Generative AI
Jun 5, 2024
Customer conversations hold the keys to driving value across the entire organization.
Historically, efficiency metrics like handle times and first call resolution have been the focus for customer care teams. But now, with rising customer expectations, it’s less about hitting efficiency goalposts and more about getting customers what they need, when they need it.
Insight into customer conversations is the cornerstone of this journey. By understanding the nuances of customer needs expressed in sales and support interactions, businesses can pinpoint areas for improvement, tailor their services, and preemptively address issues.
With new advancements in conversational analytics, there is a deeper layer of insights that can be used to locate unmet needs and unknown issues, creating an unprecedented opportunity to impact revenue across sales, marketing, supply chain, and more.
Earlier generations of conversation analytics lacked depth in insights, but now, advancements in technology allow businesses to more easily get the insights they need, with little configuration, and take action where it matters most.
In this blog, we will explore various paths to achieving ROI with contact center data analytics and how technologies like Generative Conversation Intelligence can play a pivotal role in this transformation.
Table of Contents
What is Generative Conversation Intelligence?
Generative Conversation Intelligence (CI) uses multiple large language models, or LLMs, to analyze millions of customer data points at once and extract robust, AI-generated insights on critical business risks and opportunities. Generative CI surfaces topics, themes and keywords by itself at a level of detail that is unmatched by most other technologies on the market.
So, how does it work?
The first step involves taking call recordings and running this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations. This combined use of machine learning and natural language processing technology is called conversation intelligence.
From here, the data is routed through a pipeline of numerous LLMs, each designed to tackle a specific action or business question. Some common analysis points include:
Customer intent, or the reason behind why a customer has expressed a frustration, concern, or desire
Customer sentiment, which uncovers instances of negative and positive emotions expressed by the customer
Agent sentiment, which uncovers instances of negative and positive emotions expressed by the agent
Specific details about supply chain, operations, fulfillment, product quality, and more
1. Reducing Customer Churn
Customer churn is one of the most important metrics that businesses use to gauge health.
The cost of acquiring a new customer is five times higher than retaining an existing one, so retaining the existing base is important.
Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.
But in order to really make an impact, it takes getting to the root drivers behind why customers leave in the first place, and that’s where churn prediction software can play a key role.
With generative insights, businesses gain 100% visibility into interactions to deeply understand churn drivers, and act automatically to address them. By analyzing every touchpoint and communication, companies can identify patterns and pinpoint specific issues that lead to customer frustration.
This comprehensive insight enables businesses to not only react to problems but also to predict potential churn before it happens.
Check out an example of how an online retailer crush a top churn driver on our Path to ROI Guide.
2. Reduce Operating Costs
Harnessing AI and automation isn't just about cutting costs—it's about maximizing output.
By automating the most repetitive tasks in a customer service organization, employees can focus on the work that makes the biggest difference, rather than reviewing hundreds of calls manually, for instance.
With generative CI, analyzing every customer interaction is completed within minutes. This eliminates the need for manual review, saving businesses valuable time previously spent identifying reasons behind call surges or complaints.
Generative insights are also used to automate quality scoring and acting on insights, which also relieves the burden of having to communicate alerts manually across departments.
NativePath, a health and nutrition brand, was spending 75% of the week manually reviewing calls to glean insights into agent performance. It was a time-consuming process that hindered efficiency and productivity. Now, NativePath has made its review process quicker than ever, reducing time spent by 90% across the board.
3. Boosting Performance
To generate more sales, it’s necessary to have full visibility into what sales reps are saying across every interaction to effectively evaluate sales performance.
Generative CI can be used to uncover what sales reps are saying that’s working well, and use these learnings to improve scripts and convert more leads into new business. Plus, it’s easier to ensure that reps stick to objection handling guidelines and enforce sales scripts through targeted coaching.
Businesses can answer questions like, ‘What are the real reasons customers aren't buying? How do our best sales reps handle tough objections? What products or services are we missing?’
Learn more about how a telecom provider can improve quota attainment with generative insights on our Path to ROI Guide.
4. Improving Agent Behaviors
Having a close pulse on how agents are performing plays a major role in your ability to make necessary changes to customer care processes, scripts, and more. With automatic scoring of agents, you can have data at your fingertips to make sure your agents are set up for success.
This real-time visibility allows managers to identify performance issues as they arise and intervene promptly, providing immediate coaching or support to agents. By continuously monitoring and scoring interactions, integrating customer sentiment analysis for a deeper understanding, companies can maintain a consistently high standard of service, ensuring that every customer receives the best possible experience.
Full visibility also provides a way to identify training needs, surfacing key areas in which agents struggle to adhere to brand standards or compliance requirements.
With automated quality assurance, businesses can instantly grade 100% of interactions, identifying top and bottom performers to prioritize coaching based on sentiment, resolution, and QA scores. Overall, they can stop random sampling and instead ensure every interaction that needs to be reviewed gets it.
It’s how a wellness brand rectified a potentially damaging customer experience—read the full story on our Path to ROI Guide.
5. Increasing Conversions
Analyzing conversation data to detect intent signals is a critical way to improve acquisition efforts. Leveraging these findings, businesses can activate workflows or campaigns in customer engagement platforms to capitalize on time-sensitive customer signals and enroll customers into up-sell or cross-sell campaigns.
First, businesses can use intent signals that are extracted from conversations to supercharge AI models that let marketing leaders know which leads are likely to convert, and which aren’t to optimize ad spend. Altogether, these insights unlock a better understanding of how likely leads are to move further into the funnel, and which leads are worth targeting.
From these learnings, businesses can then trigger workflows or even campaigns in customer engagement platforms to act on these perishable customer signals.
It’s how Centerfield has narrowed its Google and Facebook targeting, creating look-alike audiences based on what is learned about preferences and demographics through customer conversations. Learn more about Centerfield uses intent to improve ad spend and close more business on our Path to ROI Guide.
"Echo AI has become a centerpiece in our strategy to supercharge customer acquisition. Every customer conversation contains insights that can improve personalization, drive a conversion, or prevent churn."
—Aniketh Parmar, EVP of Sales, Centerfield
Get the Guide
Generative insights hold the potential to drive significant revenue impact across an entire organization.
In an era where customer expectations are continually evolving, the emphasis has shifted from merely tracking numbers to truly understanding and meeting customer needs in real-time.
By leveraging the right tools and systems, you can transform customer service into a proactive function that enhances the relationship between your customer and your brand while fueling revenue growth across the entire business.
Our guide, "The Paths to ROI with Generative Conversation Intelligence," shares scenarios in action to show you how business leaders are moving the needle towards retention goals, churn reduction, and more. Discover ways to elevate your customer care operations and see tangible results.
Download the full Path to ROI Guide below.
