While working as a merchandiser for big brands and retailers, I learnt that work was never ‘done’. Seasons and deadlines came and went, and plans were made and beaten – most of the time ;-).
We would look for market signals and make decisions on how to interpret them and then on how to act. When we missed a trend (the cargo pant or the raglan tee), we would do what we could to liquidate slow movers and quickly turn the cash into items that consumers really wanted. All within agreed frameworks of ‘open to buy’ budgets and ratios that we needed to respect (men to women to kids, footwear to apparel to accessories; tops to bottoms; fashion to fundamentals… and so on).
All of us were spreadsheet wizards. We would filter, sort and find relations and pitch the conclusions to get more budget to up the game. And the biggest challenge was to find the right levers to pull at the right time for the most effect. Not much has changed since then. When I visit our accounts, I notice that merchants are still Excel gurus, and hundreds of millions of Euros, Dollars and Roubles are invested via spreadsheet magic.
Today, next to Microsoft, there are many other companies that sell ready calculations on sales, markdowns and buying budgets, and where ratios as ‘weekly cover’, ‘stock to sales’ and ‘sell-through’ rates help merchants make good decisions quickly.
The problem that all of them share, though, is that instead of making the work of the merchant more effective, they merely facilitate existing processes and make them more efficient. Of course, efficiency is a good thing and worth spending money on. However, when technology has advanced according to Moore’s Law for decades since I was a merchant, and many sectors are now able to make supply meet demand at the most granular level, it begs the question: is the fashion and sporting goods sector so different from all the others that we still have to rely on spreadsheets to make good investment decisions?
Step away from using Averages
I am not saying that Artificial Intelligence and Machine Learning will replace merchant decisions on what the next collection should look like – well, not the whole collection anyway – but the first step is to move away from using averages for in-season decision-making on “what to ship where” when technology makes that possible.
For example, when you have 50 stores and each store has roughly four thousand unique items (SKUs), then using an average weekly cover to determine how many units to ship is like having your head in the oven and your feet in the freezer to keep your body temperature around 37° Celsius. Not very healthy. But twenty five years ago, making two hundred thousand good decisions on “what to ship where” and taking into account lead-times, pack sizes, minimum order quantities, based on an accurate prediction of sales for each SKU until the next delivery was simply not possible. Today it is. And tomorrow those predictions will also use the weather and social media sentiments.
So before investing in new merchant tools, ask yourself: are you merely facilitating existing processes or are you using the latest technology to optimize the store’s portfolio all the time and get a competitive advantage?
For those who read this as a shameless pitch: you are right. It is. But we’ll happily tell you more concretely how we work and how we have already extracted huge value by improving flow to retail.