Ten conversational analytics experts share their favorite speech analytics use cases
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.
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.
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.
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.
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.
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.
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.
For example, MoneySolver needed a way of measuring performance across a team of 100 people. Previously, they had to listen to sample calls to perform QA measures – a time-consuming process. Upgrading with conversational analytics allowed the company to set up automatic scorecards to assess agents on every call. The company increased its close rate by two times as a result.
Notably, the team also found using an automated system helped to take “human bias” out of the equation and push employees to take ownership of their performance.
Conversation intelligence is supercharging agent performance by enabling personalized coaching at scale. It creates a more scientific approach to coaching that’s contextualized to each agent’s specific skill, behavioral, and knowledge gaps. It pinpoints winning behaviors and practices of top-performing agents, so these can be operationalized across entire teams.
Traditionally, supervisors review a handful of calls for each agent every month, leading to coaching based on guesswork and generic recommendations. Implementing conversational intelligence improves training and upskilling while enabling greater team trust and transparency.
Agents understand that their feedback is based on actual performance data, and they’re given specific guidance. This contributes to overall higher job satisfaction and reduced agent attrition.
With a conversational analytics platform, quality management (QM) managers can create, modify and publish QM forms in one place, and share them securely.
Auditors can access information from a unified environment, and because the QM system links directly to the conversational analytics platform, all users can leverage the same searching, sorting, and filtering features. This makes it easier for managers to observe near-live trends in QM and make intelligent decisions.
The Spitch QM and QA tools also support automatic, AI-driven assessment learning. The system itself learns to sync its AI-driven assessments for any form to a small set of manually audited calls. This makes it possible to extend QM and QA processes to every customer contact and allows users to catch, assess and understand every call, and identify potential issues as quickly as possible.
Contact centers are labor-heavy environments requiring significant investment in people and time. That time is valuable, but so is the human-to-human interaction. That’s the bit that needs to be preserved, enhanced, and allowed to happen unhindered.
We don’t want to replace human-to-human interaction but enable it as much as possible. That’s something conversational analytics helps with.
For example, let’s say a call center agent gets off a call with a customer and has several more customers in the queue. Before the agent can get to the next call, they must manually update the previous customer’s record. This takes time that could be better spent talking to the next customer instead of leaving them on hold.
Conversational analytics can remove this headache from call center agents, automatically populating records as calls occur in real-time and generating summaries and next actions once the call is over. Records will be accurate, consistent, and ready for the next agent to pick up.
Conversational analytics are beginning to help brands adopt downstream AI-based technologies, like Conversational AI (CAI) and next-generation Interactive Voice Assistants (IVA).
Those brands that invest early stand to gain first-mover advantages and drive down their total costs of adopting self-service CAI technologies while improving outcomes for the consumer.
For instance, a contact center looking to transform itself can feed a high-quality analytics tool hundreds of thousands or millions of historical conversations and, with the right understanding of what to look for, quickly identify trends and areas of opportunity to better serve their customers 24/7, with lower effort, through automated bots.
Once the trends and opportunities are understood, the same tools viewed from a different perspective provide conversational designers all they need to build great consumer experiences with high containment while more quickly shuttling pre-identified high-value or complex conversations to the [now] smaller but more effective agent pool.
Interested in learning more from our experts? Then, check out our article: Conversational Analytics: Trends, Use Cases, and Predictions