AI, Chatbots and Improving Customer Satisfaction

Contact centers are not alone in trying to keep up with fast-changing technology, but they face two distinct challenges that are highly related. First would be various constraints that limit their ability to provide agents with the tools needed to effectively support today’s tech-savvy customers. This challenge was addressed in my previous blog post, which in turn supports a deeper analysis that you can access here.

The second challenge pertains to the flip side of the relationship, namely customer experience – CX – and how that impacts their satisfaction, not just with a particular contact center interaction, but with your overall brand. Technology isn’t the only factor impacting customer satisfaction, but as the gap between what customers expect and what the contact center can provide grows wider, those satisfaction levels are bound to decline.

AI Basics for Improving Customer Satisfaction

Many forms of technology can be leveraged to improve customer satisfaction, but Artificial Intelligence (AI) should be near the top of your list, not only because the capabilities can be very powerful, but also because it’s accessible even for contact centers with limited resources. However, as AI is both complex and new for the contact center, it is helpful to review some AI basics.

In terms of impacting customer satisfaction, it must be understood how two AI building blocks perform different sets of tasks. First is Natural Language Processing (NLP), which enables natural-sounding communication between customers and AI applications such as chatbots. Once chatbots can demonstrate a high level of understanding what is being said, various forms of customer service can be provided in a more effective manner.

The second AI building block is Machine Learning (ML), which leverages the computational power of the cloud to continuously improve over time. Every customer interaction provides new data sets that help fine-tune ML algorithms and improves the accuracy of chatbots to enhance CX. This can take many forms, including both reactive and proactive forms of service, and is very much in line with the expectations of today’s customers.

Two Chatbot Use Cases to Consider

Building on this, here are two compelling use cases where chatbots can improve customer satisfaction. AI may still represent an emerging set of technologies, but even a small-scale deployment can go a long way to closing the gap with customers and providing a consistently better CX.

The first would be providing more personalized customer service for every inquiry – a nearly impossible task with legacy technology. Impersonal service has long been a key reason why the customer satisfaction bar remains low, but AI building blocks as outlined above are tailor-made to provide that personalized customer experience.

Secondly, chatbots are poised to play a key role in driving better online buying experiences. If you think holistically, the contact center isn’t the only touchpoint shaping customer satisfaction, and increasingly, sales and marketing operations are turning to AI for the same reason. With e-commerce becoming central for top-line growth, businesses need to make the buying process as easy as possible, and chatbots can help do that in a myriad of ways.

Strategic Insights Series Continues

This blog post provides a high-level preview of my second Strategic Insight for Upstream Works. As noted above, the first Strategic Insight addressed how chatbots can improve agent performance, and that represents the starting point in your journey for adopting AI and chatbots. Following that, applications must be considered for your customers, and that brings us to the second Strategic Insight, which you can download here. I’ll complete this series with a third Strategic Insight focused on business outcomes and best practices for deploying chatbots, so watch for that here in the coming weeks.

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