Chatbots have become a really popular way for companies to interact with customers. Most chatbot experiences, though, are pretty static. As a developer, you need to set up a messaging flow with content that doesn’t change much based on user interaction.
Some of our users have discovered that you can use Fuzzy.ai along with most chatbot frameworks to create a much more personalized experience for your customers. Some examples include offering dynamic promotions based on user behavior, and recommending the most appropriate products.
To show this in action, we’ve built a sample bot that we call the Fuzzy.ai Promobot. You can chat with Promobot on Facebook Messenger and based on how you answer the questions it asks, Promobot will offer you a personalized discount on your Fuzzy.ai subscription. Promobot is built using Howdy.ai’s Botkit toolkit.
Here’s a sample chat with Promobot:
Behind the scenes, Fuzzy.ai is taking the data from the chat responses to predict the discount most likely to result in a purchase. To get started, the Fuzzy.ai agent powering the discount decision is a pretty simple one. It’s based on these 5 rules:
IF hasAccount IS no THEN discount IS high
IF hasAccount IS yes THEN discount IS low
IF tutorial IS no THEN discount IS high
IF tutorial IS yes THEN discount IS low
lastAPICall INCREASES discount
And the discount that Promobot offers in our example ranges from 0% to 20% based on the user’s responses.
Once Promobot offers the user a discount code, it then uses Fuzzy.ai’s machine learning to automatically optimize its rules to offer the lowest effective discount possible.
In our example, Promobot does this by asking the user if they plan on using the discount code and then providing that feedback to Fuzzy.ai. In real implementations, you might send this feedback to Fuzzy.ai once the user actually makes a purchase.
The feedback Promobot provides is the % of full price paid by the customer. So, if Promobot offers a 15% discount and the customer says they plan to use it, Promobot sends “85” (100% minus 15%) to Fuzzy.ai. If the customer says they don’t plan to use it, then we consider it a lost sale and Promobot sends “0” (zero) to Fuzzy.ai.
Fuzzy.ai’s machine learning algorithms are seeking to maximize this value, and will automatically optimize the rules to get the highest possible result.
This is a simple example, but a more complex one could include dozens of questions and external data to create a truly personalized experience.
Please feel free to chat with Promobot on Facebook Messenger and check out the project’s code on GitHub to fork it for your own bots.