Process Mining and Communications Mining, explained
Break down the differences between process mining and communications mining, and see which best addresses your business needs.
Re:infer turns conversations into structured data for analysis and automation. We give organisations a centralised NLP capability to drive visibility, efficiency and operational scalability. Making the enterprise more agile and customer centric.
What’s the difference between process mining and communications mining?
Process mining is used to extract insight from structured business processes, while communications mining extracts insight from unstructured business processes and communications.
Automation and transformation leaders use a range of process intelligence tools to identify waste and improvement opportunities in their business. Process mining and communications mining are both crucial for operational visibility, but focus on different channels.
Process mining shines a light on structured business processes, highlighting inefficiency and breakdown.
Communications mining analyses enterprise communications, exposing the causes of process breakdown and enabling new automation opportunities.
Increases process insight and identifies waste and inefficiency.
Drives understanding and automation of unstructured processes and business communications.
Process mining is used by process managers and improvement teams, data scientists, auditors and consultants.
Communications mining is no code by nature and is used by every employee, from automation leaders to non-technical service agents.
Insight is extracted from event logs created in corporate IT systems.
AI transforms unstructured communications into structured data en masse for analysis.
Analysis is constant and real-time, enabling continuous improvement.
Analysis is real-time, with automated alerts driving continuous improvement.
Process mining targets structured processes that interact with corporate IT systems.
Communications mining is fully omnichannel, extracting value from emails, calls, chats and any form of enterprise communication.
- Increased cost savings
- Improved operational efficiency
- Better business intelligence
- Data-driven decisions
- Meet SLAs
- More value from automation
- Increased cost savings
- Improved productivity
- Faster, easier operational scalability
- Improved customer intelligence and experiences
- Improved employee satisfaction
- Data-driven decisions
- Better compliance
Complete conversational intelligence
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Business runs on conversations. Customers and employees communicate across a wide range of different channels. The business will store these conversations, but they are almost always in an unstructured format that makes them difficult to analyse and exploit.
The value of communications
Conversations hold precious business and customer insights. They can reference customer issues, operational blockages, new revenue and cost-saving opportunities that business leaders mightn’t otherwise notice. Unfortunately, traditional analytics tools lack the ability to understand and extract these insights.
That’s why communications mining uses the latest advances in AI and natural language processing (NLP) to understand and analyse communications at scale. Users rapidly train the no-code Re:infer platform to extract relevant and useful concepts from their business conversations. NLP interprets and structures comms in real-time, turning it into clean and structured data that’s ready for analysis.
Re:infer’s built-in analytics interface allows users to analyse and explore all communications channels in real-time. At a glance, they can recognise the most pressing issues, identify opportunities for change, automation and improvement. They can also set up real-time alerts to preempt emerging issues and respond proactively to customer concerns.
How do Communications Mining and Process Mining work together?
Communications mining and process mining are distinct but complementary tools.
Process mining describes the ‘how’ of processes. They show you where inefficiency exists and where it can be found in the organisation. Communications mining, by contrast, explains the ‘why’. It surfaces unknown issues, reveals the drivers of work and tells users what changes would have the biggest improvement.
For example, an employee might leverage process mining to analyse and measure the benefits of a recent change programme. If the analysis shows little improvement, they would then use communications mining to explore the reasons why, before making the necessary changes.
A virtuous circle of analysis and automation is created, with both solutions working together to increase visibility, improve operational efficiency and the customer experience.