When working on AI projects, owning data to nurture your solution is key for good performance. Gathering e-mails and conversation logs to train your bot may be as good as a makeshift solution, but this lack of data can now be cut off at the root. Why not start farming your own data instead of harvesting it?
According to Gartner, the Customer Engagement Center (CEC) is one of the fastest-growing application software markets. Have you already jumped on the bandwagon of contact center chatbots, but didn’t get the expected results yet? Most consumers switch to a competitor after one bad customer experience. Can automation actually improve service? Stop wasting time and start making your chatbot work!
The upgrade we performed to TechCrunch’s Messenger bot turns 1 month today. It’s been 4 weeks since we integrated our NLP middleware into the existing bot architecture to make it benefit from our query rewriting technology, so it’s time to look at the effects it is taking.
We have already talked a lot in this blog about training chatbots, the issues bot builders encounter in this task and our tips to enhance its performance, no matter the NLU platform they are built on. Dialogflow, Lex, LUIS, we have studied them all.
Some of you have asked for more details about the TechCrunch's Messenger bot upgrade, so we decided to share them here. Thus, in this article we will disclosure what exactly did the improvement consist on, along with some query examples and how this evolution to a more conversational bot was possible.