When it comes to any modern AI technology, data is always the key. Having the right kind of data is most important for tech like machine learning. But you also need the right amount of data. Chatbots have been around in some form since their creation in 1994. And back then, “bot” was a fitting name as most human interactions with this new technology were machine-like. 

This is not the case anymore. Chatbots have evolved to become one of the current trends for eCommerce. AI algorithms have improved tremendously. But it’s the data you “feed” your chatbot that will make or break your virtual customer-facing representation. 

The Importance of Data for Your Chatbot

When a business like yours decides to build and implement a website chatbot, you will need to solve two problems:

1. How can we give customers a truly conversational experience?
2. How can we answer customer questions and resolve problems?

Many customers can be discouraged by rigid and robot-like experiences with a mediocre chatbot. Solving the first question will ensure your chatbot is adept and fluent at conversing with your audience. A conversational chatbot will represent your brand and give customers the experience they expect.

Answering the second question means your chatbot will effectively answer concerns and resolve problems. In other words, it will be helpful and adopted by your customers. This saves time and money and gives many customers access to their preferred communication channel.

Choosing a chatbot platform and AI strategy is the first step. Each has its pros and cons with how quickly learning takes place and how natural conversations will be. The good news is that you can solve the two main questions by choosing the appropriate chatbot data.

What is a Dataset for Chatbot Training?

Just like students at educational institutions everywhere, chatbots need the best resources at their disposal. The best AI will learn from what you feed it, mainly datasets. This chatbot data is integral as it will guide the machine learning process towards reaching your goal of an effective and conversational virtual agent. 

Chatbot data includes text from emails, websites, and social media. It can also include transcriptions (different technology) from customer interactions like customer support or a contact center. 

You can process a large amount of unstructured data in rapid time with many solutions.  Implementing a Databricks Hadoop migration would be an effective way for you to leverage such large amounts of data.

Types of Data You Can Use

As briefly mentioned above, there are different types of data your chatbot can learn from. Let’s break them down: 

• User input: User input is the most direct form of chatbot training data, which captures real-time interactions between the chatbot and its users. This data reflects actual user language and intent, making it highly relevant. However, user input often contains noise and irrelevant information. As such, it might require extensive preprocessing to ensure quality and usability.
• Customer service logs: this type of data offers a lot of historical interaction information between customers and servie agents. As it provides real-world scenarios, it can vastly improve a chatbot's performance. On the other hand, these datasets may include sensitive information and exhibit inconsistent quality, which is why they need careful handling and filtering.
• Emails: Similar to customer service logs, emails are useful for understanding customer intent and context in interactions. While they provide valuable insights, emails raise privacy concerns and require anonymization to protect personal information.
• Social media interactions: Social media platforms like Twitter, Facebook, and Instagram offer vast amounts of data from user interactions. But like chatbot interaction, it can also be noisy and filled with platform-specific expressions, which can be challenging for the chatbot to process and interpret.
• Transcriptions: Transcriptions of voice interactions provide essential data for training voice-based chatbots. This data is key for developing accurate voice recognition and response systems, but its quality heavily depends on the accuracy of the transcriptions, which often require significant editing and verification.

How to Collect Data for Your Chatbot

There are two main options businesses have for collecting chatbot data.

Gather Data from your own Database

This may be the most obvious source of data, but it is also the most important. Text and transcription data from your databases will be the most relevant to your business and your target audience. The more you can gather, the better. 

Chatbot data collected from your resources will go the furthest to rapid project development and deployment. Make sure to glean data from your business tools, like a filled-out PandaDoc consulting proposal template.

Web Scraping

Web scraping involves extracting data from websites using automated scripts. It’s a useful method for collecting information such as FAQs, user reviews, and product details. 

There are dedicated tools that can help you in this process. However, web scraping must be done responsibly, respecting website policies and legal implications, since websites may have restrictions against scraping, and violating these can lead to legal issues.

API Integrations

APIs enable data collection from external systems, providing access to up-to-date information. 

This type of data collection method is particularly useful for integrating diverse datasets from different sources. Tools like Postman help manage API requests and responses. Keep in mind that when using APIs, it is essential to be aware of rate limits and ensure consistent data quality to maintain reliable integration.

Open Source Training Data 

It can’t hurt to leverage freely available resources. There is a wealth of open-source chatbot training data available to organizations. Some publicly available sources are The WikiQA Corpus, Yahoo Language Data, and Twitter Support (yes, all social media interactions have more value than you may have thought).

Open source chatbot datasets will help enhance the training process. This type of training data is specifically helpful for startups, relatively new companies, small businesses, or those with a tiny customer base. 

While open source data is a good option, it does cary a few disadvantages when compared to other data sources.

Does not Reflect your Branding 

When looking for brand ambassadors, you want to ensure they reflect your brand (virtually or physically). One negative of open source data is that it won't be tailored to your brand voice. It will help with general conversation training and improve the starting point of a chatbot’s understanding. But the style and vocabulary representing your company will be severely lacking; it won’t have any personality or human touch.  

Unable to Detect Language Nuances

The vast majority of open source chatbot data is only available in English. It will train your chatbot to comprehend and respond in fluent, native English. It can cause problems depending on where you are based and in what markets.  

When non-native English speakers use your chatbot, they may write in a way that makes sense as a literal translation from their native tongue. Any human agent would autocorrect the grammar in their minds and respond appropriately. But the bot will either misunderstand and reply incorrectly or just completely be stumped.  

Generic Data

When building a marketing campaign, general data may inform your early steps in ad building. But when implementing a tool like a Bing Ads dashboard, you will collect much more relevant data. It is no different from using a chatbot.

While helpful and free, huge pools of chatbot training data will be generic. These datasets help inject general conversation skills. Likewise, with brand voice, they won’t be tailored to the nature of your business, your products, and your customers. 

This will create problems for more specific or niche industries. Customer support is an area where you will need customized training to ensure chatbot efficacy. 

4 Tips for Data Management

Building and implementing a chatbot is always a positive for any business. To avoid creating more problems than you solve, you will want to watch out for the most mistakes organizations make. 

Collect Data Unique to You

It doesn’t matter if you are a startup or a long-established company. Gather as much data as you can from your own resources. This includes transcriptions from telephone calls, transactions, documents, and anything else you and your team can dig up.

You will likely have a lot of data to sort through. Having Hadoop or Hadoop Distributed File System (HDFS) will go a long way toward streamlining the data parsing process. What is HDFS in Hadoop? In short, it’s less capable than a Hadoop database architecture but will give your team the easy access to chatbot data that they need.

This will be the chatbot data that drives home your unique brand personality. It will also help accelerate the machine learning process so that your chatbot will provide relevant and accurate solutions for your customers. 

Entity Extraction

Natural language understanding (NLU) is as important as any other component of the chatbot training process. Entity extraction is a necessary step to building an accurate NLU that can comprehend the meaning and cut through noisy data. 

This is where you parse the critical entities (or variables) and tag them with identifiers. For example, let's look at the question, “Where is the nearest ATM to my current location?”. “Current location” would be a reference entity, while “nearest” would be a distance entity. The term “ATM” could be classified as a type of service entity.   

Doing this will help boost the relevance and effectiveness of any chatbot training process. 

Utterances 

No matter what datasets you use, you will want to collect as many relevant utterances as possible. These are words and phrases that work towards the same goal or intent. We don’t think about it consciously, but there are many ways to ask the same question.

Your chatbot won’t be aware of these utterances and will see the matching data as separate data points. This will slow down and confuse the process of chatbot training. Your project development team has to identify and map out these utterances to avoid a painful deployment. 

Intent 

It’s important to have the right data, parse out entities, and group utterances. But don't forget the customer-chatbot interaction is all about understanding intent and responding appropriately.  If a customer asks about Apache Kudu documentation, they probably want to be fast-tracked to a PDF or white paper for the columnar storage solution. 

The intent is where the entire process of gathering chatbot data starts and ends. What are the customer’s goals, or what do they aim to achieve by initiating a conversation? The intent will need to be pre-defined so that your chatbot knows if a customer wants to view their account, make purchases, request a refund, or take any other action. 

Multilingual Data Handling

Handling multilingual data presents unique challenges due to language-specific variations and contextual differences. Addressing these challenges includes using language-specific preprocessing techniques and training separate models for each language to ensure accuracy. 

To maintain data accuracy and relevance, ensure data formatting across different languages is consistent and consider cultural nuances during training. You should also aim to update datasets regularly to reflect language evolution and conduct testing to validate the chatbot's performance in each language.

Conclusion

More and more customers are not only open to chatbots, they prefer chatbots as a communication channel. When you decide to build and implement chatbot tech for your business, you want to get it right. It can’t just be about communication preferences. You need to give customers a natural human-like experience via a capable and effective virtual agent. 

While a seemingly daunting task, it is quite simple. Do your due diligence. Choose the right AI approach for your business. As important, prioritize the right chatbot data to drive the machine learning and NLU process. Start with your own databases and expand out to as much relevant information as you can gather. 

Before you know it, your customers will think there is a live agent at the other end of the chat!