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If a customer leaves a negative review and a low score on your feedback survey, chances are the relationship is gone. Research from Qualtrics and ServiceNow shows that nearly half of consumers will switch brands after a negative interaction with a customer service portal such as a call center.
Contact center agents, on the front lines dealing with customer issues, are often the last chance to fix a customer’s experience. However, call centers themselves face high turnover rates and training new agents is difficult and expensive. A survey of hundreds of contact center agents revealed that more than one-third said they don’t feel they are set up for success and only 54% believe their management is investing in their team.
“For many companies, responding to feedback after a customer has had a bad experience is not enough to save the relationship. Organizations need to guide and orchestrate experiences in real time and that starts on the front line,” said Brad Anderson, Qualtrics president of products and engineering in an official statement.
To this end, Provo, Utah and Seattle, Washington-based Qualtrics has developed new call center solutions designed to save agents time and provide them with the information and coaching they need to deliver better and sympathetic customer service. These products should also help their organizations lower costs, increase customer loyalty and more accurately identify sentiment, reasons for calls, common issue resolutions, compliance risks and more .
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A new paradigm for call center solutions
Qualtrics’ Real-Time Agent Assist aims to provide agents with real-time coaching on a live call, thanks to Qualtrics’ sophisticated natural language understanding capabilities. Coaching relies on the company’s XM Discover platform, which can analyze conversations in 23 languages and includes models that focus on nuanced terminologies and topics related to different industries.
Based on customer and employee interactions, organizations can understand across a spectrum of over 50 emotions how a customer or employee felt during an interaction; determine what the customer or employee is trying to do; understand how difficult a customer or employee experienced at any step of their journey; and explore the presence or absence of empathy in a conversation.
Real-Time Agent Assist uses these capabilities to analyze real-time conversations along with a contact’s past interactions — calls, chats, and social media posts — as well as insights from the millions of other interactions customers have with the brand across all channels. With all that data, it intelligently assesses the caller’s individual needs and preferences and makes recommendations specific to each business.
“That part is key. Real-Time Agent Assist prompts the agent with brand-specific suggestions, personalized experiences and offers, relevant knowledge base articles, and relevant answers, which helps the agent focus on listening to the customer and reduces the time it takes for an agent to resolve each customer issue,” Fabrice Martin, chief product officer for Qualtrics Customer Care told VentureBeat.
The solution will also recognize when it is appropriate to deliver call reminders to the contact center agent, such as when they may be unscripted or non-compliant, when they should show more empathy for the customer, or when they offer a discount to an unsatisfied customer.
Both natural language understanding capabilities enable Automated Call Summaries to deliver immediate, accurate, automated call recaps that cover all relevant details discussed during the call, including whether why the customer called, how the call went, if the issue was resolved, how much effort was involved. needed to reach that resolution and what steps still need to be taken. Qualtrics says customers find these summaries more accurate than manual entries from their agents.
Context is key: Enter NLU and machine learning
“Most current agent assistance solutions rely only on call metadata, such as call time, duration or phone numbers, and are only programmed to understand key keywords or phrases,” says Martin . “While they may save an agent time in finding a solution in some cases, they lack the broader context and emotional intelligence needed to provide truly valuable recommendations to agents during a call. “
Qualtrics says the XM Discover platform’s natural language understanding and machine learning capabilities have been refined for a decade. Its ability to identify emotions and emotional intensity, intent, effort, and lack or presence of empathy as well as other contextual, brand-specific nuances takes the offering a step beyond the rest. call center solutions.
“Because we can analyze those experience metrics with past customer interactions, as well as insights from millions of other customer interactions with the brand across all channels, we can more accurately diagnose the individual needs and preferences of the caller and make recommendations specific to each business,” said Martin. “With Automated Call Summaries, all of these experience and action data points are added to an automatically updated, unbiased, consistent historical record that plugs into any CRM [customer relationship management] or agent management system that the company wants to use.”
The road to more automation
While CRM has been around in some form for several decades, technological advances such as natural language understanding and customer data platforms are beginning to bring more structure and usability to the river of data generated by billions of customer interactions every day. “Every organization in this economy wants to increase its customer loyalty, and that’s one of the driving forces behind the race toward real-time customer management,” Martin said. “Leading companies will move away from simply asking how they did to orchestrating and personalizing the customer experience as it happens.”
“When you combine a solution like Real-Time Agent Assist with another solution we recently announced, CrossXM, that gives leaders automated insights into the key drivers of employee experience with direct, meaningful statistical impact on customer outcomes,” added Martin, “our [users] will feel very confident in the actions to be taken that will bring the greatest value for both employees and customers.”
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