Using the right prediction-based tech tools brands can achieve better product assortment, higher conversion and an improvement in full-price sales during a season
Today’s customers want it all, here and now. As retail gets increasingly digitized and personalized, successful retailers have realised that adopting technology is necessary to thrive.
Considering that there are endless avenues for technological improvement, but a limited IT budget, the question that businesses face is: Which technologies should be adopted to make a substantial difference to the business?
Conversion matters
Traffic is the ultimate constraint of retail according to every retailer I have interacted with over the past 15 years. It is also among the most difficult variables to predict in the ever-changing world.
Retailers who have the built-in capabilities to convert higher percentages of their traffic into buyers, with both a higher ticket size and a higher margin have been the most successful.
The need for a new model
The pandemic has accelerated many trends that were underway even before the outbreak.
One of the most impactful of these was the shifting of customers to online platforms. In recent years, e-commerce in India has grown significantly. Online shopping festivals such as Big-Billion Days and Great Indian Shopping Festival have contributed to further growing the pie on digital platforms.
Additionally, with an increase in the number of small brands especially in the Direct to Consumer (D2C) e-commerce space, large numbers of customers are shifting from offline to online sales channels.
For traditional brick-and-mortar businesses, this transition means competing with established e-commerce platforms on price and service, which impacts store traffic and in consequence, profitability. In addition to offering customers an array of differentiated products, e-commerce brands do so at a cheaper price.
Furthermore, the management of supply chain and logistics has become unprecedentedly challenging with global supply chains facing delays and cost issues. As a result, long-term predictions are becoming increasingly challenging, and forecast accuracy is consistently deteriorating.
As a growing number of retailers in India are expanding into tier 2 and tier 3 cities and opening new stores, planning inventory becomes almost meaningless as businesses are not even certain it will arrive at their stores in time for the start of the season.
Moreover, current social market forces are causing an increasing awareness of the environmental costs caused by excess production. With today’s poor forecasting capabilities, assuring sustainability becomes a challenge.
In this chaotic reality, short-term predictions’ ability to control inventory, sales, and turnover during the season has become essential.
The catch
Fashion seasons are generally planned well in advance. Based on my work with professionals in this field around the world, including in India, I have noticed some key factors influenced by this workflow.
Usually, retailers determine the styles, quantities, and other key parameters, such as the dominant fashion trend and the distribution plan to stores, six to 12 months before a season starts.
They are fully aware 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% sell-through. Yet, only 40%-75% of sales are at full price, the rest are sold at deep discounts during the end-of-season sales (EOSS).
To understand why full-price sales in fashion are so low, one doesn’t need to be an expert. When the assortment at a store is being refreshed based on the predictions made long before the season started, it is inevitable to have a high share of slow-moving products on the shelves. Often, as EOSS begin, up to 50% of a store’s assortment contributes to less than 5% of the sales over the season.
Over time, the slow-moving products accumulate, block shelf space available for new collections and reduce their attractiveness. It is these slow movers that drive the deep discounts during EOSS.
The fashion industry also generates ‘best sellers’. These are fast-moving products. Fast movers are products that sell faster than predicted. They run out of stock quickly. These are the styles that we as consumers like on display only to learn that our size is already out of stock. As the inventory of slow movers piles up, that of fast movers rapidly depletes, affecting the store’s ability to convert traffic into buyers.
EOSS gives retailers the opportunity to liquidate the season’s slow movers and start the next season afresh.
Sticking to the mindset of looking at slow movers as the villains, will prevent us from breaking out of the liquidation loop. It will only drive us to criticize our planning and try to optimize within the already huge noise. One needs to look at reality from a higher vantage point to understand the cause and effect.
Slow moving here fast moving there
The terms fast movers and slow movers are relative to location. Indeed, there are products with inferior designs or poor pricing that cannot be sold without a deep discount. Yet, the majority of slow movers in a store are actually average to best sellers in other stores. That is, while these products block the shelves of some stores, they are needed in other stores, where they ran out of stock too early in the season.
Having the right algorithm to identify such slow movers and fast movers in real-time as soon as the season starts can enable us to take immediate action to ensure that the right product arrives at the right place and time, maximizing its full-price sell-through.
Making short-term decisions
With big and small retail brands in India shifting from being purely brick-and-mortar to having an omnichannel presence, it becomes important for retailers to pay attention to and focus on short-term predictions and immediate decision-making. Based on my experience, one of the best ways to accomplish this is to shift the 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 a store’s merchandising processes. The improved precision of the short-term predictions directly impacts sales and profits in the season and optimizes replenishment, inter-store transfers of fast movers and liquidation of slow movers, improving the overall store assortment in the season. They enable the right products to 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 players in India, I know for a fact that the usage and implementation of such tools have led to an increase of 5%-10% in sales per store during the entire year. It has also led 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 toward 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.
A version of this article has appeared in the Jerusalem Post.
