to Machine Learning models and algorithms and predicts the power, services, number of products, and more.
It enables businesses to efficiently process and collect data from the complete supply chain, enhancing efficiency and reducing overheads. Machine Learning empowered forecasting is transparent, rapid, and very accurate. Companies can generate valuable insights from a constant demand/supply data stream and adapt according to changes.
Image & Video Recognition
Deep learning is a subset of ML, and the advances in this technology have accelerated rapid growth in image and video recognition techniques over the past few years. It is used in various areas, including visual search, image composition, object detection, landmark and logo detection, text detection, and face recognition.
Machines are good at processing images, and Machine Learning algorithms train deep learning frameworks to classify and identify images in the dataset with much more accuracy as compared to humans.
Companies such as Facebook, eBay, Amazon, Shutterstock, and Salesforce use ML for video recognition, where videos are classified as individual digital images by breaking them down frame by frame.
Virtual Personal Assistant
From Google Assistant and Alexa to Siri and Cortana, we have multiple virtual assistants that help us find accurate data using our voice instructions, such as scheduling an appointment, opening an email, calling someone, and more.
These virtual assistants use ML algorithms to record our voice instructions, send them over the server to a cloud, and then decode them using Machine Learning algorithms and act accordingly.
With the advancements in ML, the applications and use cases of Machine Learning solutions are also expanding. To be successful in the digital era, businesses need to keep an eye on how the technology can be deployed across business domains to deliver better user experiences, improve efficiency, and reduce costs.
However, to implement Machine Learning accurately in your company, it is critical to have a trustworthy partner with deep domain expertise, such as Xavor Corporation. Xavor offers advanced ML services that involve identifying the existing gaps, offering effective technology solutions to manage varied business issues, and understating the complexity of these challenges.