After helping a number of different organisations design chatbots — we’ve learnt a thing or two!
Here are our top tips for creating a great human-centred chatbot.
Just as we try to hire “T-shaped” employees — we’ve found it’s best to create T-shaped chatbots. The T-shaped metaphor is used to describe the skills and abilities of a person within a team. The vertical bar representing depth of knowledge and experience within a specific topic, and the horizontal bar representing the ability to collaborate and work with people in other areas by having a light understanding of their topics/disciplines.
Why do this? Because you have no control over what your customers or employees will chat about it’s best to start off by lightly covering #allthethings — before giving the details on anything specific. And you don’t want to chat to a bot that can only talk about 1 thing, or a bot that only lightly covers everything #surfacelevel.
Some of our clients are starting to expand their T-shaped chatbots to become S, or N, or M-shaped! Meaning they are now able to have in-depth conversations about multiple topics and also interlink between these topics!
First impressions are so important. You want your customers first chat to be great — or they might not chat again. So by understanding entire customer experience first you can find out about all the topics you need to lightly touch, and then decide on which topic(s) you want to go deep with — and flesh out a detailed, helpful and meaningful conversation for.
This one has been quite controversial. When a chatbot says that it’s a chatbot — it’s almost going against the turing test purpose of trying to trick a human into thinking a machine is a human. But the people have spoken, and we’ve spoken to a lot of you about this — people of all ages and backgrounds, and we all prefer honesty, and transparency — we want to know what we’re talking to. So even though our chatbots can chat like a human using the latest artificial intelligence — we still want to know what it is.
For example — Charlie (Tower Insurance) and Alan (Squirrel Mortgages) both state that they are Bots — but quickly move on into natural conversation.
This tip is about balancing how you use AI in your chatbot. Looking at which type of conversations work with ‘more AI’ vs scripting. Scripting enables the business/chatbot to lead the conversation, but NLP (Natural Language Processing) enables the chatter/customer to lead the conversation.
1. Conversational AI: Code/No Code
2. Chatbots 2.0: Simplifying Customer Service with RPA and AI
At Ambit, we simply conversations into 3 different types derived from an end to end customer experience — Acquisition and Retention, encompassed by Help/support at any point in the experience. We got to these simplified conversation types by looking at trends across all our clients conversational ecosystems– finding there’s common customer/employee needs in every organisation that fit into these 3 common buckets.
It won’t come as much of a surprise as to who should lead the acquisition conversations — the customer. One of Dale Carnegie’s top tips in his famous book “how to win friends and influence people” is to never talk about what you want — always find out about what others want — then talk about how what you have to offer could give them just that.
But once you have customers — when they are trying to get tasks done with the things they already have and use from your organisation — this is when you (the chatbot/business) can lead the conversation. You can lead your customer to quickly complete tasks and make the most out of your products and services.
Help and Support is more of a dance. Start by listening to the customer (using NLP/AI) to identify the issue, then you can lead the chatter to a resolution (using scripting). And sometimes it will be a lot more back and forth — as you troubleshoot to find out the exact issue and best resolution.
Humans don’t just ask halfway through a convo, “hey, how’s this chat going?”. (Although maybe we should then we might be better listeners…)
This tip is about qualitative feedback. Hearing exactly what your customers think and want is like being given a get out of jail free card for bad customer experiences. But when do you ask for feedback? At the end of every topic? After each question has been answered? Do you just wait for people to give it? Or do you ask every second person??
Where is it most useful to gather feedback? We don’t have a one-size-fits-all answer for this, but we do know it’s important to pick your time carefully. Don’t interrupt the conversation flow and risk losing your chatter by asking for feedback, look carefully for useful places to ask and monitor your analytics to see if this affects your drop-offs.
An idea for new chatbots — when your chatbot is new and still learning more about what your customers want to talk about it will have questions and tasks that it simply cannot do — so instead of just saying “I’m sorry I don’t understand” — this is a great place to ask for feedback.
The joy a conversational user experience is that it’s informal — so use it — it’s a conversation! Have as much fun as you can (within your brand guidelines and values of course ).
Ambit creates intelligent chatbots powered by Conversational AI to engage customers, streamline business processes and automate customer service at scale. Find out more about us here.
