Customer Experience, Emotional Intelligence, Technology, Work

Rapportboost.AI Interview Part 2: Chat With Emotional Intelligence

Here’s part 2 of my interview with Rapportboost.AI – you can access part 1 here.

This time, we’re taking on the topic of emotional intelligence and discussing how technology can help in delivering emotionally intelligent customer interactions.

As Augmented Intelligence transforms the workforce, emotional understanding is proving to be an essential component of brands’ interactions with their customers. After discussing live chat agent training in Part One of our Interview Series, we asked Kaye Chapman, Customer Experience and Training Specialist at Comm100, to shed light on the best strategies for chatting with emotional intelligence.

RB.AI: Let’s talk about emotional intelligence. This concept is of special importance to us at RapportBoost.AI because we help brands use high-EQ to achieve increased revenues and conversions when using chat for sales. What are some measures you’ve found to be effective for chatting with a certain level of emotional intelligence?

KC: Regardless of use case, emotional intelligence is helpful for agents across the board, given that EI is such a cornerstone of effective personalization. It’s important for chat agents to be able to pick up on specific language and to understand how they need to adapt their behavior to suit that particular customer. When I’m thinking about how to promote communication in an emotionally intelligent way, one of the things I encourage agents to do is take full advantage of the canned message library and save those phrases that have been especially impactful. Make sure that there is a constant cycle of trying things, evaluating them for effectiveness, and using them in the future if they have been successful.

RB.AI: I think we are all excitedly watching conversational commerce take off and that has to do with so many things, such as making purchases through cell phones and the fact that millennials like to chat. For these reasons, do you think sales and chat will converge in the near future?

KC: I think sales and chat are already converging, and it’s exciting to see organizations using tactics like Account Based Chat to engage with key prospects – we liken it to “rolling out the red carpet” for customers by providing them with experiences that are deeply personalized. I do think conversational commerce is going to get bigger and it is going to be the thing that separates out the wheat from the chaff in terms of who does well in the future and who doesn’t.

RB.AI: Agreed. Along those lines, could you talk a little bit about channel optimization and some strategies you’ve used to optimize live chat?

KC: We advocate effective journey mapping to understand what types of customers are coming into which particular channels. We also encourage our clients to think about the concept of channel blending or channel pairing when they are planning different channel mixes, what channels they engage on, and how they can move customers from one channel to another. The idea behind channel blending or pairing is rather than thinking about channels in isolation, you can think about how to use the best aspects of different channels to form a great experience for your customer. For example, chat is fantastic for helping people with urgent issues in a synchronous way. But you can also enable knowledge base integration to live within the chat window, and clients can configure it so that customers go through the knowledge base before they hit an actual live chat agent. Now, that’s fantastic because, from a customer’s perspective, it might not be so easy to find out where a knowledge base is on a client’s website, it might be that your customer’s in a little bit of a rush, it is just that rush aspect that makes it easier for them to speak with an agent. Actually positioning the knowledge base within the chat window gives customers more choice to select a channel that suits them, and obviously, there are big bonuses there for clients as well in terms of deflecting unnecessary query types from chat.

Originally published here.

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