Time-based data segmentation helps you filter your responses for a particular time frame, depending on when the survey was completed. Specify a time-frame to analyze data captured only during that period. Longitudinal studies generally feel the need to analyze responses based on various time frames. This feature is exceptionally vital in cases where the market is volatile and subject to continuous change.
Researchers generally use time-based filters to measure customer behavior and social-personality over time. Organizations use them to study fluctuations in thoughts, emotions, and behaviors from time to time or moment to moment.
For example, A time-based approach helps compare different characteristics during different periods to study consumer trends. Time-based filters help assess advertising campaigns and their effects on the target audience.
Time-based data filters help organizations track consumer behavior. It helps them understand the market trends and the factors that affect them. It also helps to understand the effectiveness of advertisement campaigns and gauge consumer behavior during specific seasons or behavioral changes due to external factors. Researchers use time-based filters to understand the level of engagement created by a brand due to marketing activities.
Here are the advantages of time-based segmentation.
Learn how to set up and use this feature with our help file on time-based grouping analysis.