New trends in customer engagement: Q&A with eGain’s Anand Subramaniam
The world of customer service and contact center interactions is changing. Customers increasingly expect greater speed, a high degree of accuracy, and more personalization. And, they are not alone. Contact center agents also have new expectations as well.
Recently, Anand Subramaniam, SVP-Global Marketing, eGain Corp., reflected on evolving requirements for successful customer interactions and the importance of leveraging AI, natural language processing, and machine learning to address the needs of today’s digitally-savvy consumers.
What is the role of AI and knowledge management in customer service and contact center interactions?
Anand Subramaniam: Customers interact with businesses to find answers, get their problems resolved, and receive advice. Knowledge management is foundational to providing them a great experience at the moments of interaction. When infused with AI technologies such as natural language processing, machine learning, and reasoning, and implemented with best practices, knowledge management can truly transform the experiences of both customers and customer-facing employees alike.
How does it help customers and employees?
AS: From the customer’s perspective, imagine having to look at an old AAA map to go from place A to place B. Some of us may have done it in the past but I don’t think anyone would have wanted to do it if we had a GPS in our car. Many of today’s drivers may have never even seen such maps and probably cannot imagine driving a car without the step-by-step guidance provided by a GPS. This analogy applies to customer service as well—today’s digital customers don’t like to read and interpret documents to find the answer.
And, what about employees?
AS: For the contact center agents, as self-service continues to get smarter, their lives have gotten more difficult since they need to contend with more complex customer queries. Today’s customer service agents, most of them in the same generational cohort as consumers—Millennials and Gen Z—hate going through documentation or sitting in training sessions, nor do they like to retain [work-related] information in their heads, something that their older peers might have been OK with doing. Similar to consumers, they expect GPS-style guidance with their desktop tools to resolve issues when the customer is on the line.
AI-infused knowledge is, in fact, the GPS for customer service, both from the customer’s and the agent’s perspective. It can guide them to answers or through a problem resolution or advisory process.
Can you provide some real-world examples?
AS: A tax preparation client of ours is using a potent combination of our next-gen digital engagement, AI, and knowledge management technologies, fronted by virtual assistance, to power their new tax assistance service. Our virtual assistant converses with the tax payer and answers questions, and when needed, escalates to an adviser using our agent desktop solution with all the context intact. It’s a great experience for customers, tax advisers, and our client’s business operations alike.
One of our major telco clients also uses our AI-infused knowledge to guide 10,000 agents in the contact center and associates in 600 retail stores to answers. They are seeing a 37% improvement in first-contact resolution, a 30-point improvement in Net Promoter Score, and a 50% improvement in agent speed to competency.
And, in another case, a fast-growing SaaS provider is seeing a 67% improvement in time to answer and a 62% improvement in consistency of answers with our AI knowledge solution.
Does AI pose any risk in these customer service settings? How do you reduce the risk of adopting AI?
AS: It depends on the use cases. No one AI-technology hammer works for all business-need nails. For instance, while it’s better to use machine learning for use cases with low business risk (making contextual promotional offers on an ecommerce site, for example), you are better off using supervised or curated learning when the risk for the business or the customer is high such as for management of high-value assets or a life-and-death treatment decision for a patient.
What are some of the best practices for success?
AS: First, we recommend starting small with a focused set of use cases rather than trying to boil the ocean. Second, pick the right AI technology, based on what is best for the use cases. Third, go with a vendor who has a proven track record of success and best practice expertise in this domain—you don’t want a vendor to learn AI at your expense. Fourth, mitigate risk.
How do you mitigate risk?
AS: We offer a program called “Innovation in 30 days,” which is a no-charge, risk-free, production pilot that even comes with best-practice guidance from an AI expert for success. Many of our clients have taken advantage of this program to get their feet wet with AI in a risk-free way. Finally, avoid knowledge and AI silos. Go with a customer engagement application suite that is built on a unified, omnichannel platform for AI, knowledge, and analytics. One of our clients had four disparate knowledge silos, which reduced agent confidence in answers, as usage plummeted down to 10%. They consolidated them all into our knowledge management system, which has become truly mission-critical. In fact, agents are not even allowed to handle customer questions without it.
Do you see the related technologies of AI, natural language processing, and machine learning becoming more ubiquitous in customer service and contact center settings?
AS: Yes, given the transformational value that they can add to the contact center. Moreover, risk-free consumption models such as our “Innovation in 30 days” can help spur more adoption.
Where should companies avoid it?
AS: It is not a good idea to get into AI and machine learning if you don’t have high-quality customer interaction data and customer service process knowhow. Moreover, it is important to choose the right AI technology for the right use case to reduce risk.
Looking ahead, in 5 years, how do you see the customer service/contact center businesses changing?
AS: There will be more digital adoption among consumers. We see more adoption of digital channels among consumers. The younger generation will be born digital, of course, but even the older generation will use digital channels more thanks to ease of use and the ubiquity of smartphones.
There will be a digitalization of the agent’s desktop. To keep up with the digital consumer and the digital agent, contact centers will jettison phone-centric desktops and adopt next-gen digital-first desktops with rich capabilities to engage through digital channels such as messaging, chat, and social interaction.
AS: There will also be more AI adoption. Consumer adoption of virtual assistants will increase as they become smarter. As self-service systems get smarter, agents will only get harder questions. They need to become expert advisers. Given that next-gen agents do not like to sit in training classes, preferring instead to “learn in their jobs,” conversational guidance or augmentation tools for the agent desktop, powered by AI technologies such as reasoning, will become essential.
And, there will be more personalized augmentation. Even with GPS, drivers want personalized navigation instructions—highways/no highways, no tolls, and so on. Similarly, contact center agents will want personalized augmentation. Contact centers will personalize conversational guidance, based on the agent's preference, experience, and performance, the customer context, and also compliance requirements.
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