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Conversation analytics captures and analyzes customer interactions across voice and digital channels using artificial intelligence (AI). It empowers organizations to understand what customers are saying, how they feel, why they’re contacting you and what happens next. CallMiner Eureka transforms conversations into actionable insights your teams can use to enhance customer experience, agent performance, operational efficiency, compliance and business results.
By unifying omnichannel conversation data, CallMiner gives contact centers and enterprise teams a clearer, evidence-based view of customer intent, sentiment and behaviour at scale. This AI-powered conversation intelligence helps organizations identify emerging issues sooner and make faster, more informed decisions from the voice of the customer.
Conversation analytics involves leveraging artificial intelligence, machine learning, natural language processing (NLP) and related technologies to automatically analyze customer conversations across channels.
Instead of manual call reviews or basic reporting, conversation analytics can process interactions at scale to provide insights on:
Conversation analytics provides your organization visibility into what’s really going on during customer interactions, beyond surveys, manually reviewed calls and operational metrics.
Every interaction has data about customer needs, expectations, frustrations and experiences. When your organization has thousands or millions of interactions, manual analysis just isn’t possible. Conversation analytics software transforms unstructured interaction data into structured and searchable insights.
Conversation data empowers organizations to answer questions like:
CallMiner Eureka conversation analytics platform provides a comprehensive conversation analytics solution that enables you to extract intelligence from every conversation. Leveraging an end-to-end approach built around three key stages (discover, evaluate, act), Eureka helps ensure critical insight flows smoothly from your data through to business impact.
1. Capture interactions across channels Integrate customer conversations from voice and digital channels, including phone, calls, chats, emails, SMS/text and other digital interactions, to gain a more holistic view of the customer experience.
2. Analyze every interaction Apply AI to uncover hidden insights across conversations, such as words, phrases, topics, sentiment, emotion, intent, behaviors and other signals that provide valuable context about the interaction.
3. Identify patterns and opportunities Spot trends across your conversation data, whether it’s frequently mentioned customer complaints or attributes associated with successful outcomes.
4. Turn insights into action Put conversation intelligence to work to enhance coaching, quality, compliance, customer experience and more across your organization.
There’s insight ready for action in every customer interaction, but it can only help your team if you know where to find it. With CallMiner powering insight across the business, there’s no conversation happening that’s too small or insignificant to drive measurable improvements in CX, agent performance, compliance, efficiency and revenue. Here’s where companies typically see the biggest impact with CallMiner.
Enhance customer experience. See what customers need and pinpoint moments that cause frustration, effort or dissatisfaction. Outcomes: Lower customer effort, higher first-contact resolution and smoother customer journeys.
Boost agent productivity. Focus coaching on specific areas instead of a limited sampling of interactions or subjective reviews. Outcomes: More effective coaching, increased consistency and visibility into the behaviors of top performers.
Automate quality assurance. Evaluate more interactions against standardized criteria and decrease reliance on manual QA sampling. Outcomes: Increased QA coverage, less manual effort and consistent scoring.
Mitigate compliance risk. Stay on top of conversations that require certain language, trigger warnings for prohibited words, include important disclosures, or pose other compliance risks. Outcomes: quicker insight into deficiencies, enhanced auditing practices and improved script compliance.
Optimize efficiency. Discover why repeated calls, long calls, transfers and escalations are happening. Outcomes: Reduced handle time, improved call routing and a decrease in unnecessary contact volume.
Improve customer retention and revenue. Identify signals of churn, dissatisfaction, buying intent or missed sales opportunities. Outcomes: Understand churn drivers, recognize upsell and cross-selling opportunities and gain insight into customer objections.
Conversation analytics can have a powerful impact far beyond the call center. CallMiner puts your conversation data to work across every department. Here’s how each team leverages CallMiner’s conversation analytics.
Founded in 2002, CallMiner pioneered the speech analytics industry. With billions of hours of conversations mined, we provide exceptional value to customers by delivering highly effective, usable, and scalable conversation analytics solutions.
Our organizational agility allows us to adapt to the ever-changing needs of our customers. In fact, over 300 customer-requested features have been added to our products. Our customer success team brings years of hands-on experience to every customer engagement.
With CallMiner Eureka, you can:
“CallMiner has helped improve the coaching experience overall because it now gives us a wider range of how a team member is doing. Previously, we would only be able to see 3-5 calls, and have to assume the majority of the rep’s calls were along the same lines. Now, we’re able to see a majority of the rep’s calls and get a much clearer picture of the rep’s world class service.”
"We know that CallMiner in the call center will tremendously help us from an efficiency standpoint, but also enable the business at large to gain better insights into what our customers saying and what they want when they're talking about our service."
Your customers are telling you what matters to them, where they’re struggling and how your organization can improve. CallMiner conversation analytics empowers you to listen to those cues at scale and act on them to improve customer experience and drive business results.
Conversation analytics can be used to drive improvement in call center KPIs. The most important KPIs include average handle time (AHT), or the average amount of time it takes an agent to complete one transaction. First response time (FRT) is the time customers are on hold before speaking to an agent for the first time. First contact resolution (FCR) is the percentage of first contacts during which a customer’s needs were met without requiring a second contact. Customer satisfaction (CSAT) is a customer experience metric that shows how satisfied customers are with a company’s products or services.
Sentiment analysis is a form of speech analytics that monitors conversations and evaluates language and voice inflection to quantify the attitudes and opinions of an individual speaker. Companies use sentiment analysis to evaluate how customers feel about the business, a product or service, or a specific topic.
Conversation analytics enables organizations to understand their customers on a deeper level by capturing, transcribing, analyzing, categorizing, and scoring customer conversations across channels. By converting the unstructured information in customer interactions into structured data that can be analyzed and searched, conversation analytics enable companies to gain deeper insight into customers’ needs, wants, behavior, and emotion. Using this data, companies can focus on improvements that deliver better customer experiences to drive customer satisfaction and loyalty.
Conversation analytics captures and analyzes conversations across voice and digital channels, including phone calls, chat, email, SMS/text messaging and beyond. Analysis across channels provides a richer view of the customer journey than a single channel offers.
Real-time analytics analyzes conversations as they’re happening, surfacing sentiment, intent and compliance signals with AI so that supervisors and agents can act on them immediately. Historical analytics analyzes conversations after they’re finished to identify trends for longer-term coaching and process improvements.