Occupant satisfaction surveys are widely used in laboratory and field research studies of indoor environmental quality. Field studies pose several challenges because researchers usually have no control over the indoor environments experienced by building occupants, it is difficult to recruit and retain participants, and data collection methods can be cumbersome. With this in mind, we developed a survey platform that uses real-time feedback to send targeted occupant surveys (TOS) at specific indoor environmental conditions and stops sending survey requests when collected responses reach the maximum surveys required. We performed a pilot study of the TOS platform with occupants of a radiant heated and cooled building to target survey responses at 16 radiant slab surface (infrared) temperatures evenly distributed from 15 to 30 °C. We developed metrics and ideal datasets to compare the TOS platform against other occupant survey distribution methods. The results show that this novel method has a higher approximation to characteristics of an ideal dataset; 41% compared to 23%, 19%, and 12% of other datasets in previous field studies. Our TOS method minimizes the number of times occupants are surveyed and ensures a more complete and balanced dataset. This allows researchers to more efficiently and reliably collect subjective data for occupant satisfaction studies.