As customer expectations continue to evolve, companies are under increasing pressure to gain a deeper understanding of their clients, their needs, and the pain points they want to address.
Fuelled by Natural Language Processing (NLP) and Natural Language Understanding (NLU), conversational analytics tools extract meaningful data from the countless discussions taking place between brands and their target audience.
This helps leaders make better decisions about how to utilize resources, train staff, and ultimately earn customer loyalty.
Yet, there are many more conversational analytics use cases helping to enhance contact center performance, as following industry experts spotlight below.
- Heather Murphy, Product Manager, Analytics & Insights at Ada
- Jennifer Docken, Product Manager for Quality Management and Analytics
- Frank Sherlock, VP of International at CallMiner
- Derek Roberti, VP of Technology, Cognigy
- Andrew White, CEO at Contexta360
- Gregg Johnson CEO at Invoca
- Swapnil Jain, CEO and Co-Founder at Observe.AI
- Piergiorgio Vittori, CEO at Spitch US
- Surbhi Rathore, Co-Founder at Symbl.ai
- Chris Mina, Head of Contact Center Product Management at Vonage
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Supporting Conversational AI
Some chatbots used in the contact center today are limited. Pre-defined journeys and responses restrict the conversational experience and lead to customer frustration. But conversational AI platforms like Ada can converse with customers. They invite users to type their questions in their natural language. This opens up a world of opportunity for AI to analyze those typed questions, draw insights from those interactions, and inform the business about new and evolving customer needs.
The trouble comes in the case where a customer needs more help from a human agent. Passing the conversation from a bot to an agent is a make-or-break moment. Customers don’t want to repeat themselves.
Vendors like Ada use AI to generate a summary of the automated interaction and pass it to the live agent. That way, they can quickly get context on the customer's needs and provide the best possible experience.
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Enabling Company-Wide CX Improvement Initiatives
The beauty of conversational analytics is it unlocks critical customer insights to support successful company-wide CX initiatives.
Take Idaho Central Credit Union (ICCU), this year’s winner of our Analytics Competition. ICCU has saved thousands of dollars by eliminating more than 9,000 repeat calls and reducing the cost per call. It achieved this by combining various innovative analytics solutions, including data and speech analytics, to assess repeat calls and identify phonetic phrases that indicated negative sentiment. It then changed the language of the contact center.
However, for many operations, capturing copious amounts of data is never quite so simple, with much of it ending up in a massive black hole, a critical barrier to effective CX.
To minimize the risk of lost data streams, consider combining call recordings with screen recordings and agent keyboard metadata to provide complete and intelligent interaction insights. Then, transform these streams into trends and implementable next steps with the help of conversational analytics.
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Monitoring Regulatory Performance
Many organizations, particularly those in regulated industries, require their contact center agents to make certain disclosures for compliance. When regulations change, this also changes what agents need to say when talking to a customer.
When agents miss or make mistakes on compliance statements, it impacts more than just the customer experience. If audited, this can impact brand reputation and the bottom line. Post-interaction conversational analytics aren't suited to addressing this problem. Alternatively, real-time analytics can effectively manage compliance risks.
For example, real-time conversational analytics can understand if an agent has missed a specific compliance statement and send an alert. Similarly, alerts can be used as positive reinforcement if agents follow policies correctly.
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Understanding Customer Behaviors
An excellent example of conversational analytics transforming the contact center is evident in the cancellation process. Due to the pandemic, cancellations have become a significant issue for contact centers throughout the travel industry. Companies must monitor why and when these cancellations happen, and conversational analytics allows this at every step of the conversation.
Conversational analytics visualizes and reveals at what point in the conversation customers drop off, at what point they request to be handed over to a live agent, and why. And the analysis isn't just restricted to voicebots and chatbots: it also supports all the interactions that occur after a handover to a live agent. It's all about the entire customer experience. Customers don't distinguish between bot and human – they view their journey as a whole!
It's even possible to follow up in third-party systems. For example, one of our telecoms customers sold Internet upgrades automatically by bot during the lockdowns. Using conversational analytics, they could analyze how interactions take place in the respective channel and the linked ordering system.
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Enhancing Quality Assurance
Conversational analytics has enabled significant growth opportunities in the realm of using analytical tools to automate and enhance the quality assurance (QA) processes.
Using conversational analytics, it's possible to streamline the QA process from one often deemed repetitive and time-consuming to one that's insightful and straightforward. Users can automate everything from contact discovery to scorecard creation and coaching opportunities.
In some cases, this technology could save companies millions in time and productivity.
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Automating Call Scoring
AI-driven conversational intelligence platforms are increasing productivity and accuracy in the contact center. With most ignoring 99% of their agent calls, QA teams fail to analyze many conversations. This leads to missed sales opportunities and lost revenue.
