The chatbot craze is sweeping every business and every nation. Its capability of offering instant solutions has taken its demand to the next level.
However, these chatbots are not born smart, they happen to be so because they follow a set of commands to share the information being asked for. This means chatbots are getting intelligent because they are trained that way. Humans instruct, and they follow.
Features like a contextual conversation, voice support, NLP integration, etc, help them learn better.
When you are aware of designing a chatbot, next you need to be aware of how to train a chatbot.
The blog will talk in depth about chatbot training, its importance, and some training processes.
What is chatbot training?
Chatbot training is the process of putting enormous data in the form of conversation text, and training the chatbot using machine learning algorithms so that the bot is capable of understanding and generating human-like responses.
A well-trained chatbot is capable of understanding human emotions. It can read the intention behind human conversion and predict what they actually want. The more trained chatbots are, the more sophisticated chatbots become.
Hence a chatbot that is capable of responding naturally can make the user’s journey flawless.
Importance of chatbot training
1. Better accuracy
There’s a high chance chatbot makes mistakes at times and fails to respond as per the customer’s needs. Training the chatbot is crucial to understand the customers needs better. Providing continuous training prevents chatbots from making mistakes again. Training the bot with relevant data makes it more intelligent and accurate. Hence promotes reliability.
2. Flawless customer experience
A well-trained chatbot personalizes the conversation and provides customized responses at various touchpoints across the customer journey. For first-time visitors, chatbots can be as helpful as a search bar, which will inform potential customers about the product and services without having to visit the website or do the research by themselves. Which leads to flawless customer experience.
3. Cost-saving
Training the chatbot makes it more sophisticated. It handles repetitive yet frequent concerns and queries without needing to transfer the request. Thus, organizations do not need to hire more employees for that purpose, which saves a good amount. And the existing employees can focus more on decision-making activities.
4. Promotes scalability
If growing your business is your ultimate goal, you need to scale and optimize your customer support in order to target more potential clients. And chatbot does that for you effortlessly. It helps you deal with the increased influx of customer queries round the clock, unaffecting the support operations or making a heavy investment.
5. Multilingual support
Chatbots can be trained to carry out conversations in multiple languages. It makes it easier for you to reach out to a diverse customer base and provide them with support in their preferred language. In whichever corner of the world the customer is in, the chatbot will automatically switch to that region’s language while the customer visits your website.
Preparing for Chatbot Training
Before you start training your chatbot, there are a few things you need to take care of.
1. Defining the purpose of the chatbot
Before you start training your chatbot, it’s crucial you define the purpose of the chatbot. What would you want your chatbot to do, or what problem do you want it to solve? For instance, do you need the chatbot to improve your resolution time for customer support, or do you need to increase customer engagement, just to name a few
Being specific makes the bot more intelligent and the most potent tool that gets things done in the most productive way.
2. Gathering data for chatbot training
The conversational bot represents your brand and provides customers with the experience they expect. Therefore having the right set of data in the right amount is fundamental for any technology like Machine learning. The data you feed the AI technology can make or break your support system. you can gather data from customers’ chat logs, email, and website content.
The chatbot data guides the machine learning process toward reaching your goal of outstanding customer support.
3. Choosing a chatbot platform
You must choose the right platform if you want to implement a successful marketing strategy that will save you time and money. A good chatbot platform should have the features needed to create chatbots for multiple messengers in one place. Choosing the wrong platform will cause users to utilize the bot in ways that weren’t intended. As a result, it will have infuriating and unpleasant consequences.
How to train a chatbot
A. Preparing the data
1. Use data logs that are already available
A practical and easy way to collect data for chatbot development is to utilize chatbot logs that you already have. The existing chatbot log contains relevant and best possible utterances for customer queries.
2. Use a human-to-human chat log
You can also collect data for your chatbot by mining words and utterances from the existing human-to-human chat logs.
Or you can also create your own data training example for your chatbot development.
B. Building the chatbot model
Here are a few things you need to keep in mind while building the chatbot model.
1. Set the target audience
Keeping use cases in mind, you must identify what type of audience you will target. What can be their pain point? How can you use your chatbot to soothe the problem? Where are your targeted users hanging out? Which messaging channel would perform better?
Having answers to all the above questions will make it easier to plan more strategically and precisely. Once you’re clear about who your target audience is and their area of difficulty, you can program your bot accordingly.
2. Consider your brand and bot personality
Your bot represents your brand. So it’s important for you to train like your brand representative. Program your chatbot in a way that responds to users’ gestures and actions in a polite way with relevant answers.
3. Build with NLP
Leverage NLP while building your chatbot. It can derive meaning from text inputs. This means it helps your chatbot analyze and understand human language and reply in a more intuitive way and reply accurately.
4. User-friendly experience
Your bot should be simple and easy to navigate. It decreases the search time as well, and users will fulfill their needs in a faster and more efficient way
C. Evaluating and improving underperforming answer
You can take a few questions that you get from the customers and program the bot with the answer. Then evaluate how satisfied the customers are during the interaction with the bot. Focus on how well the answer performs and make a quick modification in case the answer is not performing well.
Underperforming responses clearly highlight the difference between what users are asking vs what your chatbot is responding to. You need to make a few attempts of trial and error to improve the response.
Maintenance and updating the chatbot
A. Monitoring chatbot performance
Now that you have a chatbot ready. You want to monitor its performance. Are the users happy with the responses it provides? Is it having a positive impact on conversation agents over recurring contacts? To get the answer to the questions, you should have quantitative key performance indicators that evaluate your chatbot’s effectiveness.
1. Chatbot activity volume
Measuring the chatbot activity means assessing the number of interactions. How frequently the chatbot is used, the number of users increasing, and so on.
2. Retention rate
The retention rate shows you how many users consulted your chatbot on repeated occasions over a period. It gives you an overall idea about how relevant the chatbot is and its level of acceptance among users.
3. Target audience session volume
These indicators verify whether you achieved your goals or not. Whether or not the targeted audiences are making significant use of chatbots. If not, you should rethink your strategies in order to get your users on board.
4. Chatbot response volume
This indicator makes it clear to you the number of questions your chatbot has answered.
5. Non-response rate
This metric measures how many times the chatbot failed to respond. Lack of content or your bot’s difficulty in comprehending the user’s query might be the reason for such failure.
B. Updating the chatbot knowledge base
It’s important to update the knowledge base so that your bot is ready to correctly deal with queries and requests with minimum communication failure.
The chatbot knowledge base should be updated with the change in the market in order to keep the users informed with relevant and fresh information. Keeping the knowledge base updated prevents confusion and errors. Regular updates build trust among users. Chatbots can respond to users more effectively.
Wrap up
Chatbot technology isn’t perfect. The limited amount of data, language barrier, irrelevant context, etc can be some common reasons that keep the chatbot behind. However, proper training can avoid these flaws and make the chatbot work beautifully.