FairPay is a very flexible, and open architecture that can exploit advanced machine learning and predictive analytics and a richly nuanced user interface. It can be tailored to a wide range of use cases and platform environments with varying scope, objectives, and content.
Recurring Subscription Sales
Two levels of features are outlined here – it is suggested that Level 2 be included or added soon after Level 1. FairPay is a highly flexible architecture, and a wide range of variations and extensions are described elsewhere (see Further Notes, below).
Discrete Item Sales and Trial/Survey Mode are also addressed in Further Notes.
This addresses user-set prices, with a probationary warm-up period for new buyers, and a very basic approach to FairPay reputation and offer criteria.
This adds consideration of buyer explanations by adjusting for reasons/justifications for prices (as only slightly more complex, but significantly more effective).
+Ask for reason? (eg: retired, unemployed, student, affluent, business user, non-profit, startup, large business)
(eg: to allow for -30%, adjust raw TFR+30%pts – so a RTFR of -30% adjusts to an ATFR of 0)
(eg: to allow for -50%, adjust raw TFR+50%pts)
(eg: to allow for +30%, adjust raw TFR-30%pts)*
(eg: to allow for +100%, adjust raw TFR -100%pts)*