Last night, I attended an event in London about how to approach building a bot. It was really well attended and the speakers were very engaging, but one question seemed to stop them in their tracks.

“What’s the point in chat bots? It seems more complicated than clicking through a website?”

To be met by this question from someone that, by self proclamation, was not ‘involved in the bot world’, was quite refreshing. It reminded me how we cannot ever over estimate the adoption rate by consumers when implementing a technology as potentially influential as chatbots for the messaging revolution.

Whilst I think that often, we can overlook how long the adoption curve could take when implementing new technologies, I don’t feel this is the case with chatbots. If there is an extended adoption curve, then quite simply, we haven’t done our jobs right.

So what do I mean by that?

I am not suggesting there will be no adoption curve whatsoever — of course there will need to be a shift in mindset. Even if you are introducing a bot to an established audience, you need to be careful that they are aware of the interaction changes and you are not confusing them by altering their current process too much. By alerting them to the changes and ensuring they are familiar with the channel, you will avoid this. I definitely think that the better the bot, the smaller the adoption curve required.

Firstly, bots are being implemented through messaging channels, and currently the most popular is Facebook Messenger. The point of having a message bot is that, with the astronomic rise of consumers using messaging platforms in their personal lives, businesses need to capitalise on this trend. So there should be no need for an adoption curve here as the channels are already being used in every day lives. Make sure that the audience you are targeting are receptive and familiar to the channel you pick, at the same time as being the right channel for the problem you are solving.

Secondly, the problems that we are solving through bots, from the end users perspective, are problems they are already experiencing and are used to reaching out to businesses about eg FAQs, support enquiries, delivery enquiries etc. So the adoption curve to move from clicking on to a website, to sending a message should be minimal. The queries are not changing, just the method of submitting them. Which as I have already covered, should not be a complex shift. Bots also have a distinct advantage over websites as they are context aware. Bots can learn, and they can be programmed not to answer questions they already know the answer to. When you click through a website, you have to continually enter information that you most likely have done at some point before. This personalization can mean that there can be less workload for the bot and a better user experience.

Lastly, if the bots have been built and trained to a high enough standard, the end user experience should be seamless and there should be almost no differentiation between the bot and a human. This is slightly trickier as this requires the bot being able to understand the non-linear conversational style of a human. Which is why you need to choose a bot-building platform that can easily handle the intuitive flow of a conversation. This is absolutely the bot builders responsibility but if they get this right, there should be no barrier to adoption.

So, if we are implementing bots through messaging platforms that end users are already used to, and solving problems that they are already used to having and the bots have been built and trained enough for a great user experience, then there shouldn’t need to be a difficult learning curve for users to use them.

As Converse.AI is a bot-building platform, sometimes we can be head down in thinking how to make the platform the most intuitive and easy user experience possible, even for those that have never coded.

But in order to ensure that our customers are as successful as possible, we also need to consider the end result and how the users will react to each bot. For example, it was exactly for this reason that when we set out to tackle the bot building problem 18 months ago, we built our own NLP stack.

Why do this? It sounds a bit crazy!! Well there were others that we tried to use initially but none of them quite had the functionality and agility that we wanted from them.

So how do we build a bot that has little to no requirement for the end user to learn how to interact with it, has a seamless user experience, and is an easier process for them that logging on to a website and clicking through questions or search options?

The secret to this is formed of a few requirements:

1) Build a bot with the right channel for your use case. This can be just rich media, or it can mean sophisticated NLP but the key is to make sure that the use case, the channel and the audience are all in synch. The more intelligent the bot, the more it can interact like a human and ensure the user doesn’t require training.

2) Build the bot to be able to use either one channel or multiple channels that your audience is used to using eg Facebook Messenger, or perhaps eventually we will have the holy grail of Whatsapp!

3) Make sure you listen to your early adopter feedback and adapt the bot in ways they suggest

4) The hardest bit…is to build a bot that can cope with the non-linear nature of a human conversation. So build your bot on a platform that can cope with moving between intents and states easily.

Those are my tips on how to build a bot that requires little to no user adoption. In order for it to become second nature for someone to send a text message to their bank, or find a holiday using Facebook Messenger, then the interaction points need to be as seamless and intuitive as possible. Going back to my original point, right now it may well seem easier to click through a website than talk to a bot, but that will change very rapidly the more time and effort is put in to creating bots that act like an employee of a company or maybe even a friend.

Thanks for reading! Please recommend this if you liked it or leave a comment. Keep following the posts on here and stay in touch via @converse_ai on Twitter. http://www.converse.ai

Converse.AI — enabling customer engagement through conversation.