Estimating Sales and Sales Market Share from Sales Rank Data for Consumer Appliances

Our motivation in this work is to find an adequate probability distribution to fit sales volumes of
different appliances. This distribution allows for the translation of sales rank into sales volume.
This paper shows that the log-normal distribution and specifically the truncated version are well
suited for this purpose. We demonstrate that using sales proxies derived from a calibrated
truncated log-normal distribution function can be used to produce realistic estimates of market
average product prices, and product attributes. We show that the market averages calculated with
the sales proxies derived from the calibrated, truncated log-normal distribution provide better
market average estimates than sales proxies estimated with simpler distribution functions.