Conversational Analytics Is More Accessible Than Ever. Use It

Thanks to the cloud and generative AI, the brakes are off the development of conversational analytics systems

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Conversational Analytics Is More Accessible Than Ever. Use It
Conversational AIInsights

Published: February 3, 2025

Charlie Mitchell

Conversational analytics is not a new technology. Contact centers have used it for over 15+ years.  

In doing so, they have analyzed customer conversations – across all engagement channels – digging up precious insights.  

For example, they have determined: why are people contacting us?  Why has traffic spiked? What’s making customers unhappy? The list goes on. 

The catch is that these conversational analytics systems have historically hinged on expansive natural language processing (NLP) models. 

Consequently, their development required expensive, specialist staff. Also, they offered limited language support and narrow vertical applications. 

Now, those limitations are practically gone with generative AI (GenAI).  

The Brakes Are Off  

Traditionally, running a contact center required on-prem virtual and bare metal servers. Adopting advanced analytics under that model was prohibitively expensive. 

Now that everything is cloud-based, implementing many AI features is as simple as ticking a box, according to Carl Townley-Taylor, Product Manager at Enghouse Interactive. 

“There’s no need for additional configuration or expensive on-prem infrastructure,” he said.  

“Additionally, contact centers can test out conversational analytics solutions without committing to massive upfront investments.” – BLOCK 

Then, there’s GenAI. While it hasn’t changed the user interface (UI) of conversational analytics systems all that much, it’s pushing their functionality forwards rapidly.  

Indeed, vendors no longer need extensive research and development (R&D) to support new languages or domains. They can get those up and running quickly because large language models (LLMs) handle that for them. 

Consequently, those providers can focus on ensuring data sovereignty, creating effective visualizations, and bringing use cases to customers faster.  

Auto-Categorization: A Game-Changing New Feature 

As noted, creating NLP models for conversational analytics systems once involved extensive manual configuration. That made industry-specific solutions a rarity.  

After all, developers had to understand the nuances of each industry, like product names or customer types. Yet, LLMs have enabled auto-categorization, which is proving a game changer. 

Sharing an example of how this works, Townley-Taylor said: “We [Enghouse Interactive] partnered with an Australian cattle company. They wanted to categorize interactions based on product names – different types of cattle, in this case.  

“Previously, this would’ve required building custom models to account for all the variations and nuances,” he continued. “With large language models, we simply provide parameters, and the system generates results.”  

“Deployment is faster, costs are lower, and the same technology can easily be adapted for other industries.” – BLOCK 

With this capability, the technology will also drive deeper insights. After all, if a business can categorize interactions by product name, sentiment, or time of day, it can identify new performance trends.  

3 Conversational Analytics Use Cases to Get Started With 

Now that conversational analytics systems have become more accessible and industry-specific, more contact centers can start to test it out.  

As they do so, they may trial numerous use cases. For instance, a service team could track compliance, bolster customer feedback activities, and even predict customer behaviors.  

Yet, in terms of speedy bang for the buck, here are three use cases to get started.  

  1. Automating Quality Scorecards

With this use case, contact centers can go from monitoring one to 100 percent of customer interactions, gaining insight into each.  

Moreover, they can attach metadata to categorize contacts by channel, intent, and customer segment to unlock new performance trends that inform coaching and recognition programs.  

Such insight will also help quality analysts and supervisors pinpoint contacts for manual evaluation most likely to offer the ripest learning opportunities. 

Lastly, it’s not just live agents that conversational analytics solutions can score. Additionally, they may monitor virtual agents’ performance to safeguard their outputs.  

  1. Monitoring Sentiment

Conversational analytics systems not only provide historical reports but also surface real-time insight into why customers are calling and their sentiment levels.  

The latter information is golden for contact center supervisors, who can spot conversations when customer sentiment is particularly low and offer live support to agents.  

Utilizing the tech with a CCaaS platform that offers “whisper” and “barge” options is crucial to enable these real-time support options.  

Additionally, supervisors may track employee sentiment. So, if an agent seems particularly tense, they can intervene to offer them a break or pep talk, safeguarding their wellbeing. 

  1. Isolating Problems

Behind almost every contact is a customer problem. Separating interactions by the issue type and analyzing trends in what each customer has to say will help customer service teams pinpoint the root cause of common issues.  

Using conversational analytics to examine all interactions will lead to Voice of the Customer (VoC) insights on issues such as broken internal processes, product defaults, pricing concerns, and more.  

VoC analysis can identify actions for all aspects of the business which, if acted on, will lead to improvements that lower customer contact volumes, improve customer loyalty, and increase sales.   

Bring Conversational Analytics to the Contact Center with Enghouse 

Despite AI advancements, too many contact centers lack the visibility and insights to make data-driven decisions.  

As a result, they miss opportunities to drive customer satisfaction and revenue.  

Enghouse Interactive helps its contact center customers with its conversational analytics system that transcribes and summarizes interactions, automates the QA process, and unlocks hidden insights. 

In doing so, they can run initiatives that drive positive customer, employee, and business outcomes.  

To learn more about its conversational analytics system, visit:
www.enghouseinteractive.com/products/insights-and-analytics/   

Artificial IntelligenceGenerative AI

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