We are at a critical juncture. Many of the fashion brands and retailers globally are facing challenge of managing their full price sell throughs, reducing inventory pile up and managing markdowns. Apart from the business metrics of revenue and profits, as an industry we are responsible for minimising the carbon footprint in the world by doing more with less.
Continuous improvements call for learning from the past apart from using technology to predict the future. In this edit we take a look at how a metric which we take for granted can impact our own understanding of demand based on how we compute them.
When you ask the question “Can you show me your top sellers?” to anyone in the industry, which metric do you think they would use for coming up with the leader board?
1. Net Sales Value
2. Net Sales Quantity
3. Rate of Sale/week Value
4. Rate of sale/week quantity
5. First “n” weeks rate of sales
Which one of them do you use? Share your metric here
In our view, as shared in our previous edit “How to move your business with Metrics?”, a good metric is one which is comparable, understandable and relative. Hence rate of sale is a right metric to understand how products perform. It connotes the velocity of demand. While knowing that is important, and many do, what is critical is how the metric is computed makes a lot of difference.
After studying various patterns of sales, here are our observations on how what we see can be misleading.
How do you calculate Rate of Sale?
Rate of sale is the demand per unit time at point of sale. For example it is the rate of sale per product per store per week. If that is the case, how would you calculate the overall rate of sale of a product at country level?You would calculate rate of sale of each product at the store level and sum it up across the country level. Is it not simple?
Is this accurate enough? There is a catch here. The real demand of a product is reflected in sale when the product is instock. How does this impact the calculation?
Going back to the method, in order to calculate real rate of sale, you need to consider every productstore level sale on days when there was stock of that product. This means, you need to have sales and stock information at productstore level on a daily basis. The product launch date in each store can be different. This means it is a calculation at each store independent of other stores.
The next level of precision is to do this at the SKU level.
What is the challenge here?
In our experience, most of the fashion brands and retailers do not preserve stock data at store level for the past periods at a day level and the information is available at week/month level. Even when it is available at these granularities, the sheer complexity of calculation makes it difficult for the systems to compute this metric on the run as the stock and sale informations sit in discrete systems or the lack of awareness of the impact of how the metric is computed.
At this point, we would like you to review at how this metric is calculated in your organisation?
What are the different practices?
The practices we observed in comparing demand performance products are,
1. Net Sales Value
2. Net Sales Quantity
3. Rate of Sale/week Value (Aggregate level)
4. Rate of sale/week Qty ( Aggregate level)
5. Rate of Sale/week Value (StyleStore level)
6. Rate of sale/week Qty ( StyleStore level)
7. Rate of sale/week Value (first “n” daysAggregate level)
8. Rate of sale/week Qty (first “n” daysAggregate level)
9. Rate of sale/week Value (first “n” daysStyleStore level)
10. Rate of sale/week Qty (first “n” daysStyleStore level)
The metrics 5,6,9,10 are relatively rare. Some brands use first “n” days to reduce the complexity of figuring out when does the stock run out. This is a smart way, but is that a good predictor of a product”s demand?
In our observations, that depends on the nature of the product and sales cycle. This method assumes that the first 4 weeks is a true representation of the season in terms of demand. Please see the patterns of sale we observed across time periods for different product (inspite of instock). Considering the different patterns, this method is also not robust.
