As we hit the end of the 1st half of the retail year, many retailers are noting various headwinds that are negatively impacting their results and profitability outlooks. High inventory levels due to inflation, supply chain disruptions, and increased costs of everyday items like food and gasoline have all led to a decrease in demand for seasonal apparel. This has prompted many retailers to ramp up markdown efforts to clear seasonal inventory in time for fall product arrivals.
In addition to these external variables is the embedded influence that the weather has had on seasonal apparel. Earlier this spring, the U.S. had the coldest March in 3 years following by the coldest April since 1999.
Weather sensitivity – the variability in sales directly attributable to the weather — is at its height during these months. The cooler conditions this spring suppressed the year-over-year demand for Spring apparel by 90 basis points. For retailers, the financial impact was significant during the critical, high-volume months of March and April — a time when categories including sandals, short-sleeve shirts, shorts, sunglasses, and swimwear see between 9% and 15% of sales volumes directly influenced to the weather.
As temperatures have warmed in July, retailers were cutting prices on seasonal apparel at the exact same time when the weather is driving consumer demand for these items. This has served as a ‘double whammy’ to profitability with many retailers discounting items at the exact time when the customer was looking to make a purchase!
While retailers can’t change the past, savvy businesses will leverage the power of predictive demand analytics to offset the negative impacts they just experienced. The holiday season is right around the corner and many retail executives are grappling with several ‘unknowns’, making it difficult to forecast the holidays. The uncertainties of a recession, gas prices, interest rates, mid-term elections, COVID, labor availability, global conflicts, and other external variables will all influence the customer over the coming months and holiday season.
The weather, however, has known and measurable impacts. It is a measured demand signal that can and should be accounted for to better plan for retail sales as well as store traffic levels. Retail fall will have significant year-over-year demand opportunities as we are comping the same period in 2021 which was the 2nd warmest Q3 in 127 years. Of course, the timing and magnitude of these opportunities will vary. Regardless, retailers that leverage the power of predictive demand analytics to quantify the impact of weather for each product, time period, and location will help better plan for, allocate, and replenish seasonal apparel. The improvements to forecast accuracy and service levels will help to drive increased profitability and look to offset the uncertainties and headwinds retailers are facing. The weather-driven demand signal is out there. Which retailers are best positioned to hear it and act on it?
To learn more about how predictive predictive demand analytics can help your business, contact Planalytics.