- Getting started
- Communications Mining™ overview
- How businesses can use Communications Mining™
- Getting started using Communications Mining™
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have 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
- Licensing information
- FAQs and more
How businesses can use Communications Mining™
This article covers an overview of the following topics:
- Optimal data types for UiPath® 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 can be combined with Communications Mining™, including RPA and Document Understanding.
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| UiPath® 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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| It does not currently support real-time call and chat data. For historical analytics on chat and calls data, these can be supported if volumes are large enough. |
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| Communications Mining™ doesn’t natively process attachments (i.e., documents), but can be combined with UiPath® 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.
See below for more details on the use cases identified above.
As we’ve seen, UiPath® Communications Mining™ opens up significant opportunities for both analytics and automation for our customers.
For analytics, some key groups of use cases include:
For automation, typical use cases are:
Further down in this article, we cover some of the tools that Communications Mining™ combines with to facilitate downstream automation.
So, where can UiPath® Communications Mining™ be deployed?
The answer: 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 ours to help them effectively manage these communications, or risk falling behind.
Here are just a few specific examples of how our customers are using UiPath® Communications Mining™:
Whilst Communications Mining™ can ultimately form part of a solution leveraging many different UiPath® tools, or form part of a discovery exercise also using Process and/or Task Mining, it most obviously combines with RPA and Document Understanding:
As covered in the previous article, Communications Mining™ acts as an enabler for intelligent automation by providing structured data to downstream automations to take action.
This hand off is typically to a UiPath® bot, and the diagram below details how the two can work together at a high level:
How Communications Mining™ combines with UiPath® RPA for automation is covered in detail here.
They 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 which are exchanged via communications:
- Together, Communications Mining™ + 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 will be able to automate some of their most time-consuming and intensive service processes.
How do the two work together?
- Introduction
- Optimal data types for UiPath® Communications Mining™
- Value pillars for Communications Mining™
- Use case: Analytics
- Use case: Automation
- Industry examples
- Example customer use cases
- What UiPath® tools can be combined with Communications Mining™?
- Communications Mining™ + RPA
- Communications Mining™ + Document Understanding
