Google’s Chatbot Analytics Platform Chatbase Launches to Public

Considerable challenge, but tracking the process data by which a chat progresses can lead to better results.  Ultimately this leads to better conversations.  Could also lead to suggestions as to how humans are appropriately inserted into chat?  Back to our concierge driven experiments.  Note the connection to a number of current poplar assistants in the list below.  Like the direction.

Google’s chatbot analytics platform Chatbase launches to public

By Sarah Perez    @sarahintampa  in TechCunch

At Google  I/O this year, Google quietly introduced a new chatbot analytics platform called Chatbase, a project developed within the company’s internal R&D incubator, Area 120. Today, that platform is being publicly launched to all, after testing with hundreds of early adopters including Ticketmaster, HBO, Keller Williams, Viber, and others.

The idea behind Chatbase’s cloud service is to offer tools to more easily analyze and optimize chatbots. This includes giving bot builders the ability to understand what works to increase customer conversions, improve the bot’s accuracy, and create a better user experience.

This data is available through an analytics dashboard, where developers can track specific metrics like active users, sessions, and user retention. These insights give an overall picture of the bot’s health and see general trends.

The dashboard also lets bot creators compared the bot’s metrics across platforms, to see if some platforms need additional optimizations.

The system today integrates with any voice or text messaging platform, Google says, including Facebook, Kik, Viber, Slack, WhatsApp, WeChat, Alexa, Cortana, Allo, Line, Skype, Twitter, and more.

And though it has had many high-profile testers in its early days, it’s not necessarily meant to be used only by larger companies. As a free service, Chatbase supports bot builders of any size – whether they have one or hundreds of bots in operation. .... " 

Optimizing the Usefulness of Chatbots

Still awaiting reasonably adept and useful chatbots that can do real conversation.  So below starts some key thoughts.  Tracking what the base of their knowledge looks like, and how they are being effectively used.   And how they need to be maintained.  Ultimately relevant common sense and common context will also be key to understand.

Using machine learning to monitor and optimize chatbots

The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.   By Ben Lorica

In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer interaction). We are still in the early days of chatbots, but if current trends persist, we’ll see bots deployed more widely and take on more complex tasks and interactions. Gartner recently predicted that by 2021, companies will spend more on bots and chatbots than mobile app development.

Like any other software application, as bots get deployed in real-world applications, companies will need tools to monitor their performance. For a single, simple chatbot, one can imagine developers manually monitoring log files for errors and problems. Things get harder as you scale to more bots and as the bots get increasingly more complex. As in the case of other machine learning applications, when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential.  .... "