- Introduction
- Overview
- How businesses can use Communications Mining™
- Getting started using Communications Mining™
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label low average precision
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining™
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Communications Mining user guide
How businesses can use Communications Mining™
This page covers an overview of the following topics:
- Optimal data types for Communications Mining.
- Key value pillars for Communications Mining, and how they link to use cases.
- Typical use cases across analytics and automation.
- Examples across industries where Communications Mining can be deployed.
- Customer examples of where Communications Mining is deployed.
- Which UiPath® tools you can combine with Communications Mining, including RPA and Document Understanding™.
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| Communications Mining is optimized for short-form asynchronous communications data, such as emails (e.g., shared email inboxes), tickets, survey responses, and case notes. |
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| Communications Mining does not currently support real-time call and chat data. For historical analytics on chat and calls data, the platform can support these if volumes are large enough. |
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| Communications Mining does not natively process attachments, which are documents, but can be combined with Document Understanding™ to process both emails and attachments. |
Communications Mining can drive value for businesses in a huge number of ways. Ultimately, the business objectives will determine the value that a use case owner is looking for, and value pillars will align with specific use cases.
As described so far, Communications Mining opens up significant opportunities for both analytics and automation for customers.
Usually, customers ask where Communications Mining can be deployed, and the answer is anywhere.
In every industry, each process and action on screen, from customer support to the ordering of parts in manufacturing, to insurance quotes, claims, and renewals, starts with some form of communication.
As businesses grow, they need solutions, like Communications Mining, to help them effectively manage these communications. Otherwise, they risk falling behind.
As covered in the previous section, Communications Mining acts as an enabler for intelligent automation by providing structured data to downstream automations to take action.
For more details on how Communications Mining combines with RPA for automation, check the API Docs Introduction.
Communications Mining and Document Understanding may handle different kinds of data, but they can ultimately come together to form a powerful combined solution.
Every business in the world processes documents that are exchanged through communications:
- Communications Mining and Document Understanding enable businesses to understand and automate complex service processes E2E - tasks where employees previously needed to read both messages and documents to complete their work.
- They create a whole new source of data for UiPath robots. For the first time, businesses can automate some of their most time-consuming and intensive service processes.
How Communications Mining and Document Understanding work together
The following image contains a graphical workflow of how Communications Mining and Document Understanding work together:
