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If a customer leaves a negative review and a low score on your feedback survey, it’s likely that relationship is already lost. Research from Qualtrics and ServiceNow shows that nearly half of consumers would switch brands after a single negative interaction with a customer service portal such as a call center.
Contact center agents, on the front lines addressing customer issues, are often the last chance to remedy a customer’s experience. However, call centers themselves face high rates of turnover and it’s difficult and costly to train new agents. A survey of hundreds of contact center agents revealed that more than one-third said they don’t feel they’re set up for success and only 54% believe their leadership invests in their team.
“For many companies, addressing feedback after a customer has had a poor experience is not enough to salvage 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 more efficient and empathetic 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 during a live call, thanks to Qualtrics’ sophisticated natural language understanding capabilities. The coaching relies on the company’s XM Discover platform, which can analyze conversations in 23 languages and includes models tuned to the nuanced terminologies and subject matter relevant to different industries.
Based on customer and employee interactions, organizations can discern across a spectrum of over 50 emotions how a customer or employee felt during an interaction; identify what the customer or employee was trying to accomplish; understand how much difficulty a customer or employee encountered at any step of their journey; and detect the presence or absence of empathy in a conversation.
Real-Time Agent Assist uses these capabilities to analyze the real-time conversation alongside a contact’s past interactions — calls, chats and social media posts — as well as insights from millions of other interactions customers have had with the brand across all channels. With all that data, it can intelligently diagnose the caller’s individual needs and desires and make 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 related answers, helping the agent focus on listening to the customer and reducing the time it takes for an agent to resolve each customer’s issue,” Fabrice Martin, chief product officer for Qualtrics Customer Care told VentureBeat.
The solution can also recognize when it’s appropriate to deliver in-call reminders to the contact center agent, such as when they may be going off-script or out of compliance, when they should show more empathy for the customer, or when to offer a discount to an unhappy customer.
These same natural language understanding capabilities then enable Automated Call Summaries to deliver instant, accurate, automated call recaps covering all relevant details discussed during the call, including why the customer called, how the call went, whether the issue was resolved, how much effort was needed to reach that resolution and what steps still need to be taken. Qualtrics claims that customers have found these summaries more accurate than manual entries from their agents.
Context is key: Enter NLU and machine learning
“Most current agent-assist solutions rely only on call metadata, such as call time, duration or phone numbers, and are simply programmed to understand basic keywords or phrases,” said Martin. “While they may save an agent time finding a solution in some cases, they lack the broader context and sentiment intelligence necessary to provide truly valuable recommendations to agents during a call.”
Qualtrics claims that the XM Discover platform’s natural language understanding and machine learning capabilities have been fine-tuned over a decade. Its ability to identify the emotions and emotional intensity, intent, effort, and absence or presence of empathy as well as other contextual, brand-specific nuances takes the offering a step beyond other call center solutions.
“Because we can analyze those experience metrics together with the customer’s past interactions, as well as insights from millions of other interactions customers have had with the brand across all channels, we can more accurately diagnose the caller’s individual needs and desires and make recommendations specific to each business,” said Martin. “With Automated Call Summaries, all of these experience data points and actions are added to an automatically updated, unbiased, consistent historical record that plugs into any CRM [customer relationship management] or agent management system a company would like to use.”
The road to more automation
While CRM has been around in some form for decades, advances in technology like natural language understanding and customer data platforms are starting to bring more structure and usability to the river of data that billions of customer engagements create every day. “Every organization in this economy wants to increase its customer loyalty, and that is one of the driving forces behind the race toward real-time customer management,” said Martin. “Leading companies will move away from simply asking how they did towards orchestrating and personalizing the customer’s experience as it happens.”
“When you combine a solution like Real-Time Agent Assist with another solution we just recently announced, CrossXM, which gives leaders automated insights into key employee experience drivers that have a direct, statistically significant impact on customer outcomes,” Martin added, “our [users] can feel very confident in the actions to take that will drive the most value for both employees and customers.”