Here’s How Retailers Can Navigate an Increasingly Volatile Back-to-School Season

The long-standing belief that back-to-school shopping is a multi-month window is outdated. In reality, the back-to-school period has a slow ramp-up, yet a surprisingly brief peak.

The fleeting and uncertain nature of the back-to-school season is due to several factors. The exact parameters of the period are hard to nail down because different counties, states, and school systems have slightly different start dates.

Leveraging back-to-school demand is a delicate balancing act — miss it and you’ll have an inventory surplus which can burn a lot of margin and produce unnecessary CO2 to offload to other branches or discounters.

We suggest that retailers’ strategic focus not just be on pencils and notebooks, but more on sweaters, scarves and coats. Traditional thinking defines the back-to-school season as the summer months. However, this period is really a mild incline — the peak comes when the weather cools enough for students to consider changing their wardrobes.

Predicting this can be difficult, not just because of the uncertainty that comes with forecasting the weather, but also because a one-size fits all approach can’t be taken — a fall outfit in Massachusetts isn’t the same as a fall outfit in Florida. Accessing accurate weather data in real time is crucial to navigating this period. An unusually breezy end of summer can have drastic consequences for a retailer’s margins.

There’s a lot of volatility on a granular level — the younger generation is much more sensitive to fashion trends. These rapid cycles of what’s in style affect fast fashion, as luxury brands aren't swayed by color trends and the influence of particular models. The demand for in-style clothing is exacerbated by students who have to travel far from home to go to school—these items have to be purchased on short notice.

The post-pandemic period has made the back-to-school season all the more lucrative. While many students have returned to in-person education during the past couple years, this will be the first semester where restrictions are fully lifted on large social gatherings and various extra-curriculars. There’s an incredible amount of pent-up demand for social activities — look at corporate conferences in 2023, they’re bigger than ever. This bottled-up desire to socialize will translate to a desire for cosmetics and fashion.

Inventory

Retailers can better brace for the floodgates of consumer demand to open by overhauling how they cluster inventory. An example of traditional clustering practices is dividing inventory into rural and urban. However, clustering for the back-to-school season is far more complex—driven by the localism of fashion trends and weather.

Retailers would do well to first consider understanding the seasonality of each product in each store on a hyper-localized level. Zoom in closer than just the differences between Massachusetts and Florida, as even south and north Florida have different seasonal buying patterns. Then cluster by category in a meticulous fashion—prioritizing an order’s type, size, location of storage, and time of shipment. If done incorrectly, the window of demand will be closed by the time it takes to ship surplus from one warehouse to another.

Real-Time Data

This feat is only achievable if retailers have access to real-time data. Even if everything is planned perfectly, an unexpected weather event may force a retailer to change course—this can be done with in-depth weather forecasting and alerts that suggest inventory change.

Artificial intelligence aids in making these complex calculations, as the human brain does not have the capacity to understand the seasonality of every product at every individual store. A.I. is key in creating clusters for each category.

No longer will organizations use the outdated practice of keeping 20% in a warehouse and spreading the rest proportional to store sales. Nor will retailers have to wait for overnight ERP information to course correct —they’ll become proactive, not reactive. This machine learning powered inventory management system would ideally be part of a larger data-driven retail planning system.

Omnichannel Integration

Omnichannel integration is the last piece of the puzzle — continuous commerce gives the customer the opportunity to buy and return products from wherever they please. A real-time data layer is crucial for omnichannel integration because a customer might buy a product in-store and return online, or buy in-store and make a continuation purchase online.

A retailer that makes the connection between these two purchases can create additional marketing opportunities through personalized discounts and suggested purchases.

If retailers are able to use a data-driven approach to managing back-to-school inventory, companies can capitalize on the back-to-school season—boosting profits, creating sustainable business practices, and elevating the customer experience.