# Customer Segmentation Using RFM Interpretation of Negative Scaled Variables

I am doing customer segmentation using RFM. Prior to proceeding with the clustering, I log transformed my Monetary variable to account for skewness.

df_RFM$log_monetary <- log(df_RFM$monetary)


After which, I did the z-standardization using scaling.

df_RFM2 <- scale(df_RFM2)


Summary statistics before scaling, after scaling, and after clustering are as attached. My question is, how do I interpret the scaled values, especially the negative ones? It doesn't make sense to say negative frequency and negative monetary retail purchases for instance.   • If $z$-standardization means what I think it does, then $z$ = (value $-$ mean) / SD and negative $z$ just means values below the mean. Whether that's what you want to use or report is a good question but some negative $z$ values are expected in practice. The only exception is that if a variable is constant, all $z$ values are 0. Note that if you take logs first, the mean of the logs is the log of the geometric mean, so taking logs can be combined with standardization. (For me, RFM is a truncation of Read The Fine Manual, but the software or tool you're using doesn't seem the issue here.) Dec 14, 2017 at 13:47
• I remain puzzled at what the real question is here. As already flagged, and as is standard, negative $z$ scores just indicate values below whatever is your mean, either as originally measured or on a transformed scale. Personally, I don't find $z$-scores especially helpful in identifying outliers as (a) I would want to look directly at the data always (b) standardization doesn't affect skewness or tail weight (c) mean and SD are themselves influenced by outliers, so if I had to choose a standardization it would more likely be (value $-$ median) / IQR. Dec 15, 2017 at 12:03