How Post-Covid-19 Retail Challenges Brands to Move Beyond Traditional Channels of Innovation – The Need of the Day
Modern retail, like many other domains and fields, is no longer just a simple game of supply and demand, if it even ever was. In our current reality, customers want it all, here and now, as more and more services are going through digitization and personalization. Retailers are fully aware that the use of technology is no longer a choice but a necessity. With endless possibilities of technological improvements across different domains in retail and a limited IT budget, the million-dollar question is what kind of technology to adopt to make a real difference in business. Per my experience in the last 15 years, every retailer agrees that the ultimate constraint of retail is traffic, yet traffic is among the most difficult variables to predict in an ever-changing world facing the Covid-19 pandemic. Successful retailers, however, are those that have the built-in capabilities to convert higher percentages of their traffic to buyers, with both a higher ticket size and a higher margin.
The need for a new fashion & retail management model in today’s post-Covid-19 world
As the whole world has changed as a result of the Covid-19 pandemic, so have the retail and fashion industries. The pandemic has accelerated some trends that already started prior to the outbreak. One of the most impactful changes was the rapid movement of customers to online platforms. For traditional brick and mortar brands and retail chains, this transition means a fierce competition with the well-established e-commerce platforms on price and service, which can make traffic at their own retail shops erratic, directly impacting store profitability. Moreover, these changes highlighted other challenges, from the need of retailers to quickly react, to preferences of customers constantly moving from one trend to the other, and even labor shortages making it difficult for stores to stay open.
Additionally, supply chain management has become unprecedentedly challenging, as the global supply chain is facing increasing delays and costs. As such, any long-term prediction is becoming more and more challenging and forecast accuracy is constantly deteriorating. For example, how can fashion retailers plan their inventory, when they are not even certain it will arrive at their stores in time for the start of the season? In this chaotic reality, short-term predictions, and the ability to control inventory, sales and turnover during the season, becomes essential.
In addition, in a world that is becoming greener and greener, there is an increasing awareness of the environmental costs caused by excess production. With todays’ poor forecasting capabilities, sustainability cannot be assured.
The Full-Price Sell-through Catch
In general, seasons in fashion are planned well ahead. Following my extensive work with professionals in this field all around the world, I have noticed some key factors influenced by this workflow. Retailers determine styles, quantities, and other key parameters, such as the dominant fashion and the way clothes will be distributed between stores, usually 6-12 months before the start of a season. However, retailers know that it is impossible to sell 100% of the merchandise at full price, for a variety of reasons. Efficient fashion retailers can end the season with 85% or even 90% sellthrough. However, only 50%-75% of sales are at full price and the rest are sold at deep discounts during the end of season sales.
One doesn’t need to be an expert to understand why full-price sales in fashion are so low. When a wide assortment is being constantly refreshed at the store based on predictions, which were made long before the season started, a high share of slow-moving products on shelves is inevitable. In fact, it is common to see that as the end of season sales begin, up to 50% of a store’s assortment contributes to less than 5% of the sales over the season. The slow- moving products accumulate over time, reduce the shelf attractiveness and block the shelf space available for new collections. The slow movers are also the main driver for the deep discounts we are used to seeing on “the end of season” signs. But fashion also generates “best sellers” which are fast-moving products. Fast movers are the products that sell faster than originally predicted and hence they run out of stock quickly. Those are the styles we liked on display, but quickly learned that our size is already out of stock. Naturally, while the slow movers’ inventory piles up in the season, the fast movers’ inventory quickly depletes, reducing the store’s ability to convert traffic into buyers.
The end of season sale is the retailers’ opportunity to liquidate the season’s slow movers and start the next season afresh. We cannot break out of this liquidation loop if we stick to the mindset that slow movers are the villain. This will only drive us to criticize our planning and try to optimize within the already huge noise. One needs to take a look at reality from a higher vantage point in order to understand the cause and effect.
One Store’s Trash is Another Store’s Treasure
Slow movers and fast movers are mostly location-specific definitions. Indeed, there are bad products with wrong designs or wrong pricing that cannot be sold without a deep discount, yet the majority of slow movers in a store were actually average to best-sellers in other stores. That is, while we saw these products blocking the shelves of some stores, they were actually needed for sales in other stores where they ran out of stock too early in the season. If we had the right algorithm to identify these slow movers and fast movers in real-time as soon as the season started, we could take immediate action to ensure that the right product arrives at the right place and time to maximize its full-price sell-through.
Short-term Prediction, Automation, Optimization
The numbers and current trends demonstrate the importance of retailers paying attention to and focusing on short-term predictions and immediate decision making. Based on my experience, one of the best ways to accomplish this is to shift our focus from having a better season plan to the adoption of in-season adaptive technologies. Such technologies are based on prediction tools that assist retailers in short-term prediction, and in automating stores’ merchandising processes. The improved precision of the short-term predictions directly impacts sales and profits in the season, and optimizes replenishment of inter-store transfers, fast movers’ management, slow movers’ liquidation, and the overall store assortment in the season. The right products go to the right place at the right time, resulting in a higher sales conversion rate in the season.
After more than a decade of working in retail, and following hundreds of conversations with leading retail actors around the globe, I know for a fact that the usage and implementation of such tools has led to an increase of 5%-10% in sales per store during the entire year, and to an improvement of 20% in season full-price sales. Moreover, since actions lead to immediate results, performance can be accurately measured in real-time.
Beyond the financial improvements, with this technology, retailers can proceed faster towards a zero-waste policy, as such algorithms help them to leave no cloth behind at the store, and be a step ahead of the conventional retail industry.
Roei Raz is the VP Sales at Onebeat and a former consultant for leading retail groups around the globe. Raz holds a B.Sc. in Industrial Engineering and an MBA, both from TAU.
