Store Clustering
You would already have a store classification. However, store clustering can be a very different take on it. A fashion retailer with 300+ stores wanted a classification on the basis of one single variable of the AGR (Average Garment Rate) for the store. We felt this would lead to a lot of misclassification (where the wrong inferences are made just because you called a certain store an “A” store basis AGR), and there was room to look at many more variables.
So we went completely ballistic and threw 68 variables into the mix. Clearly overkill – I mean, how do you even assimilate 68 variables to cluster stores into A/B/C stores? A lot of these were customer centric variables (this brand has a loyalty program contributing over 70 per cent of its revenue) – variables like: Womenswear contribution, per cent customers who are over 40 years of age, Ability of the store to drive a repeat transaction, per cent Contribution of “Premium” products in each category and so on… for around 65 more variables.
When finally we did the classification, we reduced the 68 variables down to a much smaller set by eliminating non-contributing variables, but even then the big discoveries were that there is so much more to classifying your stores right – and things like per cent womenswear sold, or per cent product sold with a discount can have a large say!
Tracking Sell-Throughs/ Managing In Season Markdowns
Once the season has started, it’s about closely tracking sell-throughs over time. The time frame could be days or weeks depending on how fast your fashion is, but the logic is the same - If it ain’t selling quick enough what can you do, and when?
Diese Geschichte stammt aus der September 2015-Ausgabe von Images Retail.
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Diese Geschichte stammt aus der September 2015-Ausgabe von Images Retail.
Starten Sie Ihre 7-tägige kostenlose Testversion von Magzter GOLD, um auf Tausende kuratierte Premium-Storys sowie über 8.000 Zeitschriften und Zeitungen zuzugreifen.
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