How to Train Your Chatbot to Deal With Customer Queries

It’s important not to rush chatbot training, as like many things you get out what you put in, and the initial learnings will form the foundations of your system going forward. You can choose to build your chatbot from scratch, utilize a bot developer platform or purchase a ready-made solution. Which route to take should be based on the in-house expertise you have and the outcome you’re looking to achieve.

Here are some of the time frames you can expect for training different types of bot:

Simple, one chat integration

Timings will vary based on the complexity of your chatbot, but you can expect to perform a simple integration with one chat in between 40 and 56 hours. That means you could have your bot up and running in around one working week if your goals are fairly modest.

Implementing a natural language user interface

Moving beyond command language and putting a natural language user interface in place will be more time consuming. It’s likely to take somewhere in the region of 120 to 160 hours to implement this type of strategy, but your deep learning chatbot will be more sophisticated as a result.

Applying business logic

Some organizations may need to go a step further with their machine learning chatbot and apply business logic. This set of rules allows the bot to access additional context for a problem and connectors to a system to offer much more nuanced answers to customer queries. Creating this sort of logic from scratch is estimated to take between 160 and 192 hours.

How to train a chatbot

When it comes to how to train a chatbot, it’s important to work through a number of steps to create a bot that performs as required. There have been several high profile cases of chatbots that haven’t been properly trained and gone rogue in ways that didn’t enhance the reputations of the organizations they were representing.

The most famous example was Microsoft’s TayTweets, which was designed to adapt to the conversations it was exposed to. Trolls immediately bombarded it with offensive tweets and the bot was normalized to hateful content. In 16 hours, the TayTweets bot tweeted over 96,000 times and offended women, LGBQT and other minority groups.

Avoid such mishaps with good chatbot training.

1. Start with the business problem

While there are many use cases for AI chatbots, it’s important to identify which business problems you’re aiming to solve with your bot. That will ensure its development is targeted and actually solves customer queries when it’s rolled out on your site and other communication channels.

2. Clearly define your intents

Avoid a frustrating end experience for your user by creating intents with a defined purpose to train your chatbot. These will enable your AI-enabled bot to establish firm foundations based on clear principles that will develop over time.

3. Cover all potentials in your utterances

The only way to train a chatbot to be truly useful is to expose it to as many potential utterances as possible. That’s because language in the real world is made up of multiple expressions to say the same thing and your bot must be able to recognize the intent in each phrase.

4. Diversify your training team

Recruit a mix of people to train your chatbot so it becomes accustomed to a variety of styles and perspectives. This is the best way to ensure it’s given as much exposure as possible to different questions and the ways they’re asked in preparation for the real world.

5. Employ continuous training

It’s unlikely you’ll find every area is covered when you deploy your chatbot, so be prepared to plug gaps after it’s gone live. Even if it seems to be performing well, your chatbot will need ongoing training to keep up to date with developments in technology, your industry and your business.

Further reading:

• The State of Marketing Operations in 2022 [Infographic]
• Why Marketing Data Management Remains a Huge Headache for Marketers
• Marketing Automation is More than Just a Software: It's Strategy