One of the earliest and most effective ways to quickly gauge user interest for a new feature is using what's called a Fake feature test.
It involves doing minimal development, combined with Google analytics, to generate some on paper proof that a feature might prove useful to users.
I propose we create an additional KnetMiner view: Summarise results/ GPT Summary. The button should only appear once the user has generated a graph. When a user clicks on it, the viewport will simply display: "Coming soon!" or maybe somewhere to sign up for updates at most.
This then needs to be connected to GA to track clicks and should give us early insight into the viability of such a GPT summary feature in KM, without much development at all...
One of the earliest and most effective ways to quickly gauge user interest for a new feature is using what's called a Fake feature test.
It involves doing minimal development, combined with Google analytics, to generate some on paper proof that a feature might prove useful to users.
I propose we create an additional KnetMiner view: Summarise results/ GPT Summary. The button should only appear once the user has generated a graph. When a user clicks on it, the viewport will simply display: "Coming soon!" or maybe somewhere to sign up for updates at most.
This then needs to be connected to GA to track clicks and should give us early insight into the viability of such a GPT summary feature in KM, without much development at all...
Open to comment