[benswerd]

Without using agnost, what are some basic SQL queries I can run on my data to find outliers I'd otherwise be missing?

How far can I get with just keywords, common phrases, boring traditional analysis?

Depending on what I measure there, when is the right time for me to consider upgrading to something like Agnost/what is a specific example of what it will find that traditional/rigid analytics approaches will miss?

[AjmeraParth]

keywords and sql rarely work - you can not find the repeated hidden feature requests, cause we don't know them at the first place yet, or a frustrated user puts vague signals as ugh, ahh, or just an 'f!' (and added modalities, accents and languages makes it much more challenging)

interestingly, even embeddings seem to bucket "no" and "nooo!" somewhat similar, but are pretty different when viewed from a user satisfaction perspective.

A sweet spot on moving to Agnost is the time when you get higher inflow of conversations you can't manually read or listen, and want to clusterize them into things which matter, with the outliers highlighted