Implementing a chatbot project can be a long and complex journey if you don't know how to do it. There are many risks associated with automating customer caps, and ultimately the reputation of the organization is at stake, as the chatbot becomes the welcome mat for customers to create that first impression that makes up the majority of what they will think forever. It's also easy for a chatbots project to drag on, costing the shirt.
To avoid these pitfalls, here's a guide to creating the best conditions for an efficient and well-executed chatbots project:
Step 1: Choose a supplier who works WITH you
There are different types of chatbots services out there. Some providers offer DIY platforms where you set up your own questions and answers in a user-friendly way. The challenge with this is that it's hard to know exactly how to structure your query trees and how to navigate customers when you have no experience with automated conversation. The result is often a stylized experience. The responsibility for training the bot is often entirely placed on the customer, which in our experience sometimes leads to the bot being mistrained. And this in turn creates a jammy experience for the customer. Stilty and jammy, then— no further, in other words.
It is important that the supplier contributes with its expertise from the beginning. Comes up with suggestions on how to structure questions & answers. What the work should look like before and after the bot is completed. It should be possible to pitch ideas and be challenged.
In a nutshell: don't think of a chatbot company as a service provider, think of it more as a partner.
Step 2: Start easily and set a clear scope
It's easy to think that "now that we're going to have chatbot, we're going to connect every system we have and our bot should be super-duper-mega-bronto-smart and respond to everything completely automated". Yes, absolutely, but over time. It can be costly both in time and money to invest in cool integrations that customers then do not use. In addition, these efforts, made prematurely, can remove the focus from what is most important to get in place first: the language comprehension.
The tip here is therefore to set a simple, clear scope for what the bot should be able to do in the first place. A chatbot goes a long way without integrations. With a focus on language comprehension and solving customers' problems more easily, we often see that our customers manage to automate around 60% of all incoming queries without integrations.
Step 3: Test internally
When the contours of your chatbot start to become visible, it's time to try it out before you go live. Here, the whole team can feed it questions, to see how it behaves. It is difficult to predict how the bot actually acts against people who have not been involved in building the bot. Therefore, this test run will be the bot's first real experience with real conversations. Here, errors that need to be tweaked can be easily detected, and the experience when you go live becomes much better. Think of it a bit like rewriting a text before publishing.
Step 4: At launch, work has only just begun
Now it's getting exciting! The bot has gone live and interacts with live, real customers. All of a sudden, vital data comes in here that can be used to make the bot better over time.
- Everything customers write uses the bot to get better at the language and understand customers better.
- You can measure what kind of issues come in, and therefore know what is worth focusing on going forward, what staple integrations are worth investing in, etc.
- You can see how customer satisfaction reflects the different answers to the questions, and adjust the answers to improve the customer experience.
Step 5: Expand
Now that your chatbot has a foundation in the language comprehension to stand on, and you have the right data to know what's worth investing in, it's time to build on that. Here you can walk a lot of different paths, but the most common ones are usually:
- Translate the bot into more languages and launch in other countries.
- Build on the breadth: the bot answers more questions
- Build in-depth: Use data to know which questions the bot answers, and improve the degree of automation/experience there through integrations.
- Build on within the organization. Internal bot that answers employees' questions? A bot that collects leads? A cure as a helpdesk?
By now, you have a stop-and-run chatbot, and I'm sure you've automated a significant portion of repetitive questions. And to move forward, just repeat steps 4 and 5. If you want to know in more detail about what our projects with customers have looked like and what the results have been, we are happy to talk to you!
Learn more about how we have worked with our customers and what a chatbot project looks like.
Recently, one of our clients asked us to teach Ebbot to detect toxic messages in conversations. Thanks to this special request, we got a chance to work on one of the most difficult topics in the Natural Language Processing (NLP) field. And yes, we can't be more excited! 🥳
After successfully extended a powerful Natural Language Processing (NLP) model called "SentenceTransformers" to Swedish language, we decided to continue with another exciting project aiming to half-automate the intent training process by grouping similar sentences.
Implementing a chatbot project can be a long and complex journey if you don't know how to do it. There are many risks associated with automating customer caps, and ultimately the reputation of the organization is at stake, as the chatbot becomes the welcome mat for customers to create that first impression that makes up the majority of what they'll think forever. It's also easy for a chatbots project to drag on, costing the shirt.