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Conversational Analytics

Discover how Conversational Analytics is driving process insight, operational efficiency, and transforming service and the customer experience.

Conversational Analytics

What is Conversational Analytics?

Conversational analytics is the examination and interpretation of conversational data in order to extract useful insights from it. Conversational analytics is used by businesses to understand the motivations and desires of their customers, and to get a better idea of how their organisation operates.

Conversational analytics is one of the main applications of Conversational Data Intelligence, known as Communications Mining. It is a vital method of extracting management information, operational and customer intelligence from business conversations and communications-based processes.

Why is Conversational Analytics important?

Conversational analytics is crucial to business intelligence and customer intelligence.

Business runs on conversations. At some point, conversation mediates almost every single process in an enterprise as customers communicate and colleagues collaborate to get work done. An ever-increasing amount of this business conversation takes place on digital channels, including email, chat and calls.

Conversations such as these contain valuable customer and business intelligence - insight into intents, the drivers of workflows, the reasons for operational efficiency and failure. However, extracting this information hasn’t been straightforward. Business conversations are based in unstructured data that can’t easily be read by machines. As a result, companies have traditionally relied on their human employees to manually read, sort, action and extract the important information from inbound communications.

However, this manual process of extraction is no longer sustainable. The scale of digital communications is growing rapidly. Almost 320 billion emails are sent every day, and this is expected to grow to over 376 billion by 2025. The average employee already processes 126 emails a day, and up to 40% of that time is spent solely in Outlook. With so many messages to process, it’s impossible for any business to achieve sufficient speed and coverage of analysis with human employees alone. What’s more, they can hardly rely on the accuracy of insights extracted by employees while short on time and under pressure.

Conversational analytics represents a break in this largely manual model. Leveraging the latest advances in AI and natural language processing (NLP), conversational analytics is able to automate the interpretation of unstructured communications data, turning it into structured data for detailed and rapid analysis. The most sophisticated solutions, including the Communications Mining platform of Re:infer, a UiPath company, are able to extract information like sentiment, emotion and even intent from conversations, and can analyse messages from any channel - from email to calls and chats.

What are the benefits of Conversational Analytics?

Conversational analytics has many important benefits, for service and customer experience as well as operational performance. Insights extracted from conversational analytics can be used to drive key improvements, including:

Better decisions

Increase insight into unstructured channels and processes to highlight issues, inefficiencies and change opportunities.

Improved CX and customer retention

The factors affecting customer loyalty and retention become clearer when you can process what people are saying at speed and scale.

Happier employees and reduced churn

Conversational analytics limits the need for employees to manually sift through emails and other messages, freeing them up to focus on more rewarding and challenging work.

Better efficiency and cost savings

Conversational analytics accelerates wasteful processes related to customer service, product intelligence and service monitoring.

Enhanced productivity

Teams waste less time on investigating issues and validating opportunities, enabling them to focus more on creating value for the business.

Superior compliance

Conversational analytics gives compliance teams real-time insight into all communication channels. Compliance risks can be surfaced and resolved faster.

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Where can Conversational Analytics be used?

Conversational analytics can be leveraged anywhere that conversations play a role in business processes. This provides conversational analytics with a very wide application, but it is perhaps most relevant in operations and service functions - including customer services and business services. The most sophisticated conversational analytics solutions can analyse messages in a variety of diverse formats, including tickets, chats, CRM and ERP system notes. All that conversational analytics needs to generate value is a large quantity of communications.

Conversational analytics has already experienced widespread adoption in industries that rely on high-volume, complex communications between two or more parties. It is prevalent in customer service, banking, asset management, insurance, ecommerce, telecommunications, consumer and business travel, and in the front offices of large, consumer-facing enterprises.

NLP and Conversational Analytics

Originally, conversational analytics was limited to only a small number of surveillance and sentiment analysis applications. These relied on broad-brush, keyword matching to identify specific words and phrases that were then sent to human agents for review. This rudimentary approach resulted in many false positives that created more work for agents, and the solutions struggled to achieve coverage across all corporate communications channels.

The most important recent technical innovation in conversational analytics has been the utilisation of natural language processing (NLP). A branch of AI, NLP is the application of machine learning to create value from human natural language. It describes a large number of different solutions, but most commonly NLP helps train machines to understand and correctly process text or speech.

In the last few years NLP technology has been revolutionised by large-scale language models powered by Transformers. These massive, complex models contain billions of parameters and training data points, creating NLP models that are more accurate and reliable. For the first time, NLP models can be depended on for real-world, mission-critical use cases and applications. In fact, new NLP models routinely outperform human workers in language understanding and reading comprehension.

The adoption of NLP in conversational analytics has greatly expanded the variety and effectiveness of conversational analytics solutions. It’s now possible for conversational analytics to recognise and understand more complex conversational insights, including intent data that shows the motivations of people having a conversation. This has made the technology much more useful for businesses seeking improvement opportunities in their services, operations and customer experience.

What is the best Conversational Analytics software?

There is no all-purpose conversational analytics tool. Depending on the application, a business may need several to complete its objectives or achieve satisfactory coverage.

However, the most popular solutions include:

Communications Mining

Communications Mining, or Comms Mining, is a type of conversational analytics that focuses on understanding and extracting value from communications processes, at speed and scale. It’s the practice of converting the unstructured information these processes are built on into structured, machine-readable data that can then be used for analysis and automation.With these insights, users gain a deeper understanding of their business and customers, and the ability to direct process improvements to where they will have the biggest impact.

What distinguishes Communications Mining from other forms of conversational analytics is that it’s omnichannel, able to analyse conversations expressed in text and speech. No message format that customers or colleagues use to communicate is off-limits, from calls to emails, tickets, chats, instant messages and notes in CRM and ERP systems.

Learn more about Communications Mining.

Sentiment Analysis

Sentiment analysis can be used on both speech and text, but is most commonly used to analyse social media posts. It uses a simple form of NLP to recognise certain trained keywords and patterns in text, providing insight into customer sentiment and emotion. For example, the use of several consecutive negative keywords may signify that the sender is angry or disappointed at the service they have received.

Text analytics

Text analytics is only suitable for use in written language such as emails or chat support messages. One of the oldest forms of conversational analytics, text analytics uses NLP to convert unstructured text into structured data which can then be properly analysed for patterns and insights - normally in-platform through a visual analytical dashboard.

Speech analytics

Speech analytics is a specialised form of conversational analytics that can only be used on speech data. Most commonly, speech analytics uses transcription techniques to turn spoken words into text, before NLP is applied to convert the text into structured data for analysis.

Voice analytics

Similar to speech analytics, voice analytics can only be used on recorded or real-time uttered speech. However, rather than trying to understand what was said, voice analytics is concerned with how. To illustrate, voice analytics could pick up on the fact that a customer is agitated through changes in voice modulation, diction and pace. It is most commonly found in customer service applications as an aide to agents.

Is Conversational Analytics the same as Conversation Analytics?

While similar, conversation analytics can be described as a specific and limited application of conversational analytics to a particular type of conversation. Conversation analytics tools are most commonly used by enterprise sales teams to review and analyse the spoken interactions between sales agents and potential customers.

Conversation analytics save sales leaders valuable time, reviewing sales calls en-masse and highlighting important areas for manual review. Conversation analytics has been a powerful productivity tool, giving sales leaders time back to focus on coaching, but it shouldn’t be confused with the much broader category of conversational analytics.

Is Conversational Analytics the same as Conversational Chatbots?

Conversational chatbots make use of various conversational analytics tools, but they aren’t the same technology.

Conversational chatbots are most commonly used as a form of contact deflection, automating the most simple customer requests and helping triage more complex requests to the right team. They can engage customers both on a web portal through text and over the phone with automated speech. The most advanced conversational chatbots use a range of conversational analytics technologies, including speech analytics and voice analytics, to engage customers in human-like conversation. However, they remain a tool for automation and workflow rather than one that extracts important data for detailed analysis.

Conversational Analytics and Re:infer

Re:infer offers powerful conversational analytics capabilities through its Conversational Data Intelligence platform. Communications Mining is a comprehensive conversational analytics tool that is helping the world’s largest enterprises mine and monitor every business conversation - whether it’s an email, chat exchange or phone call - at speed and scale.

Re:infer is no-code by nature, helping teams rapidly train their own NLP models for conversational analysis in a matter of hours. The platform offers full custom dashboards and real-time alerts, ensuring businesses have constant insight and control over their service channels and communications.

Unlike other conversational analytics solutions on the market, Re:infer is omnichannel and uses advanced machine learning to extract intent data from conversations - revealing the drivers of work, the source of customer issues, and the root causes of operational efficiency.

Learn more about the Re:infer platform.