#bot platform

How to add Quick Replies buttons to your Chatbot? - SnatchBot

How to Build a Chatbot - SnatchBot

Big Data is a term used for extreme data volumes that may include varied data types. Traditional data processing software may not be able to handle such large volumes, and it’s often tough to perform predictive analytics on such data. The challenges include capturing and storing such data, efficiently querying and updating databases, and analysing it to make important business decisions. Big Data analytics involves analysing such data sets to find patterns or trends.

So how do chatbots fit into this scenario? Let’s see what we already know about chatbots: they simulate natural conversation in a quick, easy and convenient manner, and they’re customer friendly. Chatbots are huge in the world of technology and machine learning right now, and are expected to get bigger this year. But how can they help in your Big Data project?

Chatbots work on data. What makes them different from programs that work though basic APIs, is that they feed on data, and use it to make conversations. They can help answer users’ questions naturally, quickly and efficiently, just like a human would. Such artificial intelligence is possible only through a huge and well-built data infrastructure that can back up your chatbot. This also works the other way round. Chatbots can be one source that helps you develop a smart, useful and productive system that helps in your Big Data project. Here’s how:

Your database will grow fast. The same data that your bot identifies from the user’s questions or text/voice input will be fed into your system. This takes you one step further from just tracking basic user behaviour like clicks and cart activities. Chatbots focus on human conversation instead, thus providing more valuable insights. You won’t get these insights from a system that simply tracks a user browsing your website.

More than just sourcing information for your Big Data project, a chatbot also detects keywords or entities in its customer conversations. This aids you in identifying what your target audience is exactly looking for, and what influences them to, or stops them from, buying a product. This in turn fuels Big Data analytics, and may help you answer a lot of your own marketing or customer service-related questions.