All of these solutions assume all users are the same. You could of course fragment users and test within the fragments, but how do you determine the fragments - gender? income? language? country? time of day?
Some color might work well for English speakers in China, while another might work well for Spanish speakers in the US.
Is there a way to both perform these tests, and automatically fragment results based on known facets about users?
As of now, all methods I've seen are based on the assumption that if 60% of users prefer one thing, that's the right choice. What about the 40% - how do we give them what they want?
You are implying that this climate change reference is in any way related to the current politicized theories about anthropogenic climate change. I'm guessing if you actually read the article (as you also imply,) you would conclude otherwise.
Based on corroborating evidence from people I know who have interacted with Lightbank, I would say yes.
They are the Groupon mafia, which should set some alarm bells off to begin with given Groupon's shady behavior toward investors (fudging numbers, cashing out most of their billion dollar round to shareholders, etc). I've heard stories self-indulgent term negotiation, deceptive terms, and dragging their feet before scuttling deals.
So in other words, dirty tricks sound right up their alley.
I just heard of Unicorn / Rainbow as a way to get more out of Heroku workers/dynos. I wonder if this is also compatible, or a better alternative?
I'm new to this whole concept, so am just about to get my feet wet.
Hopefully, Heroku will implement something like this internally, as it seems it would help them to eliminate the greatest flaw with their system - that each deployment require 5-10 seconds of downtime as new dynos are started.
Some color might work well for English speakers in China, while another might work well for Spanish speakers in the US.
Is there a way to both perform these tests, and automatically fragment results based on known facets about users?
As of now, all methods I've seen are based on the assumption that if 60% of users prefer one thing, that's the right choice. What about the 40% - how do we give them what they want?