How to grow your chatbot into a full-blown conversational AI machine.

Photo credit: Volodymyr Hryshchenko

Making the case for domain-specific NLU models

Domain-specific chatbot NLU models could be the key to handling all of those lengthy rants from frustrated users

Compared to humans, chatbots are terrible listeners 🤦

Over time, we humans have become used to venting and going on long, pointless rants when we’re frustrated. For the most part, this was never an issue as there was always another human at the other end of the phone call or chat session that was ready and willing to weed through the meaningless details of these rants and determine what we actually need.

Arm your chatbot with conversation flows that actually get users to the answers they need

The ability to help users disambiguate poor utterances can make chatbots deadly compared to traditional user experiences

You’d be surprised how many chatbot users don’t know how to properly articulate their question 😲

If you’ve spent time scouring through conversation logs in Dashbot or Chatbase you’ve likely seen some frustrating, head-scratching conversations where your chatbot ultimately had the answer the user was looking for but never ended up serving it to the user.

Nailing the most important chatbot flow you’ll ever create

This is what happens when you have a poor chatbot fallback experience…

Unfortunately, you’re not psychic and neither is Dialogflow 🔮

Hopefully, at this point in your chatbot journey, you’ve come to the realization that trying to predict user utterances for a given intent is about as hard as predicting the weather in San Francisco. Note the ‘about as hard’ as I’m sure there’s a lot of San Franciscans out there that would love to debate me on that.

The days of a one-size-fits-all approach to chatbot design are over

Create a personalized user experience that takes your chatbot user engagement metrics to new heights

Let’s be real; no one wants to talk to a one-size-fits-all chatbot 😒

In the early days of the chatbot, we were all treated to the concept of a generic one-size-fits-all chatbot, and oh what a treat that was. It was a pleasant surprise if the chatbot could even refer to the user by their name let alone leverage any meaningful data on the user.

What a chatbot needs to be successful in 2019

Image result for chatbot

Disclaimer: This strategy isn’t for the faint of heart… 😲

If you’re looking for basic beginner-level chatbot UX design strategies, then I recommend you check out this article I wrote in 2018 on the ‘6 Chatbot UX Design ‘Must-haves’ for 2018.’ This piece you’re reading is an expansion on the chatbot UX design principles I covered last year and is for those looking to get their feet wet in advanced chatbot UX design concepts.

Safeguard your chatbot from one of the most prominent user errors

Thinking about using ‘Yes’ or ‘No’ chatbot buttons? Please STOP and read this article first…

Users will misuse your chatbot far more often than you expect 🤖

If you think most users will use your chatbot as expected this is clearly your first rodeo. Not only will user conversations surprise you but you will likely find yourself dumbfounded by many of them.

Winning over potential chatbot users is no easy task, but it can be done with the right mindset and approach

You expected chatbot user adoption to be easy, didn’t you? 😂

Let’s be honest. When building your chatbot, you were so thrilled with the user experience that you expected users to flock to it in droves once it went live. Now you’re three months in post-live and practically banging your head against the wall when analyzing the user data.

Creating successful outcomes by automating what matters.

Wait… What is the 80/20 rule when it comes to chatbots? 🤔

The 80/20 rule as it pertains to chatbots or support automation, in general, is the belief that 80% of your issues come from 20% of the potential root causes. This 20% of potential causes make up what we would typically refer to as frequently asked questions or FAQs for short.

Knowledge sharing fueled by AI

Casey Phillips

Product Lead, Conversational AI @ ADP. AI fanatic, tech enthusiast, and passionate product builder!

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