By Ankit Patel
What is Machine Learning?
Artificial Intelligence (AL) and Machine Learning (ML) are ringing the bells in the digital market and are benefiting businesses to a greater extent. Machine Learning has emerged from Artificial Intelligence and is considered the necessity of the current period. Amazon has already recognized its benefits. Due to ML technology, Amazon is able to recommend products to their customers on the basis of their purchase history or shopping behaviours. This factor needs to be learnt by the start-ups and must employ ML strategies to scale their business. The input is customer behaviour, and the resultant output is customized options for boosting sales.
ML-based apps amazingly learn details iteratively such as anniversaries, birthdays, important festivals, and other information provided by the consumer willingly to a company. Driven and powered by the latest computer technologies, machine learning is evolving rapidly.
Machine learning helps a start-up in enhancing its scalability and business operations all over the globe. In the community of business analysts, numerous Machine Learning algorithms and Artificial Intelligence have gained remarkable popularity. Factors behind the massive boom of Machine Learning are easy data availability, growing volumes, faster and cheaper computational processing, and reasonable data storage. Thus, start-ups can now benefit by implementing ML.
Business Benefits Derived from Machine Learning
ML helps in extorting meaningful data from innumerable raw data. If ML is implemented properly, it can smartly predict altering customer behaviours and act as a problem solver for a variety of complex business problems. Big technology giants like Microsoft, Google, Amazon, etc., have landed with their Cloud ML platforms. Below are listed the reasons why a start-up should quickly adopt ML and how it can help start-ups scale up are:
Instantaneous Decision Making
Every start-up relies on precise information so as to make timely right decisions. Without the help of intelligent technology like ML, it is next to impossible to extract the right data from such huge data. ML permits start-ups to transform large sets of data into actionable and knowledge intelligence. Hence, to respond to this continuously changing business circumstances and market demands, this info can be used to carry out everyday business operational activities. Consequently, the start-ups can make instant business decisions and stay at the edge of their competition.
ML can also be used in making financial analysis as it can collect accurate and quantitative historical data. The finance department of a company uses ML algorithms for loan underwriting, managing portfolio, algorithmic trading and scam detection. Certainly, future apps of Machine Learning in finance are going to include Chatbots together with other chatty interfaces for customer service, sentiment analysis and security.
Corrective and preventive maintenance practices are regularly followed by manufacturing firms, which are very often inefficient and expensive. With the introduction of Machine Learning, manufacturing firms can use ML for discovering hidden patterns and meaningful insights from their data. This we call as predictive maintenance. It helps in minimizing the risks linked with unexpected failures. Also, it eliminates avoidable expenses.
Enhancing Security & Network Performance
Network intrusions, anomalies and cyber-security threats often crop up with little or no warning beforehand. Start-ups need to maintain their network security. For this they must implement some technical tool that would proactively identify any gratuitous networking behaviour and impede the intrusion at an early stage from performing the tasks like service outages, security attack and data leak. With ML algorithms, network behaviour can be monitored for anomalies immediately and proactive measures can be executed automatically. As ML algorithms self-train, cyber-security state improves continuously, thereby adapting to changes. Here, Ml lets security providers create newer technologies that can detect unknown threats effectively and quickly.
Eliminates Manual Tasks
Inaccurate and duplicate data entry can become the biggest problem for start-ups. There are many start-ups that have come across this problem. Predictive Machine Learning algorithms can, to a greater extent, avoid any inaccuracies or errors caused because of data entries done manually. Machine Learning discovers duplicate or inaccurate data effectively. Thus, instead of digging the data to figure out the errors, the businessman and his staff can utilize that time to carry out fruitful tasks that would add value to his business.
For the past few years, Machine Learning has been used by start-ups for detecting spam. Earlier, spam was used to be filtered out by email service providers using the pre-existing, rule-based practices. However, new rules are being created by spam filters using neural networks to detect phishing messages and spam.
Customer Behaviour Prediction
Customer segmentation and customer behaviour prediction are the two biggest challenges among other challenges that the marketers of the current period face. Companies have the right to access a lot of data, which they can use effectively to derive significant business insights. Data mining and Machine Learning can help start-ups predict complex customer behaviours, their purchasing patterns. On the basis of their browsing as well as purchase histories, it helps the start-ups in sending the best possible deals to the individual customers.
Almost 80% of e-commerce websites are using ML to make product recommendations. The ML algorithms use the shopping history of the consumer and then match it with the vast product inventory for identifying hidden patterns as well as for grouping similar products together. Then the customers have suggested these products and motivated to purchase the product. Predicting more accurately “the related products” will help a start-up increase its revenue per consumer.
In Medical diagnosis, ML has helped a number of healthcare sectors to improve the health of the patient and reduce the costs incurred for healthcare, using helpful treatment plans and advanced diagnostic tools. Most of the healthcare sectors are making use of it to predict readmissions, for perfect diagnosis, to identify patients at high-risk and to recommend medicines. These insights and predictions are figured out using a patient’s data sets and records together with the symptoms the patient exhibits.
Measuring Effectively the Risk Factors
Ascertaining risk factors is not as easy as running a knife on the bar of melted butter. Countless variables have to be considered and complex decisions have to be made by the managers. With the help of Machine Learning frauds, errors and other risk factors can be understood and measured effectively.
As with Artificial Intelligence and Python, in the current period, Machine Learning is equally being adopted and used for boosting sales. ML is helping start-ups plan ahead of time efficiently and boost their sales.
About the Author
Ankit Patel is a Marketing/Project Manager at XongoLab Technologies LLP and PeppyOcean, which are offering top-notch mobile app development solutions globally. During free time, he loves to write about new & upcoming technologies, web & mobile, programming, and marketing. His write-ups have been published on popular platforms like TechTarget, SmallBizDaily, JaxEnter, Social-Hire, TorqueMag, and many more.