According to Gartner, around 70% of customer service interactions this year will happen via automated tools. That’s up from just 15% only three years ago.
Chatbots and other automated customer service tools, like Interactive Voice Response (IVR), promise to help resource-constrained service teams do more with less. The potential cost savings are compelling as well. Chatbots could help the retail, banking, and healthcare sectors save as much as $11 billion by 2023, according to a recent estimate.
For customers, however, the value proposition is more nuanced. Qualtrics recently found that, while customers are comfortable using chatbots for simple requests, they want to be able to talk to humans when needed.
A chatbot often is the first tool customers use to reach out for help, and, as a result, need to work seamlessly to leave a positive first impression. As organizations deepen their investment in automation, they need to focus on addressing every type of customer need, leveraging virtual tools to improve both the agent and customer experience.
Building a Better Chatbot
For customers, chatbots can promise speed and convenience. Rather than reaching out to customer service during business hours (and likely being placed on hold), customers can get help at times that work best for them.
Chatbots, however, aren't the perfect solution for every type of ask. One of the key scenarios where customers can get frustrated with chatbots is when an issue is too complex for the bot to resolve and the chatbot fails to quickly escalate to a human agent.
Innovators are working to improve chatbots through a combination of natural language understanding (NLU) and machine learning. NLU can help chatbots better identify user intent and understand customer requests, while machine learning can help chatbots learn from past customer interactions and grow more intelligent over time.
As chatbots improve and continue to take on a more central role in customer service operations, human service agents will always be needed for certain tasks and for certain customers. The same technologies that improve chatbots can also be used to help organizations refine the process of determining which jobs require the nuance of an agent, which jobs can be addressed by machines, and which jobs require the combined efforts of both.
Connecting the Dots Between Human and Machine
Just as we can improve chatbots' underlying smarts, we can also improve their handoff with human support agents. For example, it's common for customers to experience situations where they're asked to provide the same details twice -- once to a chatbot, and again to a support agent when their request is escalated. This is inefficient and frustrating for customers, and frankly unnecessary.
AI-powered workflows that automate the process of sharing relevant customer and case information are critical to ensuring customer experiences are seamless and efficient. As chatbots become more intelligent, live agents can lean on them as they juggle multiple conversations at once. They can transfer control to the AI-powered chatbot when an issue is better solved via self-service, like when a customer needs to book an appointment or change their payment option, speeding resolution times and minimizing the burden on live agents. If customer service is truly interconnected, it also offers new opportunities for self-service, and teams can integrate more actions within a chatbot UI, increasing the potential for first contact resolutions.
The customer experience doesn't end when a customer closes the chat window. It ends when their question or problem is resolved. So, while enabling customers to engage anytime on a vast array of channels is important, it's not enough. What happens behind the scenes, across the middle and back offices to ultimately resolve the issue, has a huge impact on how the customer experiences the brand.
Creating Seamless Experiences
Ultimately, the true potential of AI-powered customer service is its ability to guarantee that every communication a brand has with a customer is aligned and transitions smoothly to proactively solve customer requests. AI-powered chatbots can be used, both in customer-facing and in internal roles, to help move teams from service to action, simplifying processes for agents and providing a better overall experience for customers. The same technology that powers smart chatbots can also be used outside of text Q&A to dynamically offer human agents information and resources while they’re on the phone with a customer, or to improve voice experiences for customers directly.
Painless, positive experiences are critical to creating loyal, happy customers – a core goal of any organization – and the smart integration of chatbots is one of the keys to unlocking them.