It depends on what you really want to “measure.”Job definitions aside, I never quite find myself in a situation where any one job description fits the bill, nor where I get to use sexy techniques that one hopes will reveal a mountain of insights. Much of the job is dealing with messiness, not sexiness.The most important tool in my entire toolkit — I kid you not — is the good old-fashioned paper and pencil (and critical thinking). Of course, as a former finance analyst, the rigor of cross-checking comes naturally.Hence, many conversational AI startups have been popping up, and many existing software packages have created some module to support chat experiences.At Unboxd, we’ve spent hundreds of hours researching and testing many of these bots, while trying all of the AI cloud services to build bots on.In a series of posts, I hope to give a brief tour of a some potentially novel uses of Mixpanel, plus a few tips on getting the most from the tool. In part 2, I’ll discuss ways to think about funnels and AARRR metrics in a developer platform context, including folding data from multiple sites into a single Mixpanel project to measure events (in this case for a hackathon campaign). I call myself a “Data Scientist” although by training I’m a statistician and financial modeler who traveled the R and Python road to end up in data la-la-land. ) at Cisco, working in the realm of developer platforms.
Trump’s chatbot however is as blunt and baffling as the real man, does not think it’s important to justify his actions or words.A significant part of this uprise has to do the growing popularity of chat bots.Popular chat platforms like Kik, Telegram and (Facebook) Messenger now all have APIs to create rich chat experiences managed by bots.Fans are always looking for ways to talk to the celebrities they look up to.First there were talk shows with dedicated Q&A segments, then came Facebook fan pages and Twitter, Instagram and AMA sessions soon followed suit and now, there are Chatbots!But I’ve come to learn that the phrase “These numbers don’t look right” can mean many things, such as “I don’t like numbers.”The old adage of garbage-in, garbage-out, reigns supreme in data la-la-land and it’s easy to spend effort crunching crappy data (and without “commonsense” checking it).