6 Tips on How to Train a Chatbot for Non-Technical People
Today, most of the companies interact with their customers via many communicational channels. A chatbot can be one of them.
You can think of chatbots as your brand representatives. Often, they can be an initial touch-point between clients and your company and form the first impression of your brand. So, you need to make sure it is as sharp as possible, helpful and relevant. To do so, you have to train and test your chatbot.
It’s also worth to note that a chatbot training is an ongoing process that doesn’t end after chatbots launch. To always keep up with user needs you have to improve your bot constantly.
In this article, we will give you 6 tips on how to train chatbot that will save you from falling into common traps.
So let’s get started.
1. Keep in mind your target persona
You need to know your chatbots audience to build a relevant bots flow, a tone of voice and vocabulary. So, if you haven’t still formed your buyer persona profile, here’s a great article that will help you do that. Some questions mentioned in the article are mainly B2B so you can skip them if they are irrelevant to your business.
To understand who is your targeted user, you need to collect and analyze clients data you already have.
If relevant, consider things like gender, age, location, language, income, their industry and job title, hobbies and interests, their buying behavior and the most significant challenges. Don’t worry if you don’t have all the information in clients base, you can send surveys or have customer interviews to fill in gaps.
Analyze the information you have collected. Are there any patterns, or things are in common for your customers? Have a look at your conversations with these clients, try highlighting things that connect them.
Note you don’t have to have only one buyer persona. You can create two or more profiles if you need to.
After you have figured out your target persona, you need to understand the main client’s requests. If you are wondering where to start, visit your customer care or tech support and find the main reasons clients contact your company. This will help you to understand what are the most popular issues which your chatbot will need to handle. At this stage you don’t have to be specific, try to define main types of problems your users have.
Have a look at Maggie. A healthcare chatbot that has a friendly and welcoming persona. Her flow includes a variety of different bitmojis that Maggie uses in different situations to warm up a conversation with a user.
2. Categorize main customer requests
Now, when done with chatbots audience, the next thing is to define the main customer intents.
To do so, create categories. These categories will contain different customer requests on the same topic.
For example, you have pulled the information about popular requests from customer service and noticed that most of the interactions are about a delivery date. You can group requests like “when my parcel will be delivered?”, “what is the delivery date?”, “when I will receive my order” etc. into one category “Delivery info.”
Define a few of the main customer issues and move to the next step.
3. Create a dataset to train your chatbot
When you have created categories with the main requests, you’ll need to fill these groups with “user says.” By this, I mean that you need to write as many ways of saying the same thing as possible. The more alternatives to a request you collect, the more data you will have to train your bot and the more prepared for real interaction it will be.
As you need a lot of training data, here you have two options:
– Train a chatbot with available data
To create a database you can use old data from your current customer support. Find previous interactions with your customers. To do that, you can review call logs and scripts, email chains, analyze FAQ pages. Check your @support or @info Inbox for the repetitive requests. Think about what are the most repeating questions and issues your clients stumble upon. Consider which of these questions, words, phrases your chatbot has to understand. At this step, it’s better to be specific and collect as many ways of saying the same thing as possible.
– Train a chatbot using pre-made datasets
The other option is to use pre-made ready-to-use datasets. These datasets are handy when you need to train your chatbots Natural Language Processing (NLP) fast, or you don’t know where to start. They already have questions and answers and can help you cover the basic topics. The most popular datasets are Cornell Movie-Dialogs Corpus, The Ubuntu Dialogue Corpus, and Microsoft Research Social Media Conversation Corpus. These datasets include some basic dialogs and conversations that can help you at the beginning of the testing stage.
So, when you have created your first database, you can test it.
4. Let real users test your chatbot
Now, you need to test your chatbot. The best way to test chatbot is to have a conversation with it and pay attention to things like:
- Bots Flow
- User Experience (UX)
- Response Speed
- Chatbot’s Accuracy
- Fallbacks and what happens when bot doesn’t understand a user
- Is it engaging to talk to a bot
There are a few options on how to find users for testing.
One way is to ask your co-workers to join the testing and collect training data from their interactions with the chatbot. But, remember that your stuff can be biased as they are familiar with specific terminology, your company, services, etc. and the way they interact with a bot can differ from your chatbot’s audience.
Also, you can involve your real customer in the beta testing of the bot. You can ask your most loyal clients to join the testing. Or as an example, you can engage your current clients to chat with the bot for some reward like a discount or a coupon.
And remember, the more people interact with your bot, the more training data you will get to make your chatbot prepared for different use cases.
5. Keep improving your chatbot after launch
Don’t forget that you need to improve your chatbot constantly. After you have launched the chatbot, keep analyzing its interactions with users.
You need to find the areas your chatbot is having trouble with and fix them. Perhaps, the bot wasn’t sure how to respond to a situation, or it was not appealing to communicate with for users.
Find weak spots and track how smoothly your bot is operating by connecting it with analytics.
Not all chatbot builders support integration with analytics, but sometimes they may already have one. The main task of this part is to improve the structure of the flow based on statistics and user’s feedback.
6. Don’t forget to support your chatbot
We recommend you to have a person who will monitor the work of the chatbot during the initial launch period. The main task of this person would be to take over the communication process if something were to go wrong. This will help you not to lose the lead and potential client.
Also, be sure to add a Live Chat option either as a button or train NLP to understand this request. This is important so a user could contact a real person if something goes wrong.
When training your chatbot don’t forget about these main tips:
- Keep in mind your target persona to build a relevant data set, a tone of voice and bots flow.
- Find and categorize the main customer request into groups.
- Create your data set or use a pre-made one to create chatbots vocabulary.
- Let real users test your chatbot. Engage co-workers to chat with your bot to collect more training data and feedback.
- Don’t forget to keep improving your chatbot after launch and use analytics to find weak spots.
- Be sure to support your chatbot and have a Live Chat feature.