What is full-price sell-through rate?
Full-price sell-through is the percentage of product units sold at their full price for a specific period. Often Full-price sell-through is measured at the end of the season – before the start of the EOSS – End of Season Sales.
Brands and retailers can anticipate customer demand and optimize stock levels by tracking sell-through rate. Demand forecasting is necessary for retailers to stock the right quantity of products at the right time. Accurate demand forecasting helps increase supply chain efficiency, ensure better utilization of working capital, increases availability, enhances full-price sell-through, and increase sales and profits.
According to Accelerated Analytics, 80% of the sell-through rate is considered a good benchmark. The average rate can be around 40% to 80%.
The average sell-through rate of fashion apparel ranges from 35% in the first eight weeks to 76% in 52 weeks.
Understanding full-price sell-through
Full-price sell-through is a function of the right product, at the right price, at the right moment, and in the right quality. The season, brand, type of garment, price and channel are significant factors influencing a fashion brand’s full-price sell-through.
1. There are two possibilities where full-price sell-through can be high.
a. Under-forecasting leads to insufficient manufacturing by the brand or understocking by the retailer, resulting in high sell-through but a potential loss of sales.
b. The retailer has been able to sell the goods at full price.
2. A low full-price sell-through indicates that the retailer has not been able to come close to the target set, resulting in high inventory at the end of the in-season period, leading to higher end-of-season discounts and lower margins.
3. If the full-price sell-through is low throughout, but the sell-through rate is high, it indicates that the customers liked the product but are sensitive to price. Purchases happen during the end-of-season sale at a markdown.
A 2018 survey by Coresight Research reveals that the average full-price sell-through among US retailers is 60%. The remaining units were sold at a markdown during the end-of-season sale.
US Retailers: Share of Respondent’s Company’s Total Inventory Sold at Full Price
1. Easy price comparison options available for consumers
A study by Mckinsey found that nearly 90% of consumers in the US shop from discount retailers. Internet penetration and mobile technology advancements allow consumers to compare prices across multiple eCommerce channels and retailers. The customers are aware of upcoming sales promotions and await special offers or end-of-season sales, making full-price sell-through challenging for retailers.
2. Lack of a data-driven pricing strategy
Pricing strategy depends on factors including initial markup, historical rates, internal margin and competitor prices. Without the support of a pricing engine, retailers may lack pricing discipline and end up considering only one or two factors for product pricing, reducing full-price sell-through.
3. Poor judgment of total inventory requirements
Lack of data and rigorous forecasting leads to incorrect inventory estimation and an inventory mismatch. Inventory mismatch occurs when a retailer buys a higher amount of the wrong product or an insufficient quantity of the right product.
4. Lack of understanding of customer preferences
Making informed merchandising decisions is critical in a shopping climate where consumer trends are changing rapidly due to the influence of social media and endless fashion choices. Incorrect judgment of customer preferences can lead to brands and retailers opting for markdowns due to poor sales. Brands should be equipped with data to anticipate micro-fashion trends and bring them to market quickly and cost-effectively.
5. Incorrect allocation of stock across different channels and stores
Multiple eCommerce channels, stores and warehouses create a complex retail environment. Lost sales due to stockout or dead inventory requiring markdown can be avoided through balanced inventory allocation.
External factors that impact full-price sell-through for fashion brands
a) Unpredictable weather: Weather is a critical driver of product demand, affecting the propensity to spend, the purchase channels, and apparel choice.
b) Covid-19: The pandemic impacted the demand for fashion, apparel and other non-essential retail goods. After two poor years, the sales figures for non-grocery businesses and brands are reaching pre-pandemic levels.
c) Shopping events (in addition to end-of-season sales): Annual events like Cyber Monday and Black Friday mean frequent discounts and promotions, which disrupt full-price sales.
d) Competitor discounts: Unexpected discounts from competing brands or rival retailers can force retailers to implement impromptu promotions.
1. Effective Inventory planning and optimization
Data-driven planning, procurement or manufacturing decisions will result in optimized inventory levels.
2. Planned pricing and promotions
Planning in-season promotional activities and pricing schedules using insights derived through analytics can help reduce markdown sales and increase full-price sell-through.
3. Customer-personalized assortment of products and brands based on current shopping trends
Merchandising and developing the right assortment of products and brands based on customer demand prediction will help retailers realize their targeted full-price sell-through rate.
4. Stock allocation to channels/individual stores
Fashion brands and retailers who leverage the omnichannel model to offer a seamless customer experience across mobile devices, computers, and brick-and-mortar stores, are better placed to gain from full-price sell-through.
Online orders can be fulfilled in various ways such as store pick-up after online purchase, shipping from stores, and using traditional fulfillment centers. These methods can disperse slow-moving inventory, thereby decreasing markdowns in stores.
5. Product replenishment and markdown optimization
Fashion brands and retailers should identify the best products to be replenished each season for full-price sell-through. Data-driven markdown optimization can reduce margin erosion.
6. Speed to market
Fashion and lifestyle brands have a high product development time and a relatively short sales cycle. According to a McKinsey report, top-performing fashion brands address this challenge using data analytics during the creative process. Full-price sell-through can increase if brands predict consumer preferences accurately.
Dynamic pricing helps a premium US apparel brand improve full-price sell-through by 5.28%
A leading US apparel brand in India, having over 300 stores and 1,500 products, wanted to improve full-price sell-through, reduce warehouse returns and maximize profits. Their in-season markdowns were based on fixed rules which did not adjust to the rate of sale and other market conditions. They also incurred a loss of profits due to over-promotion.
To arrive at the pricing algorithm, four key questions needed to be addressed:
This system was refined weekly, using the rate of sale data - incremental sales and inventory data. Rapidious recommended a markdown approach that prioritized buy-one-get-one deals over flat discounts.
By leveraging the Rapidious Dynamic Pricing Intelligence Engine, the client was able to increase the full-price sell-through rate by 5.28 % (from 64.1% to 69.4%). This also led to an increase in the accuracy and timeliness of markdowns, a reduction of staff hours spent on pricing decisions and enhanced automation.
Delivering the right product with the right stock levels at the right price, time and place is the recipe for a high full-price sell-through. This can be done with accurate sourcing/manufacturing, stock allocation, merchandising, and product/brand development. Fashion brands and retailers can leverage data, analytics and AI can to optimize processes in order to see tangible results and increased sales.
The Rapidious dynamic pricing engine along with its experienced team of data scientists can help in your mission to achieve higher full-price sell-through.
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