Читать книгу Machine Learning with Dynamics 365 and Power Platform - Vinnie Bansal - Страница 25

Successful businesses understand that the customer's feedback is a crucial point for the growth of the business. Every business website has a feedback form where you can ask your queries or write your product experience. But Dynamics 365 Customer Voice is providing something more. It provides your organization with some additional capabilities, such as the ability to create a survey and share it with your customers for valuable feedback. Once the responses are received, Power BI analyzes feedback about your products and services to evaluate the information and take actions to transform products and employee experiences.

Unlike the past, people are now connected through social media and communication solutions. Therefore, businesses are beginning to realize the potential of data‐driven decisions, large‐scale coordination, and the efficiency of online tools. Thus, Power Platform tools play an important role in improving productivity and automating business processes. To unlock the true potential of Dynamics 365 and Power Platform tools, this book will show you how you can do wonders in your business by using machine learning with these two technologies. It is an important topic because it will help you to make proactive decisions for your business by using predictive data analytics. It also allows businesses to utilize future predictions to perform operations more efficiently. Now let's look at how adding a layer of intelligence to Dynamics 365 and Power platform using machine learning would benefit the businesses.

Whether it's sending follow‐up emails, similar record creation, or scheduling and managing appointments, isn't it frustrating to do the same task at your workplace every day?

Just imagine that your employee takes 5 minutes for a repetitive task of sending follow‐up emails to all the possible prospects every day. This means he spends 125 minutes, or 2 hours in a month, considering a month has, on average, 25 working days. In a year, that employee will spend 1,500 minutes or 25 hours. If your organization has 100 such employees, imagine how many productive hours your business will lose. Think about how much your business would have achieved if these employees had worked on tasks that demanded their skills. What if you could reduce these numbers by using machine learning in Dynamics 365?

Dynamics 365 has a feature of email engagement in a sales insight suite that sends you an alert when recipients open your email, click a link in your email, open an attachment sent in email, or give a reply. The activities it doesn't do on its own that require manual work are:

 It doesn't send a follow‐up email on its own.

 It doesn't automatically set a time to send a follow‐up email.

Sure, Dynamics 365 does give you the facility to create an appropriate email template based on your organization's previous email interaction history. But choosing the right follow‐up template and setting time for a follow‐up email is still manual work. These two problems can be solved by using machine learning in Dynamics 365.

Machine learning has an algorithm called a predictive time algorithm that will help you to set optimal open times for every recipient. For this, a machine should have the recipient's past data, including time engaged with opens and clicks on the links sent in previous emails. This way, a machine will learn what's the best time to send a follow‐up email and will set an alert in Dynamics 365.

The second problem can be resolved by a sentiment analysis of email responses by using machine learning (ML) techniques with natural language processing (NLP). ML, if provided with large datasets, can effectively classify the sentiments of email within minutes using precise algorithms. This will help the machine to automatically send appropriate follow‐up emails to recipients on the optimal time already set by the machine.

How amazing it would be if all these repetitive tasks are performed by a machine so that you can channel your energy and time on a more complex and productive task.