For brands that decide to analyze their own influencer programs – instead of hiring Fuse to do so – we recommend the following approach:
Descriptive Analysis: Examine the percentage of programs reporting each predictor to look at the overall distribution of the variables. If a variable does not exhibit variation, do not proceed to look at this variable. The distribution represents the spread of a variable around a mean. If a variable doesn’t differ, then it is not useful as a predictor.
Correlation Analysis: Estimate correlations between the variables and the outcome measure of success. This tests to see if there was an association between the predictors and success. If there seems to be a significant association (i.e. moderate to large correlation), then proceed to Test of Difference.
Test of Difference: Estimate a statistical test to see whether the outcome is different depending on the mean or level of the predictor variable. The outcome is a 3-level categorical variable. For predictor variables that are continuous, use the ANOVA test. The ANOVA tests whether the mean of the predictor is different at different levels of the outcome. For predictor variables that are binary (no/yes) or categorical, use the chisquare test. If the predictor and outcome are not associated, then expect an equal distribution of the predictors across levels of the outcome. If the observed distribution is different than expected given an equal distribution, then conclude that the variable is associated with program success.