Customers’ expectations are shifting – they want authentic and personalised communication experiences with brands. The problem? Current volumes of incoming communication make it hard to stay intimate with each customer. Chatbots have become a ‘balancing act’ towards delivering more personalisation without large budget increases. And they have already won over younger users – 69% prefer this medium for instant communication.
Businesses that were quick to embrace chatbots are now seeing significant pay-offs in terms of employee productivity, lower administrative costs and higher revenues. A pilot chatbot tested by one telecom company managed to independently resolve 82% of common customer queries, rising to 88% when combined with help from live agents. KLM airlines deals with 50% of customer queries on social media with the help of AI assistants.
Chatbots also shorten the consumer path to purchase. What’s more, millennial consumers are ready to spend up to £481.15 with a brand exclusively via chatbot.
Whether you plan to develop a chatbot to facilitate the sales process, assist with customer support chores or provide important product updates, you will need to pay special attention to your content strategy. Content design for conversational UIs abides by slightly different ‘rules’ than you may be used to.
Chatbots pose a new challenge for designers – the medium’s interface lacks visual cues for interactions (the familiar tabs, arrows and buttons). Instead, all these elements need to be communicated by words to guide the user towards a desirable action – something 58% of UK consumers deem important when communicating with a brand.
The first step to designing a truly helpful and clever AI ‘conversationalist’ is to establish the primary reason why a customer will interact with your chatbot.
Ubiscent survey of UK consumers indicates the additional preferable use cases for chatbots:
A great chatbot is able to cope with one or two of such tasks at a time. Teaching it to handle everything at once may initially result in frustrating experiences for users.
The first step towards designing information architecture (IA) for a chatbot is scheduling a content audit first to organise and label all the existing information you already have.
The second step is building a content model – a rough outline of the possible conversation and information that your chatbot will deliver to users. For instance, if you are creating a chatbot that would provide users with hotel recommendations, you will need to specifically look for:
Afterwards, you will need to determine the correlation between those qualities/attributes and how those interrelations can be used to compile a hotel recommendation. The task may seem rather daunting and granular, so here’s a quick tip – you can use data visualisation to help you identify all the relations between different informational cues.
Your third step is to work out an efficient navigation. It should be developed in line with the main use case for your chatbot. Start with designing the default menu – the top-level point of your entire hierarchy.
A hierarchy determines how your chatbot can call the needed information. There are a few hierarchy types worth experimenting with:
The final step is to create a content taxonomy that will help your chatbot identify and retrieve necessary information for the user. Basically, it’s the information about your information.
Your chatbot can be compared to a search engine crawler – it needs some cues, such as metadata, to render the page content. And so does your chatbot. Creating a taxonomy means that you label and organise all the information available around certain attributes and parameters.
For example, an interaction with a hotel recommendation bot may go the following way:
User: What other hotels are available in Paris?
Bot: There are 150 other available properties in Paris for your dates. Would you like to review all of them or shall I narrow them down by [attribute] e.g. location, price, rating, stars?
The chatbot will then rely on your taxonomy to obtain the requested information. Designing proper IA is a test and trial approach – start with operationalising a small part of your content and add new conversational layers later on.
Consumers may be excited by this new technology – 55% of millennials claim that interacting with a chatbot has positively improved their perception of a brand. Yet some 60% of consumers are wary that chatbots will likely end up providing the same frustration that traditional self-service/IVR options do.
As a business, you will need to prove those naysayers wrong. Teaching your bot to deliver accurate information is one part of the equation. You will also need to train it to be a friendly and user-centric conversationalist. The travel industry already has some striking examples you may want to check out for inspiration.
Seamless and effective communication is underpinned by the assumption that cooperation between the conversational participants takes place.
The Cooperation Principle is based on four rules:
Thus, when interacting with chatbots users tend to exhibit the following behaviours:
Users often rely on chatbots to receive instantaneous information. Most will be rather specific with their preferences. For instance, Tacobot is smart enough to capture user preferences (no lettuce) instantly, instead of asking them to choose between a series of standard fillings. Cooperative users may get frustrated when asked to repeat the same information again, so keep that in mind.
Some users tend to provide extensive details – more than your chatbot may need to execute a command. Create dialogues that steer users toward the right direction. Don’t overwhelm them with an instant choice and ask them to take one specific action at a time.
On the other hand, users may not always deliver commands in the acceptable format. You will need to develop casual and fun dialogues asking them to provide the information in a more acceptable fashion without having your chatbot appear too ‘dumb’. Look how Poncho, the weather bot, handles similar matters:
Make it easy for users to cooperate with you by adding special cues and anticipating their actions. Don’t go too creative though, as 47% of consumers will choose a chatbot that solves a problem over a chatbot with a great personality.
Opt for plain and fun language, similar to your everyday speech. Avoid obscure word choices at all costs as they will make your chatbot sound like … a bot, which isn’t a good thing.
Going back to the previous example, Poncho simply uses “Excuse me?” phrases when asking the user to rephrase her question instead of going robo-like with “Your command is invalid. Try again”. Sense the difference?
Here are some essential rules to develop lightweight and engaging conversation copy:
Split test different dialogue options and run pilots with your team or a group of beta users before sending your chatbot into the field. Learn what types of interactions work best with your customers and help you achieve your business goals at the same time. Most importantly, keep your interactions fun and casual as chatbots are all about delivering better customer experience, rather then trying to solve every business problem you have.
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Why conversational UIs mean nothing without personalisation
Categories: Conversational UI, PPC, SEO
Categories: Conversational UI, Travel
Categories: Martech, PPC, SEO
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