4 Top Tips for Implementing SDA in Contact Centers | Market Insights™
Top tips for both enterprises and service providers
Top tips for both enterprises and service providers
Service Delivery Automation (SDA) adoption can see positive results as quickly as 9-15 months for RDA and RPA and 18-24 months for chatbots
Organizations can leverage analytics and automation to deliver end-to-end workflow orchestration and resolution. As the chatbot learns, the focus will eventually shift from reactive resolution to proactive detection and resolution
The digital workplace construct is leading to an evolution in the enterprise chatbot landscape; future generation chatbots need to have simplistic, intuitive, and user-friendly design and features, and a robust functionality set that can address a wide variety of use cases.
The digital workplace construct is leading to an evolution in the enterprise chatbot landscape
Everest Group tracks several AI-based automation technologies
Enterprises and their contact center operations are increasingly adopting self-service technologies to improve the customer experience. Over the past several years, chatbots have become one of the most embraced. And it’s easy to see why.
First generation chatbots were software programs that could hold a natural conversation on a text-based channel such as chat, email, or social media. Modern day chatbots’ capabilities – interchangeably called virtual agents – have leapfrogged ahead through use of Artificial Intelligence (AI), machine learning, and Natural Language Processing (NLP), which allow them to understand free flowing language and generate real-time responses. They are no longer bound to a single channel, but instead cover the entire ambit of services, including voice support. This helps them fit well with enterprises’ broader omnichannel strategy, as they can deliver a seamless experience across channels.
Unlike earlier versions, today’s chatbots can even contextualize information in a conversation. Here’s an example:
Jim: I would like to know the status of my flight. It’s booking # PNR AZ12356.
Chatbot: Hi Jim, checking into it. It appears that your flight’s departure will be delayed by 4 hours, due to scheduling changes.
Jim: Can you cancel it and check for an earlier flight in the morning?
Chatbot: Confirming…
As per your request, I have cancelled your flight American Airlines 235 from Boston to New York. Based on your past bookings, I have three suggestions for alternative flights on the same day:
Jim: Book one ticket in the second option.
Chatbot: Confirming availability. Based on your preferences, would you like me to book an aisle seat, and select a bruschetta omelet with coffee for breakfast?
Jim: Yes, that will be great.
Chatbot: Excellent. Your flight Delta 2471 from BOS to JFK will depart at 0600 hours on October 30, 2017. Your seat number is 4C. I have sent a copy of the ticket to your personal email id. Have a safe flight.
Notice how the chatbot contextualized the information based on unstructured and more natural language flow, and offered recommendations based on the user’s past preferences. These degrees of evolution have made chatbots much more self-service capable, and are significantly enhancing the experience that contact centers deliver to their client’s customers.
The end goal for today’s enterprises is to deliver the best possible omnichannel customer experience. Chatbots can help customers solve problems on their preferred channel of communication (voice and non-voice). However, the technology does have shortcomings. The well-known example of Microsoft’s Tay – a Twitter-based intelligent bot that had to be pulled down within 16 hours of deployment due to offensive tweets – highlights one technology gap that needs to be addressed.
Everest Group’s just released viewpoint entitled, “Chatbots Delivering Enhanced Customer Experience: It’s Easy to Get It Wrong” details how chatbots can fit in enterprises’ omnichannel strategy, the risks they need to be aware of, and how they can mitigate them.
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