To thrive in today’s customer-focused landscape, many businesses are attempting to get closer to customers, understand their preferences, and uncover their needs.
In doing so, they need to dive deep beneath the surface of the words their people say to surface insights into intent, satisfaction, and engagement.
Conversational analytics tools are perhaps the most effective solution for driving genuine value from business communications. Using Natural Language Processing (NLP) capabilities, these tools can extract phenomenal data from human interactions.
The result for brands is a better understanding of the customer journey, greater opportunities for loyalty, and more intelligent business decisions.
In this edition of the CX Today roundtable series, our panel of industry experts shares their hottest conversational analytics trends, use cases, and predictions. Today’s contributors are:
- Heather Murphy, Product Manager of Analytics & Insights at Ada
- Jennifer Docken, Product Manager for Quality Management and Analytics at Calabrio
- Frank Sherlock, VP of International at CallMiner
- Derek Roberti, VP of Technology at Cognigy
- Andrew White, CEO of Contexta360
- Gregg Johnson CEO of Invoca
- Swapnil Jain, CEO and Co-Founder of Observe.AI
- Piergiorgio Vittori, CEO of Spitch US
- Surbhi Rathore, Co-Founder of Symbl.ai
- Chris Mina, Head of Contact Center Product Management at Vonage
10 Top Conversational Analytics Trends
1. Conversational Analytics Evolves Into Conversational Intelligence
Rathore: As AI evolves, conversational analytics goes beyond basic and deterministic functions, like auto-summaries and transcriptions, to a place where machine learning can enhance and augment human-on-human interaction and provide in-depth insight in real-time.
As such, perhaps the most exciting trend is the evolution from primarily passive conversational analytics to conversational intelligence, which is proactive and progressive.
Conversational intelligence goes beyond transcripts and logos to look at tone, sentiment, and action points. Such insights offer call handlers real-time intelligence and feedback while automatically populating post-call logs and CRM records.
2. Real-Time Adds Another Dimension to Customer Insights
Vittori: There is a growing interest in real-time or near real-time conversational analytics. This appears to be motivated by a desire from contact centers to better understand their customers, so they can identify and react more nimbly to emerging trends.
Moreover, real-time and near real-time analytics capabilities support in-call triage, alongside more efficient training and monitoring for agents, managers, and higher-level stakeholders alike.
It's also becoming possible to track KPI trends in real-time and identify and resolve developing issues, thereby improving CX and reducing the need for callbacks.
To do so, a live bot can listen in on a call and provide hints and feedback to the agent in real-time, providing both a faster resolution for customers and much-needed agent support
3. Conversational Analytics Changes the Role of the Contact Center
Jain: Contact centers are elevating their position within the CX sphere as business value and growth drivers. Conversation intelligence is enabling them to move beyond isolated, transactional touchpoints and deliver cohesive and meaningful customer experiences. Contact centers can now quantifiably influence revenue generation, lifetime value, and loyalty.
Additionally, more companies will likely use conversational analytics to create models for real-time agent guidance. Combined with real-time data and post-call visibility, they can create richer, more contextualized intelligence that advances CX and revenue generation.
4. Conversational Analytics Adds Maturity to Buying Experiences
Johnson: One tech trend causing lots of excitement is delivering buying experiences that combine digital self-service and the human touch. In tough economic times, leaders can invest in technologies that support seamless digital experiences. This includes delivering live, expert advice during purchases.
With customer loyalty on the line, more businesses are turning to AI tools to focus on automation and efficiency while reducing costs.
Indeed, there is a new wave of companies thinking much longer-term about the opportunity and potential offered by the contact center, and the data within. As such, the contact center will likely become the next tech frontier.
5. Conversational Analytics Lowers the Bar for Meaningful AI Adoption
Mina:
Out-of-the-box templates and machine learning systems designed for specific segments of customer experience and engagement - e.g. sales, support, commerce etc. - will enable automatic intent detection and next-gen sentiment analysis across channels. In doing so, it lowers the bar considerably for broader and more meaningful AI adoption in the space.
The AI technologies this empowers will not only incorporate conversational analytics but flow over to conversational AI, conversational commerce, and broader business intelligence.
As a result, brands may more easily connect the dots between call drivers and critical customer outcomes, which will support self-service automation.
6. Greater Accessibility to Customer Insight Enables Customer Centricity
White: Conversational analytics is not new, but historically it has only been available to the ‘back-office’ analytics teams due to complexity. While back-office staff still need these insights, they’re becoming more accessible to other team members.
Perhaps the most exciting emerging trend is the ability to extend the power of conversational analytics to leaders of small teams and empower them to make day-to-day or even call-to-call improvements based on what is best for the customer.
7. Conversational Analytics Delivers New KPIs
Roberti: Customers want to have a clear idea of customer experience success. They want to know how customers react to each step in the dialog and why. Today’s tools can display KPIs like the number of handovers, conversation length, ratings, and goals reached.
Also exciting is the advancement in optimizing the performance of humans and bots as a team. As the first point of contact, bots will increasingly be at the heart of service interactions, 24/7, and across channels. They can also pass conversations along to humans when necessary. Using conversational analytics, we can examine every touchpoint in this journey.
8. Conversational Analytics Increases Confidence In AI
Sherlock: As more organizations embrace conversational analytics solutions, they’re starting to realize the true power of artificial intelligence (AI).
Indeed, there is a trend toward organizations actively seeking out more advanced AI capabilities. In CallMiner’s recent CX Landscape Report, 33% of organizations said they have fully implemented AI technology and are using it extensively.
Over the coming years, organizations will have to invest the time and resources to properly reap the benefits of AI.
When organizations set clear goals before adopting and implementing conversational analytics solutions, they’ll have a better roadmap for improving critical metrics like agent performance, operational efficiency, customer experience, and more.
9. Conversational Analytics Offers Insight Into Digital Experiences
Murphy: While the emergence of digital channels creates convenient customer experiences, Ada has found that 60 percent of its clients’ demand still comes from the phone. However, customers increasingly want to interact with brands in digital channels like live chat, SMS, and social messaging.
With conversational analytics, brands can build, measure, and improve their strategy for both voice and digital channels on a single platform. Plus, they can learn from the conversations held with customers, identifying trends and opportunities for improvement.
10. Conversational Analytics Supports Contact Center Wellbeing
Docken: Conversational analytics assesses the sentiment of agent and customer interactions in near real-time. This gives contact centers the visibility they need to monitor agent-wellbeing and step in if particular agents are feeling particularly low.
Such innovation is especially helpful in the world of remote work, supporting agents when they are at their lowest, giving them a pick-me-up, and reenergizing their motivation to help customers.
10 Top Conversational Analytics Use Cases
1. Root-Cause Analysis
Vittori: The next step beyond analysis and identification is explanation. Spitch tries to help clients understand not only what is happening or when something is happening but why particular actions are taking place.
The ability to uncover event trends or outliers and identify underlying causes can provide businesses and managers with clear direction, not just in understanding that they have a CX problem but steps to take in addressing it. Or, conversely, when things are going well, to understand what is driving that improvement and how to maintain it.
2. Plugging Data Gaps
Johnson: To provide the best possible call experience, the DIRECTV team uses Invoca data to send its sales agents a screen pop with each caller’s digital journey information before a call is connected.
With pre-call data, contact center agents can proactively understand the caller’s needs and tailor the conversation accordingly. This creates a seamless digital-to-call experience that makes each caller more likely to convert.
Pre-call data also reduces call times since agents already know the context and can jump into the conversation.
3. Self-Service Automation
Mina: Self-service automation is gaining traction, but much more work is necessary. Advances in conversational analytics will help brands to more easily understand what conversations can and should be automated first through digital deflection and Conversational AI.
As a result, brands may further the ability of agents to focus on higher-value and more complex conversations. At the same time, transactional contacts are handled automatically, with lower cost, less time, and greater customer satisfaction.
4. Driving Customer Insight Across the Business
Murphy: The hidden gem of conversational analytics is using conversational analytics data to inform product and marketing strategies.
For example, Pair Eyewear learned from customer interactions with Ada’s conversational AI that their customers were interested in buying non-prescription lenses, specifically blue light glasses.
Pair’s CX team shared with leadership how often customers asked their Ada-powered bot about non-prescription glasses with blue light lenses (thousands of times per week) and made a case to launch a blue light glasses campaign to capitalize on this opportunity in the market. This campaign ultimately drove a 2.3x increase in revenue for the product line.
5. Monitoring Agent Language
Docken: Manners matter. Considering how agents use courtesy words such as “please” and “thank you” is a critical first step towards developing the all-important soft skills necessary for excellent service experiences.
But what about the word "sorry"? It’s a phrase that people use all the time, but do they really mean it?
Leaders at Bluegrass Cellular ran ‘sorry’ phrases through a speech analytics solution to understand the perceived strength and sincerity of agent apologies. They found representatives repeatedly used “I’m sorry” as a way to pause a conversation rather than sincerely apologize.
Leaders built a custom training program to correct the situation, featuring tangible examples of proper/meaningful apologies.
Bluegrass Cellular immediately shrank the number of insincere agent apologies by 40 percent and decreased the number of formal customer complaints by 43 percent.
