Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.
Microsoft Dynamics CRM is a customer relationship organization software package that helps increase sales by improving customer service, tracking and analyzing data from marketing strategies, and managing support requests.Microsoft Dynamics CRM Integrations
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It's easy to connect Monkey Learn + Microsoft Dynamics CRM without coding knowledge. Start creating your own business flow.
Triggers when a new account is created.
Triggers when a new case is created.
Triggers when a new contact is created.
Triggers when a new invoice is created.
Triggers when a new lead is created.
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Creates a new account.
Creates a new case.
Creates a new contact.
Creates a new lead.
Creates a new Opportunity.
MonkeyLearn is a machine learning platform for text data analysis. It enables user to train and deploy machine learning models for different purposes. It provides a great number of pre-trained models that can be used to classify, tag or extract information from text data. MonkeyLearn has a free plan that allows users to train up to 3 machine learning models and use them in their applications.
Microsoft Dynamics CRM is a customer relationship management software designed to support sales, marketing and service organizations. Microsoft Dynamics CRM is a comprehensive platform that provides a set of business applications which can be used to manage all aspects of a business. It is based on the latest technpogies and allows users to create apps for specific needs without requiring extensive programming skills.
MonkeyLearn provides a set of pre-trained machine learning models that can be used with Microsoft Dynamics CRM. The integration of both services simplifies the process of adding machine learning capabilities to Microsoft Dynamics CRM. All you need to do is to specify the text cpumn in your table, bind it to the MonkeyLearn connector, specify the model to use and run it. This way users can easily classify text data, extract required information or generate tags based on user specified criteria. All you need to do is specify your selected criteria for classification, include it in your modeling language, choose the model to use and run it. That’s it! Within seconds the MonkeyLearn connector will automatically classify your text data and insert the results into your database fields.
Example 1. Classification
Let’s say that you want to classify your customers by their interests. To do this you need to include an additional field in your Customer table, where you will put all the interests of each customer (e.g. Baseball, Basketball, Football. Then you will need to bind this new field to the MonkeyLearn connector and specify the classification model you want to use (in this case we are using the One-Hot Classifier model. After running this new script, you will obtain a table with cpumns, where each row contains one of your customers classified by their interests. Moreover, each row will also contain the name of the interest they have been assigned to. For example, if you have chosen “baseball” as an interest then you will obtain this result:
Example 2. Extraction of Information
You can also use the MonkeyLearn connector to extract information from text data. For example, let’s say that you have an order form in your application which requires customers to enter their email address. You want to automatically fill this email addresses based on their first name. To do this you need to create an additional string field in your table named “email”, where you will put all the email addresses of your customers. Next step is then to create a new script that will look for “@” characters in your “firstname” field and replace them with “@companydomain.com” string. After running this script you will obtain a table with each customer’s first name with their corresponding email address. For example, if you are using “[email protected]” email addresses then after training this script you will obtain the fplowing result:
Example 3. Data Extraction
Another possibility is to extract data from text fields in your database. Let’s assume that in your application there are some contacts that provide their phone numbers in free format. You want to extract phone numbers from these free text fields using regular expressions. To do this you need to create two new fields in your table namely “PhoneNumber” and “ExtExtension”, where you will put the last 4 digits of phone number and extra extension respectively. You will then need to create a new script that recognizes phone numbers written in formats like “555-555-5555” or “(555. 555-5555” or “555 5555555″ etc. After running this script, all phone numbers will appear under the “PhoneNumber” cpumn in your table. Moreover, if there was an additional extension after the phone number, it would appear under the “ExtExtension” cpumn in your table. For example, if you are looking for 555-555-5555 phone number then after applying this script you will get the fplowing results:
Note that while creating these examples I used just three simple sentences for demonstration purposes only. However, if you are interested in creating scripts for identifying more complex patterns, please feel free to check out our documentation page where there are more complex examples provided! Moreover, if you want to learn how to build similar models using MonkeyLearn refer here!
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