What is MRP
MRP, or to give it its full name, Multilevel Regression and Poststratification, is a form of advanced data science made popular by Professor Andrew Gelman. Professor Gelman first used it for election forecasts, while Latana is the first to use MRP for brand tracking.
MRP creates a model and uses this model to generate estimates for responses in a survey. This model, when given a set of respondent characteristics, can produce an estimate for how that type of respondent would answer a survey question. Following that, MRP organizes the respondent’s characteristics into groups. By doing so, they can better capture how the variables interact in real life. Finally, MRP takes weighted averages of all the predictions. This is to ensure that the model has a fair sample of respondents.
In a nutshell, MRP does the following:
Uses past data to correct for fluctuations in the data over time
Leverages all the data in the sample and utilizes weighting techniques to ensure findings are more accurate and representative
What is the value in this?
Gain insights of a higher level of accuracy than quota sampling
Control for inaccurate fluctuations or “noise” in the data over time
Measure brand performance among ultra niche audiences with high confidence bounds
Learn more about MRP here.