- 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
- 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
Communications Mining™ overview
Natural Language Processing (NLP) is a field of Machine Learning focused on building tools that can automatically understand and process natural language data similarly to the way humans can.
A key focus of NLP tools is taking unstructured communications data and turning it into actionable structured data, by understanding the intents, themes and concepts within, and extracting key data points.
From support, to sales, to finance and services. Communicating with each other is how business gets done.
The number of emails, tickets, and messages is rising every year, and this is bringing services to a breaking point. It's increasing the cost to serve, and damaging employee and customer experiences.
Thankfully, Natural Language Processing, a branch of AI that focuses on helping machines read and understand human language, has seen massive advances in recent years.
Natural Language Processing, or NLP, has come of age, and it’s now beating humans in language understanding and reading comprehension.
This generates new solutions and opportunities for the enterprise.
With NLP, it's now possible to understand communications at scale.
This enables businesses to:
Communications Mining™, or Comms Mining, is a field that focuses on understanding and extracting value from communications data.
It’s the practice of converting the unstructured information this data contains into structured, machine-readable data that can then be used for analytics and automation.
Importantly, UiPath® Communications Mining™ not only identifies the pain points, but can also help resolve many of them through enabling more intelligent automations.
UiPath® Communications Mining™, formerly Re:infer, automates the interpretation of communications, helping businesses understand and automate messages – at speed, at scale, on any channel.
Our solution combines machine learning (ML), natural language processing (NLP), and employee-led supervised learning in a powerful, no-code solution that anyone can use.
It provides complete visibility into channels like emails, tickets and customer feedback, helping businesses better understand their customers, and where improvements will have the biggest impact.
It also enables intelligent automation from communications, as it generates the structured data required by downstream automations to action requests without human intervention.
Here's an overview of the typical journey that your data goes on within the platform:
- Pre-built connectors for ingestion into historic comms store. Proprietary ML segmentation and cleaning engine to clean data.
- Proprietary Deep Learning Sentence models extract semantics for data efficient learning.
- Proprietary Unsupervised Learning models identify common intents and constantly search for new ones.
- Train bespoke supervised models efficiently in our Proprietary Active Learning engine and interface.
- Real time aggregate statistics for meaning-based Management Information and Analytics.
- Real-time model validation and model lifecycle management.
Connect
First, we connect to your channels of unstructured communications data.
These could be shared email inboxes, workflow tickets, or collections of survey responses, to list just a few examples.
Getting this data into the platform can be done via:
- Live integration with pre-built connectors, for channels such as Microsoft Exchange or Salesforce
- Building API integrations
- Uploading historic data, either via CSV or our API
Discover
With data uploaded, the platform automatically kickstarts the discovery process.
It uses unsupervised learning to cluster together groups of communications that share similar themes and concepts.
These clusters can link to repetitive processes, requests, issues, and sentiments. They can both help us to define the right structure for our model and speed up the first stage of model training.
By reviewing these clusters and applying labels and fields that capture relevant concepts and data points, the platform quickly starts to build a picture of what’s in the data.
Train
Next, we use a variety of training modes to build out the training data for our model.
Here, we’re teaching the platform to confidently identify these labels and fields, across the breadth of our data.
These training modes are designed to maximise the impact of training actions, and minimise the time spent training. Meanwhile, the platform’s zero-code interface means that a Model Trainer can be any business user working in the communication channel. No data scientists or engineers required.
With every training action, the platform continuously retrains, improving its understanding of each concept and data point and updating its predictions in real-time.
By annotating a small, representative sample of training data, the platform is able to apply its understanding of each label and general field at scale, automatically interpreting and making predictions across the entire dataset.
Predict
The end result? Structured label and general fields predictions, each with their own confidence scores, for every communication, just like this example email here:
These predictions are made available for analytics in the platform or via the API for consumption by UiPath® bots and other tools for automation or further analysis.
Validate
But before we rely on these predictions to influence decisions or enable action, we need to know how our model is performing.
The platform’s validation functionality provides full transparency when it comes to performance, validating your model automatically each time it retrains.
We can easily understand if our model is performing as it should across key performance factors that are aggregated into a single model rating for simplicity.
The platform also guides Model Trainers to make improvements as needed with recommended next-best actions.
Analyze
Once we’re happy with our model’s performance, we can very quickly generate valuable and actionable insights from these business conversations.
The platform aggregates all of the predictions for labels and fields with key metadata to provide a wealth of queryable data, providing visibility into previously hidden processes and channels.
This allows users to:
- Create custom dynamic dashboards to track key metrics and the quality of service delivered to customers and clients
- Run analyses to identify opportunities to improve processes or customer experience
- Or set up alerts to monitor channel performance and risk events
Just to name a few examples.
Alongside analytics, we can deploy trained models to enable production automations.
UiPath® robots and downstream systems can utilise the structured data created by UiPath® Communications Mining™ to extend automation into service and conversation-based processes. This allows businesses to automate transactional requests and workflows.
Tasks like triaging emails, updating customer information, and case creation can now be automated end-to-end by UiPath®.
Leading enterprises trust Communications Mining™ to analyze and automate their communications - and here are some of the reasons why:
- No code - We democratise NLP for the business user. The platform’s zero-code interface provides a guided user experience that any employee can use - regardless of technical ability.
- Fully customisable - You can create fully bespoke models that extract the exact intents, themes and sentiments that your business needs.
- Accurate - You can train accurate models with minimal training data, with full transparency on model performance to avoid unexpected outcomes in production.
- Fast to train - You can keep costs and effort low and confidence high, with models that are quick to train and fast to adapt.
- Hyper scalable - You need to start small to succeed, but you’ll need a solution that can rapidly scale with your needs for bigger use cases, which our platform does.
- Real-time monitoring & alerts - With configurable dashboards, email alerts and reports tracking key metrics in real-time, you’ll have all the data you need to make proactive, informed decisions.
- Secure - Above all, you need a solution that you can trust your data with, and our platform’s permissioning and encryption ensures customer data is secure and protected.
- Easy to integrate - We have pre-built integrations for key communications channels, easy-to-work- with APIs, and connectors for workflow and RPA, helping it easily fit into your technology stack.
Our solution opens up the power of AI and NLP to all business users, not just data scientists and engineers.
- What is Natural Language Processing (NLP)?
- Why is NLP becoming critical to enterprises?
- How can NLP solutions help solve these challenges?
- What is Communications Mining™?
- Communications Mining™ vs. Task Mining & Process Mining
- What is UiPath's® Communications Mining™ solution and what does it do?
- How does this help our customers?
- How it works: Overview
- How it works: Deep Dive
- Why do enterprises trust UiPath® Communications Mining™?
- What does Communications Mining™ mean for you?
- What does Communications Mining™ mean for your business?