Question $1$:

Machine learning system:

Firms use machine learning technologies for a variety of purposes across several industries. They are employed in their respective sectors in order to promote innovation, obtain new insights, make better decisions, and improve client experiences. Here are some typical forms of machine learning systems used by companies adopt.

They are,

User Acquisition:

A basic customer sales pipeline consists of three stages: segmenting the client base to identify and meet their requirements, connecting them with suitable material at the right time, and turning them into product users. Many businesses use machine learning techniques to dynamically modify branding, content, and promotional pricing in order to optimize the chances of a sale for individual customers. Sales force has introduced Einstein, an enterprise tool, which analyzes CRM data and offers customized suggestions to boost a prospect's likelihood of converting from a sales presentation. The program may even propose the optimal time to send an email.

Customer Support:

A specific system that assesses the sentiment of customer support requests and elevates critical replies to the head of the help queue is developed in supermarkets using Google machine learning APIs. As a result, significant queries are responded to four times faster. Conversational bots are now leveraging machine$-$powered natural language to provide a first response capable of completing common tasks like providing return labels, all while triaging support queries without the assistance of a human operator. In addition to saving support expenses, chat bots may increase customer happiness by replying more quickly, and as their comprehension abilities advance, so will the range of their capabilities.

Security and Fraud Detection:

Machine learning is a powerful tool to detect fraud because of its ability to identify trends and find anomalies that leave from these patterns. It analyzes the typical behavior of each individual the client, including the location and timing of purchases made with credit cards. Subsequently, this data together with other kinds of data are used to precisely decide in seconds when transactions fall within the norm and are hence authorized, as well as to identify those that differ from expected norms and may therefore be fraud. The potential of machine learning as a powerful instrument to intelligently monitor millions of transactions in real$-$time and cut down on fraud waste is beginning to materialize. PayPal is a the pioneer in this field; to reduce the amount of false alarms generated by their outdated fraud models, they have built an artificial intelligence engine from scratch using open$-$source technologies and their massive repository of transaction data.

People Management:

Machine learning may be able to find diverse, high performing applicants which human recruiters would miss the first time around. In terms of employee retention, machine learning may supplement the guidance of excellent managers and improve worker performance by producing objective, targeted career recommendations based on the profiles of previous workers. To fully use the potential of machine learning teams and ensure the success of ML projects, good people management is essential. To foster an atmosphere that supports creativity and excellence in machine learning activities, one needs a blend of technical competence, leadership, communication, and teamwork.

$[$Tech $(2021)]$

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