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Every year, businesses spend over $1 trillion on customer service calls. But AI and natural language processing solutions are changing the game, says Dr. Daniela Braga, CEO of DefinedCrowd. AI-powered virtual agents and chatbots not only help businesses slash the call center costs, but dramatically improve customer relationships as well.
That’s why investment in contact center AI is growing, Braga explains.
According to a Deloitte study on the future of customer service, 56% of companies are investing in conversational AI technology to improve cross-channel experiences. Increasingly, customers aren’t interested in spending time on the phone. When they need support, they turn to their computers or mobile devices, preferring to look for support through self-service databases or through messaging customer service agents.
“AI does require some investment, but these conversational solutions can reduce customer service costs by up to 30%” Braga says. “Clients can see a return on their investment in three years.”
By 2022, chatbots and NLP will save companies about $8 billion per year in customer supporting costs. For every second chatbots can shave off average call center handling times, call centers can save as much as $1 million in annual customer service costs.
Significantly reducing wait time
One of the biggest challenges of customer service centers is the volume of calls they receive — and the difficulty has been magnified in the face of staff reductions during the pandemic. Customers often wait in long queues to have even basic requests answered, or might never get an answer at all, Braga explains.
A huge number of these customer support calls are for routine requests, from password resets to questions about interest rates, that can be resolved quickly. When virtual assistants take on these calls, more complicated requests and questions can be shuttled to the human agents whose time and attention has been freed up.
“Digital assistants can resolve 80% of frequently asked questions, even before you get to a human, without having to wait until the bank opens,” she says. “Virtual assistants give people an answer on the spot, which is already a huge improvement in the customer experience.”
Augmenting human agents
Virtual assistants aren’t taking over, however, Braga says.
If a question too complex to be answered by the AI, it can send the customer to the right place quickly, and offer the customer service agent the information they need to help the customer efficiently. Instead of starting all over at zero again, having to re-explain a problem, the virtual assistant can pass along the data it has gathered, speeding up the interaction with the customer and making the experience faster and more pleasant overall.
“I see it more as an augmentation of humans rather than a replacement,” Braga explains.
When human agents have the time to provide better service, and are handling more interesting questions, it also helps make the job experience far more engaging — a tremendous advantage for an industry in which attrition numbers are so high, in part because of the extremely repetitive nature of the routine calls that can make up the bulk of a shift.
Obviously, unlike live agents, virtual assistants don’t need mandatory breaks, or take vacations or holidays, so augmenting live agents with virtual ones helps eliminate downtime for your customer service operation. But virtual agents can also be used as a business continuity strategy, stepping in when live agent hours are impacted by natural disasters — or helping businesses operate even during ongoing global issues, like the pandemic, where protecting the lives of employees is the primary importance.
To learn more about how your company can leverage AI, NLP, and voice technologies to dramatically improve customer service, boost efficiency, and improve your bottom line, don’t miss this VB Live event.
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Register here for free.
- Understand the different types of AI initiatives a company can launch to improve CX based on NLP and Voice technologies
- Know how to develop those AI initiatives and the role of data on training AI/ML models
- Get to know a case study from a major FI company (Mastercard)
- Dr. Steve Flinter, VP of Artificial Intelligence & Machine Learning, Mastercard Lab
- Dr. Daniela Braga, CEO, DefinedCrowd
- Hari Sivaraman, Head of AI Content Strategy, VentureBeat (moderator)