With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business. A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business. ML is a data analysis process that leverages ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for.

Let us look at some of the most significant ML and artificial business benefits, starting with the sales and marketing sector.

• Simplifies Product Marketing and Assists in Accurate Sales Forecasts

ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to the sales and marketing sector, with the major ones being ?

? Massive Data Consumption from Unlimited Sources

? Rapid Analysis Prediction and Processing

? Interpret Past Customer Behaviors

• Facilitates Accurate Medical Predictions and Diagnoses

In the healthcare industry, ML helps in easy identification of high-risk patients, makes near-perfect diagnoses, recommends the best possible medicines, and predicts readmissions. These are predominantly based on the available datasets of anonymous patient records as well as the symptoms exhibited by them. Near accurate diagnoses and better medicine recommendations will facilitate faster patient recovery without the need for extraneous medications. In this way, ML makes it possible to improve patient health at minimal costs in the medical sector.

• Simplifies Time-Intensive Documentation in Data Entry

Data duplication and inaccuracy are the major issues confronted by organizations wanting to automate their data entry process. Well, this situation can be significantly improved by predictive modeling and machine learning algorithms. With this, machines can perform time-intensive data entry tasks, leaving your skilled resources free to focus on other value-adding duties.

• Improves Precision of Financial Rules and Models

ML also has a significant impact on the finance sector. Some of the common machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection. In addition, according to a report on ?The Future of Underwriting? published by Ernst and Young, ML facilitates continual data assessments for detecting and analyzing anomalies and nuances. This helps in improving the precision of financial models and rules.

• Easy Spam Detection

Spam detection was one of the earliest problems solved by ML. A few years ago email providers made use of rule-based techniques to filter out spam. However, with the advent of ML, spam filters are making new rules using brain-like neural networks to eliminate spam emails. The neural networks recognize phishing messages and junk mail by evaluating the rules across a huge network of computers.

• Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry

Manufacturing firms have corrective as well as preventive maintenance practices in place. However, these are often costly and inefficient. This is exactly where ML can be of great help. ML helps in the creation of highly efficient predictive maintenance plans. Following such predictive maintenance plans will minimize the chances of unexpected failures, thereby reducing unnecessary preventive maintenance activities.

• Better Customer Segmentation and Accurate Lifetime Value Prediction

Customer segmentation and lifetime value prediction are the major challenges faced by marketers today. Sales and marketing units will have enormous amounts of relevant data sourced from various channels, such as lead data, website visitors and email campaigns. However, accurate predictions for incentives and individual marketing offers can be easily achieved with ML.

• Recommending the Right Product

Product recommendation is an important aspect of any sales and marketing strategy including upselling and cross-selling. ML models will analyze the purchase history of a customer and based on that they identify those products from your product inventory in which a customer is interested.