The pandemic – and the associated supply chain crunch – accelerated the speed at which fashion retailers need to push through their digital transformation. Bare virtual shelves and unpredictable backorders have exposed the fact that too many retailers are still using historical data sets to forecast demand, when they should be using real-time intelligence to tame growing volatility. Despite retailers having sophisticated statistical models and advanced tools to forecast demand, the product data that they often use as a key input is often thin, inconsistent, or even inaccurate.
So, can better product attribution data save the day?
The Problem With an Out-of-Sync Supply Chain
The e-commerce share of fashion sales jumped from 16% to 29% during the pandemic, and with recent shifts in buying behavior looking permanent, the forecast continues to be positive. According to a McKinsey survey, 77% of fashion executives said they expected their online business to grow by 20% or more in 2021. The problem is that not enough retailers have the infrastructure to handle the increase in demand or meet the investment required. When the supply chain is out of synch with the front end customer experience, two things can happen:
- Out of Stock: Return rates in fashion retail are already as high as 50%, according to Shopify. But when a customer cannot even order the item they want to begin with, that negative customer experience can be just as costly.
- Overstock: Stock sitting on shelves is bad for business, and not just through high storage costs. When a retailer is too slow to respond to trends and misses the fashion window, the only option to clear the shelves is to mark items down at the end of the season. Bear in mind that just 60% of garments are sold at full price as it is, and the damage to the bottom line can be catastrophic.
Surging Demand Will Continue to Impact E-Commerce
We’ve already established that consumers are shopping more online than ever, particularly since the onset of the pandemic. In the US alone, 33% of retail sales are now through e-commerce, and fashion has earned its share — with the e-commerce fashion market expected to be worth $672 billion by 2023. A staggering 43% of consumers who had never purchased clothing online did so for the first time in 2020, and a significant proportion of those took advantage of disruptive models in retail.
With new ways of shopping come new expectations for delivery. Amazon Prime has set the benchmark for exceptional convenience at unprecedented speed, and the same-day delivery market will be worth $9.6 billion in the U.S. by 2022.
All these trends point to a sector that is evolving at a furious pace. Herein lies the problem. Retailers simply can’t respond with the data they have right now. Most lack the data visibility they need to see how various trends and styles resonate with customers or offer personalization to build customer loyalty and tame the serial returners.
How Granular Product Attributes Make a Difference
By leveraging granular, intent-driven product attributes, retailers can make significant progress in demand forecasting and supply chain optimization. With the data visibility and insight they need, these retailers can accomplish three urgent objectives:
- Replace wholesale pre-orders with a leaner, demand-led, made-to-order model that empowers product development, fueled by AI to launch lines that are guaranteed to sell out fast.
- Reduce assortment complexity, clean up the product catalog, and use data analytics to identify the most profitable SKUs.
- With many brands scaling back the number of seasonal collections a year, data from granular product attributes can help extract more value from those that remain. With better analytics, retailers can lean more towards price optimization and dynamic discounting rather than big end-of-season markdowns. This is of huge value to a retailer’s bottom-line.
Instead of embarking on a futile chase to catch up with historical trends, retailers can use granular product data to pivot to the much-heralded zero-based supply chain management. Crucial to the shift is better visibility of costs, demand, resources and capacity. By unlocking the insights of granular product attributes, retailers can:
- Invest manufacturing and fulfillment budgets in the products that generate the most revenue.
- Improve demand forecasting, particularly around seasonal spikes or emerging trends.
- Gather proxy products, defined through AI-powered computer vision, to accurately forecast demand for brand new product lines.
- Deliver personalized experiences to open up the option of a subscription-forward business model.
- Nurture an engaged, active, and loyal customer base that is more likely to buy new product lines and less likely to return items.
The supply chain is only as robust as the data that feeds into it at the source. In retail, granular product attributes can help reduce mountains of unsold inventory in the warehouse. The more insight the retailer has into their customer’s preferences and the intent and context behind their choices, the better positioned they’ll be to anticipate demand and eliminate the uncertainty that causes overstocking. The future of retail is not off-the-shelf, but rather, on-demand.