Three Strategies for Using Conversational AI to Optimize Customer Experience and Outcomes

Three Strategies for Using Conversational AI to Optimize Customer Experience and Outcomes

11 min read

Driving more value from every customer experience

Today’s telecommunications industry looks nothing like it used to. With the rapid shift to work-from-home and hybrid workforces, service providers initially saw great gains. However, as that shift has normalized, it has subsequently created a new set of challenges fueled by higher customer expectations and more competitive product and service offerings.

With more options for communication and broadband services and with little to no switching costs, customers now have the power to change service providers whenever it suits their needs. That means providers have to win over customers at every customer experience touchpoint—or risk losing them to competitors with better CX. Every touchpoint has a direct impact on loyalty, customer lifetime value and top-line growth.

At the same time, service providers are being asked to do more with less. During the global pandemic, providers invested in virtual agents to handle basic inquiries and manage high call volumes. As a result, agents now spend more time dealing with the more complex requests. That rise in complexity—coupled with outdated tools and training—is driving agent attrition up and customer experience down. Those agents who remain are expected to deliver fast, frictionless service that grows revenue and customer lifetime value. In other words, to transform what has traditionally been a cost driver into a value center. Given the current situation, telecom providers should focus on three key areas:

Customer Loyalty and Retention

Operational Efficiency and Effectiveness

Agent Lifecycle

This playbook explains how — using advances in artificial intelligence (AI) and automation across these three different strategic plays — contact centers can flip the switch to becoming a value center and gain a lasting competitive advantage in the new telecommunications marketplace.

Unlocking the full value of every conversation

Before we discuss the three contact center plays to optimize customer experience, it’s important to understand how the new conversational AI technology that makes them possible is different than the simple point solutions that telecoms have adopted in the past (and that many continue to use).

Customer service will always be about conversations, whether they are digital or voice. That includes not only the words spoken but also the behaviors, intents and emotions expressed by the speaker. Without the ability to understand the end-to-end conversation (i.e., before, during and after) and connect both verbal and nonverbal cues to their relevant actions and knowledge, point solutions are unable to bring about the type of contact center transformation that telecoms need to make significant leaps in customer satisfaction and loyalty.

That’s why a conversation-centric, platform-based approach that integrates the latest advances in conversational AI, emotion AI, knowledge AI, automation and analytics is the better approach. By understanding the entire conversation, a conversation-centric AI platform can optimize customer and agent experience and deliver significant, measurable business value.

Top challenges for telecom services


of customers switch telco providers due to bad customer service*


switched providers because their issue wasn't resolved in the first call (i.e., poor FCR)*


of customers would be willing to spend more with companies that offered excellent CX**

1 in 3

customer service reps is at risk of turning over due to poor agent experience***

Understanding what’s inside a conversation-centric AI platform

The three plays described in this ebook rely on a conversation-centric AI platform that combines multiple technologies to enable understanding, optimization, and automation of end-to-end conversations in virtually any language.

Core capabilities for a conversation-centric AI platform

Conversational AI: A set of advanced AI technologies that recognizes and comprehends human language and uses this understanding to optimize and analyze conversations in and across multiple channels.

Natural language processing and understanding (NLP/NLU): Components of conversational AI that help computers understand and interpret human language.

Robotic process automation (RPA): Software that can emulate the actions of a human interacting with digital systems to automate repetitive tasks and end-to-end business processes.

Intelligent applications: AI-powered software that includes rules engines, user interfaces, notifications, and alerts, and other components that handle specific use cases within the contact center, such as intelligent agent assistance, intelligent self-service, and others.

Emotion AI: A highly advanced subset of artificial intelligence that uses computer vision, tonal and facial analysis to “read” emotions during real-time interactions and deliver actionable insights based on audio and visual data.

Knowledge AI (KAI): KAI synthesizes structured and unstructured data in the form of knowledge bases, website pages, documents and more and surfaces the exact information virtual agents (self-service) and human agents need to accurately answer customer inquiries.

Throughout the rest of this ebook, whenever we mention specific technologies such as conversational AI, NLP, RPA, or others, we’re referring to these capabilities within a conversation-centric AI platform.

“Our priority is we’ve got to protect the base that we have. We’ve got to be
able to protect and grow our ARPU. We’ve got to compete appropriately but
not irresponsibly, to try to drive subscriber volume. [Mobile] is a very good value to the consumer, if you look at our service plan pricing attached to a
compelling broadband proposition.

Source: Jason Armstrong, CFO & Treasurer, Comcast, Deutsche Bank Media, Internet & Telecom Conference, 2023

Play #1: Focus on Customer Loyalty and Retention

Growing the Customer Lifetime Value (CLV) of your existing customer base is the most efficient way to drive revenue growth—significantly faster and more cost effective than new customer acquisition. In fact, research shows that it can cost 5 to 10 times more to acquire a new customer—with no guarantee on ROI—than to sell to an existing one. By contrast, returning customers spend 67% more on average than new ones. According to Bain & Company, a mere 5% increase in customer retention can grow profits by as much as 95%. That’s huge.

Consider this equation: CLV = ARPU x Months. There are two ways providers can grow CLV: 1. Increase ARPU (Average Revenue Per User) through upselling and cross-selling; and 2. Increase Months via excellent customer service that drives loyalty. While providers can’t always control ARPU, they can control their customer experience. By eliminating CX friction points, telco companies can reduce bad experience-related customer churn. And with 60 percent of customers considering switching providers after two or fewer bad experiences, CX is pivotal to CLV growth.*

With a conversation-centric AI platform that understands customer intent and sentiment, your company can zero in on the three pillars of customer loyalty and
retention: upselling and cross-selling (upstream retention), customer experience and proactive churn reduction (downstream retention).

*Source: CCW Study

Questions to Determine Current Pain Points

  • Are your customers defecting to rival providers or cherry-picking products and services from several providers?
  • Can your self-service channels understand customer sentiment and intent and can they identify upsell/cross-sell opportunities?
  • Can they assist customer service reps with upselling and cross-selling with contextual insights, sales prompts and guidance for overcoming objections?
  • Can they initiate proactive engagement through automated outreach and post-interaction follow-ups?
  • Are poor metrics (Average Handle Time (AHT), First Contact Resolution (FCR), wait times, etc.) dragging down your Net Promoter Score (NPS) and impacting customer retention?

Action List

  • Deploy an agent assist program that guides customer service reps on what to say during live interactions to increase upselling and crossselling.
  • Leverage conversational analytics to identify customers at risk of churning as well as their specific pain points.
  • Use analytics to find and fix frictions points within the customer journey that are impacting key metrics.
  • Choose an AI platform that can automate proactive engagement actions, like outreach emails and post-interaction follow-up communications.


  • Increases CLV through loyalty-building actions and initiatives.
  • Grows revenue by upselling and cross-selling products, services and plan upgrades.
  • Creates “stickiness” (i.e. how indispensable you are to a customer).
  • Decreases call waiting times for customers.
  • Improves customer experience and satisfaction by resolving their queries faster.
  • Further improves the customer and agent experience when escalating from self-service to agent assistance by alerting the agent to customer sentiment, intent, and next-best actions.

Play #2: Increase Operational Efficiency and Effectiveness

A conversation-centric AI platform optimizes every conversation by minimizing the amount of manual effort required, enabling agents to be more productive and empathetic while personalizing the experience for customers. It also streamlines time-consuming processes and post-call tasks, like After-Call Work (ACW) and promise management, through automation.

With conversational AI and analytics, you can leverage conversational data to improve operational processes and eliminate friction points that drive up call effort, time and cost.

Questions to Determine Current Pain Points

  • Do your agents have in-call coaching guidance based on understanding customer intent, sentiment, and emotion?
  • Can agents immediately access customer information, such as customers’ current equipment, without having to manually look up information in multiple systems?
  • Are agents quickly resolving customer issues or are call handle times exceedingly long?
  • What percentage of your customer interactions are you able to analyze today?
  • Are you using post-call analytics that help identify trends and points of friction?
  • How much time are agents spending on ACW?
  • Are agents fulfilling promises made during calls manually or do they have assistance?

Action List

  • Choose a conversation-centric AI platform that provides real-time analysis of customer context, including sentiment and intent, to help your agents through in-call coaching alerts, giving them the insight to be more empathetic as well as improving upselling and cross-selling. For example, the AI could suggest that the agent offer a triple or quadruple play bundle when a customer already has more than one service to increase ARPU.
  • Use NLP and conversational AI capabilities to analyze and detect customer and agent-centric patterns across voice, email, text, or chat. This can help you identify points of friction in the customer journey.
  • Automate post-call analytics on 100% of customer interactions to understand reasons for customer churn and sales effectiveness, drive compliance, and identify other opportunities to improve core customer and agent-centric experience areas.
  • Automate ACW tasks, like call summarization, that bog down agents and keep them off the call queue.
  • Automate promise management actions, like follow-up communications, to minimize agent effort and improve customer satisfaction.


  • Increases agent effectiveness and performance.
  • Improves note-taking accuracy and consistency.
  • Eliminates operational bottlenecks caused by manual ACW
  • Ensures promises made on calls are kept.
  • Enables data-driven operational improvements.
  • Improves customer satisfaction and loyalty.

Play #3: Optimize Agent Lifecycle and Employee Experience for Faster Proficiency and Better Retention

The telecom industry has long struggled with high agent attrition rates. While estimates vary, roughly 1 in 3 customer service reps is at risk of turning over, according to research by Gartner. Given the resources invested in agent recruiting, training and onboarding, that number represents a significant hit to a contact center’s bottom line. It also impacts a center’s ability to consistently deliver excellent customer service, with fewer fully proficient agents available to assist customers.

A better customer experience starts with a better agent experience. Your company can use a conversation-centric AI platform to improve agent experience by accelerating training and onboarding, assisting agents during live interactions and automating manual processes and knowledge searches that fuel agent stress and attrition.

Questions to Determine Current Pain Points

  • How much time do your agents spend on manual processes, like data entry, searching a knowledge base and ACW?
  • How many programs must agents learn to use?
  • Do your agents operate independently or are they assisted during calls?
  • How long does agent training and onboarding take on average?
  • How much does it cost to recruit, train and onboard a new agent?
  • What is the average lifecycle of an agent at your contact center?

Action List

  • Use conversational AI and RPA to automate time-consuming manual processes, like note-taking and knowledge searches.
  • Deploy a conversation-centric AI platform that understands caller context and provides agents with real-time guidance and next-best action prompts.
  • Leverage AI to train and coach new agents during live interactions instead of relying exclusively on classroom training.
  • Choose a solution that automatically updates the CRM system as well as other applications your contact center relies on and provides automated call disposition to maintain the quality of call categories.


  • Increases agent retention and satisfaction.
  • Saves on agent hiring and onboarding costs.
  • Reduces classroom training time and accelerates speed to proficiency with automation.
  • Makes it easier for agents to focus on the customer conversation.
  • Increases agent productivity by eliminating tedious, manual tasks.
  • Shortens call times by automating backend system tasks.

AI-optimized operations = real cost savings

A $213 billion telecommunications company with 40,000+ agents around the globe was struggling with long average handle times. The problem: manual call summarization was grinding after-call work to a crawl and creating reporting inconsistencies. The solution: call summary automation powered by conversational AI.


Reduction in after-work call


Increase in call summary accurarcy


Seconds cut from repeat calls on average

Agent experience matters now more than ever

With more remote work options available, contact center agents today are more likely to switch employers based on agent experience.

1 in 3

customer service reps is at risk of turning over


Disengaged agents are 87% more likely to look for a new job


is the average cost to replace a contact center agent

Next Steps

In today’s hyper-competitive telecommunications market, one thing is clear: telecom companies must do more to retain those customers going forward and grow ARPU. This requires taking bigger, bolder steps than ever before to improve the customer and agent experience.

Conversational AI can be the foundation for optimizing every conversation to transform the customer and agent experience, drive customer satisfaction, and generate greater loyalty and revenue. An AI platform that addresses the entire customer conversation can help your telecom contact center deliver a positive, frictionless experience for your customers and agents.

Take the next step and find out more about conversational AI and how a conversation-centric AI platform can help you solve your customer service challenges.

Table of Contents