To perform during sales, it is essential to measure the intrinsic quality of a product in order to optimize its markdowns and to be able to obtain reliable advanced predictions for the entire duration of commercial transactions. Sensitivity is measured by the acceleration coefficients of the product according to its markdown rate on each markdown.For example, a -30% on the 1st markdown does not necessarily accelerate in the same way as a -30% on the 4th markdown.The first thing to do to calculate how much a product will accelerate to is to measure and make its reference VMH comparable during the season. This calculation must be done in accordance with important rules, starting with taking into account the date of establishment of the product in stores and its possible roll up.We are therefore talking here about taking a good look at the sale at full potential, based on a reliable reference period for sales, and according to its distribution in the Network. Many people take into account the last 2 weeks because The data is recent, but this biases the analysis of products that are no longer on the sales floor or that are no longer fully in season. When they are no longer offered, what to do with their VMH? Can it be reapplied at a given moment? This involves rendering the indicators Comparable.To calculate the acceleration coefficient, we will look at this VMH, which carries with it the intrinsic qualities of the product, and on scrolling. The scrolls precisely makes it possible to make the product comparable, because it compares it with the entire network.
- VMH analysis (average weekly sales) across the network
- Scrolling : an interesting metric because it depends on the location. VMHs can be strong if all stores in a network have a model; to calculate comparable sales velocity between products, it is better to relate it to the number of stores that actually offer it
The most important factor explaining the acceleration coefficient of a product is its Scrolling in season.You must then carefully adjust your calculation with the rupture rate.The idea here is to check if the product has a Degraded offer (= what management points are available on all initial management points) .In other words, What is the probability that a customer will find the size they are looking for when going to the point of sale ? Not integrating this dimension into the markdown strategy risks sacrificing a product that would not actually leave stronger. If the size chart has a hole in the racket, we're not going to sell more. It is as simple as that. Another indicator to be calculated: the waist cover, that is to say the product stock divided by the VMH.This gives the number of weeks of stock available at the established flow rate.Almost nobody (except Revenue Studio customers!) Don't do it waist cover. The default reflex, often due to a lack of appropriate tools, is to work on stock of all sizes combined. To make up for this deficiency, we added a score to the Revenue Studio platform to allow us to work on these figures.
- A score : homogeneous coverage with a flow of all sizes at the same time
- C score, on the other hand: more than 38 or 40 in 3 weeks, but 50 weeks of size 44...
You will have understood it: we must look at the quality of the offer, examine the location of sizes in stores and make the covers truly comparable.On this last point, we set a threshold that corresponds to the point of sale at which we want to look at the coverage (75%). This approach makes it possible, among other things, to limit noise during the analysis.Finally, for brands that also offer discounts on the Internet, you must Differentiate the acceleration coefficients of the Web from those of stores. The customer is much more promophilic, because he has the possibility to filter all products by reduction brackets (-60%, -40%...). Online merchandising promotes often marked differences in behavior.
How the Revenue Studio platform meets these needs
As we saw at the beginning of the article, weekly sales speed is an essential indicator of product performance.The difficulty is to know How fast will a product accelerate : will he do x2, x3, x5 compared to his VMH according to the rate we are going to ask him? Revenue Studio by Alphalyr makes it possible to anticipate this VMH acceleration coefficient. The platform is able to predict what a product will do based on X, Y or Z rates by looking at the past accelerations of an identified VMH product. We also know that a product that already works very well in season will accelerate all the more strongly if we give it a given rate.In other words: the VMH in season gives a lot of information on its ability to accelerate according to the markdown rates.This is why we designed Revenue Studio so that you can “play” in a personalized way with acceleration coefficients. We are capable of create a specific model for calculating accelerations according to your product offer and the buying habits of your customers. Our model also allows determine the acceleration coefficient of a product and of a rate never observed or tested before (e.g.: test a -40% on a model that has never been discounted in this way, what margin gain, etc.?)
End note: Revenue Studio vs machine learning?
We are regularly asked if machine learning is desirable for optimizing markdowns. The concern is that it has a major disadvantage: that of quickly becoming a black box. We can therefore not extract ourselves from it to simulate other hypotheses. It is a poorly adaptable tool that “simply” analyzes a catalog with past sales and annotated data. He learns from a history that is by nature chaotic (weather, Yellow Vests, etc.) .Our Revenue Studio platform aims to offer you the opposite: Get back in control of your strategy. We prefer advanced statistical methods such as econometrics to answer our class of problems and leave machine learning to other uses for which it is better suited (visual recognition...) .The aim of the econometric model is to separate the contextual traffic effect from the effect of the true acceleration coefficient of the product.
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