Traditional retail landscapes are undergoing disruptive changes. Many brands reshape their portfolio and close down stores, while facing the challenge of improving the Like-4-Like performance of their remaining points of sale (POS).
This post introduces 3 low-cost ways to push sell-through, reduce mark-downs and improve the gross margin.
1. Identify Bestsellers by Average Rate of Sale
The average rate of sale measures how many pieces of one specific product have been sold on average per POS in which the product was stocked. In the fashion industry, this could be the number of red sweaters of a certain fit and design sold in a particular store. Time-wise, you can measure the average rate of sale (depending on your stock turn) in any imaginable dimension (e.g. daily, weekly, monthly, quarterly, seasonally, annually). Whatever dimension you decide to analyze, you need to make sure not to include marked down items. This would push sell-through but would also reduce your margin and thus void the entire comparison.
Many companies are nowadays able to measure sell-through rather than sell-in. This is a big step in the right direction but still doesn’t yet telling the full story. Most companies identify their best-selling products by the means of a bestseller list that ranks products according to the total number of items sold per option across all POS. This is misleading and limits the financial effect of bestseller management as it compares apples with oranges.
A product that you can sell-in to 100 POS will naturally rank higher on your bestseller list, even in sell-through terms, than a second product that is available only in 10 POS. This is the case even if the sell-through rate of the second product is much higher than that of the first.
The average rate of sale combined with the information of how many POS sell an item, helps you identify your true bestsellers early on. This also helps you decide whether it might be worth replenishing it not only at its original POS but also for POS that did not originally stock the product. It also helps to assess the efficiency of your product development by category and to adjust your product development capacities accordingly.
2. Compare Sell-in / Sell-out on SKU Level Before Mark-down
Too many companies do not use the data available for in-depth sell-through analyses that would help them improve sell-through and increase their exit margin. Let’s use an example from the fashion industry, in which SKU-level refers to the red sweater I’ve mentioned earlier, this time in size 36.
Nearly all fashion and lifestyle companies I know now perform seasonal sell-through analyses by size and product group. This may represent a leap, but one that falls short.
Considering that 20-50 % of all products are sold at a discounted price at the end of their life cycle, in mid or end of season sales, comparing sell-in and sell-through by size at the end of a given season does not provide you with the right answers.
A simple change in time perspective can give you an entirely different point of view. Adjusting your next order based on those findings will push your sell-through, reduce your mark-downs, and therefore increase your exit margin.
Installing a pivot-based pre-mark-down-size-analysis on product group and POS level allowed a multi-channel fashion brand I worked for to increase its exit margin by 4% points without changing the buying budget, and another multi-brand retailer to increase their exit margin by 2% points.
3. Analyze Structure and Content of Product Master Data
Everyone is talking about big data and predictive analytics. But due to the high upfront investment this will remain unachievable for most companies for another couple of years. This does not, however, explain why so many companies don’t start making more efficient use of the data they do have available. Nor does it explain why they don’t invest in making their product master data smarter.
Every brand wants to know more about its consumers, but only very few invest in adding attributes to their product information that would help them analyze what types of consumer buy which product and why they do so. In the fashion and lifestyle industry such attributes might be:
- Degree of fashionability like basic, mid-fashion or advanced/image products
- Fit information like slim, loose, or fitted
- Body type (H, Y, X, A, O)
- Window items, marketing items or featured outfits
and many more.
Analyzing sell-through data by such and similar dimensions – combined with the average rate of sale, sell-through rate, mark-downs, exit gross margin, stock turn and price range analysis – can help product managers make much smarter decisions on structure and size of a collection or assortment.
Pushing sell-through and increasing the gross margin is not easy for a company that is already doing all these things. But my experience tells me that many companies out there don’t seriously work with the data on hand yet. Respectively, they don’t yet invest enough in making their data pool accessible and their methods of analyzing it smarter.
About the Author:
Heike Blank has worked for big organizations such as VF Europe and s.Oliver but also for niche brands such as Ecko Unltd. and Zoo York in top executive positions. Her extensive experience with opening and managing own retail, partner stores, concessions and shop-in-shops in 23 countries in Europe, the Middle East and Asia make her an expert in expansion. Get in touch with her via e-mail and read more from her here.