Big Data from the Retail Equation

I have previously discussed The Retail Equation. For those who missed my previous posts or who haven't heard of this firm, The Retail Equation helps retailers manage merchandise returns, with the purpose of identifying "serial returners" and guarding against things such as "wardrobing" (buying clothing for an event, and returning it for a refund after the event).

But in addition to managing returns, The Retail Equation gathers up a lot of data. (Doesn't everybody?) And since we have just concluded the busiest return days of the year (post-Christmas), the company has shared some of its statistics on holiday returns.

For example, on a state by state basis, the state with the highest rate of returns ("comparing total dollars purchased to total dollars returned and exchanged") was Ohio, with 25.6%. The lowest was Nevada, with 16%. You can see why retailers want to manage this; in the worst-case scenario, only 75% of the products that are purchased remain as purchases.

Oh, and the high point of returns occurred at 12:42 pm Pacific time on December 26. I think I waited on my exchanges (wrong shirt size) until December 27 or 28; no way do I want to brave the December 26 madhouses.