When the pandemic disrupted every aspect of business and life, telecommunications became nearly as important as electricity to many consumers. In fact, purchases of new internet, phone, and TV services set records in the U.S. Most service providers were able to meet the 30% to 40% increase in bandwidth demand on their networks as millions of people became homebound.1
At the same time, most of the industry, with the exception of wireless carriers, continued to struggle with poor customer satisfaction and loyalty. Record call volume and resulting extensive wait times for assistance during the height of the pandemic exacerbated long-standing customer experience problems.
The question then is how can telecom providers convert record demand into a sustainable advantage going forward given their track record of customer dissatisfaction and churn? To overcome years of customer experience challenges, telecoms must now take big, bold steps to finally connect with customers in a way that meets their expectations.
This playbook explains how — using advances in artificial intelligence (AI) and automation across four different strategic plays — telecom contact centers can optimize the customer journey and the customer/agent conversation to drive measurable and sustainable business value.
Record demand for telecom services
$118 Billion: What U.S. consumers spent to switch or buy new internet, phone, and TV services in 2020
37 million: The number of households that purchased new internet service in 2020, an increase of more than 46% year-over-year
24-point increase: Wireless carriers saw their highest-ever customer satisfaction scores from their business customers, increasing 24 points for enterprise customers and 25 points for small/medium business customers.
Sources:
“2021 U.S. Internet, TV, & Phone Shopping Study,” Julia Tanbark and Tyler Cooper, Broadband Now Research, April 2021
“Wireless Carriers Achieve Record High Satisfaction with Business Customers During Pandemic, but Challenges Loom on the Horizon, J.D. Power Finds,” J.D. Power, October 2020
Why AI has failed telecoms until now
Before we discuss the four 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 early AI products and solutions that telecoms have adopted in the past.
Until now, superficial or point AI solutions did not span the entire customer conversation, leading to disappointing and merely incremental improvements for telecoms. That’s because, while most solutions claim they are AI, in reality they are primarily rules-based. This means that instead of understanding the full conversation, they can only apply rules based on recognizing certain keywords for specific types of interactions. Any success with these types of solutions often came at the expense of the customer and agent experience.
Customer service will always be about conversations, whether they are digital or voice. Without the ability to understand the end-to-end conversation, 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, automation, analytics, and other technologies 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.
Understanding what’s inside a conversation-centric AI platform
The four 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 in multiple languages 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.
AI analytics/intelligent decision support: Machine-directed analytics that use machine learning and reasoning to discover insights, find patterns, and uncover relationships in data, automating the steps that humans would take if they could exhaustively analyze large datasets.
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.
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.
“With customers’ increased reliance on their network infrastructure, comes the
increased likelihood of new issues such as congestion and slower speeds, problems with insufficient WiFi coverage, and security breaches. Telcos should therefore augment their existing customer support services to address a wider range of customer pain points.”
Source: “COVID-19 and the Connected Telco Consumer,” Capgemini Research Institute, September 2020
Play #1: Make Customers Happy With Intelligent Self-Service
Improving the customer experience starts by understanding and optimizing every conversation before, during, and after an agent/customer interaction. Often the conversation starts in a self-service channel, but existing self-service options are frequently more frustrating than helpful for many telecom customers.
With a conversation-centric AI platform that understands customer intent and sentiment, your company can increase self-service and automation rates and deflect transactional interactions from contact center agents — all while delivering an optimized customer experience.
Questions to Determine Current Pain Points
- Are your contact center agents overwhelmed with calls, including many that could be resolved more quickly and easily with intelligent self-service?
- Do your self-service channels understand customer sentiment and intent?
- Do they transfer context to interactions in other channels as conversations move or escalate to an agent from self-service or do customers have to start over in the new channel?
- Can customers quickly find the answer to their questions? /li>
- How often do customers have to start over in a new channel to get their queries resolved?
Action List
- Deploy an intelligent virtual assistant (IVA), across voice and web, to drive the completion and personalization of the customer self-service experience.
- Use an IVA with industry-leading NLP capabilities to automate telecom journeys such as paying bills, inquiring about services, upgrading current services, tracking usage, and more.
- Make sure your IVA uses conversational AI to understand customer sentiment and intent, which will drive self-service automation rates, thus reducing contact center costs.
- Choose an AI platform with automation that passes the context from your IVA to agents to create a frictionless interaction and a shorter average handle time.
Outcomes
- Reduces contact center call volume and costs.
- 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: Reduce Friction in the Customer and Agent Experience
A conversation-centric AI platform optimizes every conversation by enabling agents to be more productive and empathetic while personalizing the experience for customers. By using conversational AI to understand the customer’s real intent and sentiment, you can help your agents deliver a more conversational experience while resolving their issues faster.
With conversational AI and analytics, now every conversation can be analyzed to give your contact center insight into trends and opportunities for improving customer satisfaction as well as sales effectiveness.
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?
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.
Outcomes
- Improves customer satisfaction and loyalty.
- Increases agent effectiveness and performance.
- Drives higher revenues and ARPU
- Enables data-driven operational improvements.
Post-call intelligent analytics save telecom $5.3 million
A large Asian telecom company serving more than 300 million customers implemented post-call intelligent analytics to extract actionable insights for improving the efficiency and effectiveness of their contact center
reduction in average handle time
reduction in human QA effort
reduction in
repeat calls
Million saved over three years
Play #3: Automate After-Call Work to Optimize Experiences
In the telecom industry, what happens after the call ends is just as important to your contact center’s business outcomes as what happens during the conversation. The time spent in after-call work (ACW) — including categorizing and summarizing the call, updating systems, and taking follow-up actions on promises made during the interaction — impacts average handle time, call waiting times, customer satisfaction, costs, agent productivity, and agent satisfaction.
A better customer experience starts with a better agent experience. Your company can use a conversation-centric AI platform to automatically handle ACW, improving both the customer’s and the agent’s experience, while improving productivity and accuracy.
Questions to Determine Current Pain Points
- How much time do your agents spend on ACW?
- How long does it take agents to summarize each call?
- How long is average handle time (AHT) and wait time for other customers?
- How accurate are call notations and summaries?
Action List
- Use conversational AI and RPA to automate ACW and call disposition for high-value telecom journeys such as opening new accounts.
- Deploy a conversation-centric AI platform that automatically listens and transcribes calls in real time. Then after a call ends, the solution should automatically create and present the call summary to the agent to edit and confirm.
- 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
Outcomes
- Improves accuracy of call summaries and dispositions.
- Reduces AHT and shortens wait times for customers.
- Makes it easier for agents to focus on the customer conversation.
- Increases agent productivity by eliminating tedious, manual tasks.
Automation expected to save telecom $19 million annually
A major telecommunications company automated ACW, including call summaries and categorization, for its 16,000 agents. The automation is projected to save the company $19 million annually based on the reduction in agent effort.
Play #4: Capture and Manage Promises Automatically
A promise made that is not kept or tasks that are not performed correctly can quickly negate the positive effects of a good conversational experience. Promises management, or a lack thereof, directly impacts your call handling times, wait times, and customer satisfaction and loyalty as measured by Net Promoter Score (NPS). In fact, a major communications provider in the U.S. has found that the top negative impact on its NPS is missed promises.
A conversation-centric AI platform can automatically recognize, log, and enrich promises. After the call, it sends the customer a summary of the promises made to align expectations and then automatically manages the fulfillment of the promises, which improves NPS, reduces repeat calls, and improves processing time.
Questions to Determine Current Pain Points
- How are commitments (also known as promises) logged today?
- How are commitments fulfilled?
- How many repeat calls are due to missed commitments and follow-ups?
- What additional time is added to call handle times to address repeat and escalated calls due to missed commitments?
Action List
- Choose a platform with conversational AI and RPA designed to automate promises management, such as offering a promotional credit on the next monthly bill.
- Deploy a solution that automatically logs and enriches promises and commitments made by agents in real time.
- Use automation to align expectations with customers immediately following the call.
- Take advantage of RPA to automate fulfillment after the call.
Outcomes
- Reduces repeat calls and improves NPS, where a one-point improvement translates into $3.39 in ARPU (source: Forrester, 2020).
- Shortens AHT by reducing ACW time and effort.
- Improves agent productivity.
- Reduces call waiting time by freeing up agents.
- Reduces the handoffs for approvals and number of screens agents need to log into to execute workflows that register and fulfill promises made.
Next Steps
Although the pandemic clearly demonstrated the importance of connectivity and drove unprecedented levels of demand, 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
- “COVID-19’s Impact Will Evolve the Telecommunications Industry”
Mike Robuck, Fierce Telecom, May 2020
