It is the new phase in technology that employs Artificial Intelligence (AI) to replicate human-like characteristics of learning and improving with experiences where it follows an iterative aspect in which, as models are exposed to new data, they are able to independently adapt and compute with the learned logics.

Simplifying Product Marketing process and facilitation of Accurate Sales Forecasts

Today ML has become an integral part of strategizing marketing product models as it helps businesses in multiple ways to promote their products better and make accurate sales forecasts. According to Capgemini, 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10%.

Some of the methods which make use of ML in Marketing are –

• Lead scoring
• Customer Churn or Customer Turnover
• Dynamic Pricing
• Sentiment analysis
• Chatbots

Talking about sales forecast, apart from collecting a vast amount of data ML along with AI helps a business to adapt to real-time change. In other words, one will get forecast that will always be in accordance with the market scenario at a specific time.

Providing Precise Medical Predictions and Diagnoses

ML has opened up new doors of possibilities for healthcare sector. With technological advancement intact, it has now become easier and much simpler to identify patients with high risk factor. ML enables doctors to deduce detailed and precise diagnoses of the ailment, thus assisting them in deciding the right kind of treatment at the earliest along with recommending best possible medicines for the same.

The Rise of ML in Financial Sector

The significance of ML has witnessed an exponential rise in financial sector in recent times.

This is due its effectiveness in various vicinities of this domain, viz:

• Portfolio Management
• Algorithm Trading
• Loan Underwriting
• Fraud Detection

In addition ML also helps in:

• Reducing operational cost through process automation.
• Increasing revenues as a result of better productivity and enhanced user experiences.
• Better compliance and improved security

Customer Segmentation and Lifetime Value Prediction with ML

Customer segmentation, which is grouping of customers upon some common characteristics, is done to identify the target customer base for a company to market their products effectively.

Based on these characteristics, customers are mainly segmented into four types:

• Demographic segmentation.
• Behavioral segmentation.
• Psychographic segmentation.
• Geographic segmentation.

In machine learning, such segmentation takes place using techniques like K-means, which is a popular clustering algorithm used for unsupervised machine learning process. It helps in classifying similar data points (e.g. customers) into a number of groups that are already predefined.

As mentioned earlier, ML allows the system to ‘learn’ from previous experiences or data obtained by it. In this case the machine will learn from each customer interaction, a whether or not a customer is being provided with the right kind of preferences according to his or her respective taste.

Based on these inputs, the system can build up statistical models automatically, which will enable further prediction of engagement models that will most likely tend to suit each customer individually.

Machine Learning calculates CLV using various methodologies, such as:

• Classification and Regression Trees (CART),
• Support Vector Machines (SVM)
• Additive Regression
• K-Star Method
• Multilayer Perception (MLP)

Spam Detection made easy with ML

According to an estimate, around 60% of the global email traffic is made up by unsolicited bulk mails or Spam mails. However with the advent ML, the detection of such fraudulent mail has become quite easier to some extent by applying various spam filters and making new rules using neural networks to eliminate spam mails. The neural networks recognize phishing messages and junk mail by evaluating the rules.