Key takeaways
- Seasonal demand forecasting can be used year-round, not just during peak or holiday periods.
- Seasonal forecasting focuses on time-of-year patterns rather than recent sales trends alone.
- Seasonal forecasts rely on sales from the same period in prior years, unlike default forecasts that emphasize recent performance.
- This approach works well for relaunching intermittent items and for year-round products with predictable seasonal demand.
- Seasonal forecasting helps retailers prepare for promotions and demand spikes while reducing overstock and stockouts.
When exactly is the best time to use a seasonal forecast?
Is it only in the fourth quarter? Back-to-school time? The summer? Only certain months of the year?
Let’s take a look at what exactly a seasonal forecast is, and when the best times to use it is. You will see that it is definitely not just for the Christmas holidays or peak times of year. If you properly mine your data, you will see that seasonal forecasting is something that you can use year-round to maximize profits and optimize your inventory.
What is seasonal demand forecasting?
The forecast describes predicted future sales.
The forecast is calculated using the sales velocity and the sales trends in recent months (are sales increasing or decreasing?). Sales velocity is the rate of sales excluding out of stock days. Seasonal products emphasize the sales trends from the prior year rather than the most recent months.
With seasonal forecasting for June 2024, we will reference June 2023 and June 2022. A seasonal forecast picks up on sales that spike or dip at certain time each year. In this case, we want to know what effect the time of year has, rather than emphasizing what happened during the last few months.
Default forecasts are not the same as seasonal forecasts. The default forecast emphasizes trends in the last few months, but the seasonal forecast looks sales twelve months ago.
When to use seasonal demand forecasting
Use case for items intermittently available
You carry t-shirts in your eCommerce site. In February, you relaunch your famous “Erin Go Bragh” t-shirts for a St. Patrick’s Day promotion. How will they sell with this relaunch after not being available for sale for several months?
In order to forecast that, you need some sort of sales history, preferably for that exact item twelve months prior. You can see the number of units sold or any growth rate year over year. This is one easy way to forecast holiday- or season-specific items.
Items available year-round with a different selling pattern
Imagine you carry grilling accessories in your inventory. They sell well during Memorial Day and other summer holidays. These are not fourth-quarter items, but are usually tied to specific times of the year.
If you use regular default forecasting, you will miss out on some of the variations that you see at those peak times of year. The seasonal forecast can pick up on the summer holidays for the following year.
If you are a retailer is going to a trade show, having a unique pop-up, or a big promotion every year around the same time, these are also times to consider seasonal forecasting. Your sales are higher in those months, and that can be a time you will want to use that seasonal forecast so you are ready for spikes in demand.
As you can see, seasonal forecasting is not just for the fourth quarter. Take into consideration re-launched merchandise, intermittent-selling items, or other big promotions. You will see careful seasonal forecasting will help you be better prepared to sell your inventory at special intervals and avoid overstock or stockouts, thereby maximizing your revenue.
How to forecast seasonal demand
Forecasting seasonal demand starts with understanding how timing influences buying behavior. Instead of relying only on recent sales activity, this approach looks at how products performed during the same period in previous years. By focusing on recurring patterns tied to the calendar, seasonal demand forecasting helps you plan inventory more accurately and prepare for predictable shifts in demand.
1. Review historical sales from the same period
Start by analyzing sales data from the same month or season in prior years. Comparing year-over-year performance helps reveal predictable demand patterns tied to timing rather than short-term fluctuations. This is the foundation of effective seasonal demand forecasting.
2. Account for stockouts and irregular events
Adjust historical data to remove days when items were out of stock, as these gaps can understate true demand. You should also account for one-time anomalies such as supply disruptions or unexpected promotions that could skew results.
3. Identify recurring seasonal patterns
Look for consistent spikes, dips, or plateaus that repeat at the same time each year. These patterns often align with holidays, weather changes, promotional cycles, or annual events and are central to most seasonal demand forecasting methods.
4. Apply seasonal forecasts to inventory planning
Use the adjusted historical patterns to plan future inventory levels, purchasing, and replenishment. Seasonal forecasting is especially valuable when preparing for known demand shifts, helping you balance inventory levels and avoid overstock or stockouts throughout the year.
Common seasonal demand forecasting methods
Seasonal demand forecasting relies on different methods depending on the type of products you sell, the consistency of your sales history, and how predictable your demand patterns are. Some approaches focus on historical performance, while others account for growth trends or external influences.
Year-over-year historical comparison
This method compares sales from the same period in previous years to identify recurring seasonal patterns. By focusing on how an item performed during the same month or season, you can better understand the impact of timing on demand without overemphasizing recent fluctuations.
Seasonal index analysis
Seasonal index analysis measures how demand for a product rises or falls relative to its average sales level throughout the year. These indexes help quantify the strength of seasonal effects and make it easier to adjust forecasts for peak and off-peak periods.
Trend-adjusted seasonal forecasting
Trend-adjusted forecasting combines long-term growth or decline trends with seasonal patterns. This approach is useful when demand is changing over time but still follows a predictable seasonal cycle, allowing forecasts to reflect both momentum and timing.
Event- and promotion-based forecasting
This method accounts for demand changes driven by recurring events such as holidays, promotions, trade shows, or annual campaigns. By reviewing how past events influenced sales, you can better anticipate similar demand spikes in future seasons and plan inventory accordingly.
How Inventory Planner helps with seasonal demand forecasting
Seasonal demand forecasting becomes more reliable when historical data, trends, and inventory constraints are analyzed together. Inventory Planner improves seasonal forecasting by helping teams account for recurring demand patterns while still adjusting for changes in sales behavior and availability.
Inventory Planner helps with seasonal demand forecasting by:
Using historical sales data to identify recurring seasonal patterns
- Accounting for stockouts so forecasts reflect true demand
- Helping to plan purchasing and replenishment ahead of known demand spikes
- Improving visibility into upcoming inventory needs
- Enabling more confident inventory planning throughout the year, not just during peak seasons
Plan ahead with seasonal demand forecasting
Seasonal demand forecasting is not limited to peak months or holiday periods. When used consistently, it helps businesses anticipate predictable changes in demand, plan inventory with greater accuracy, and reduce the risk of overstock or stockouts throughout the year. By combining seasonal forecasting with historical sales patterns and growth trends, inventory teams gain a clearer basis for planning and purchasing decisions.
If you want to see how seasonal demand forecasting fits into day-to-day inventory planning, book a demo of Inventory Planner to explore how data-driven forecasts help you prepare for recurring demand shifts with confidence.
Frequently asked questions
What is seasonal forecasting?
– Seasonal forecasting is the process of predicting future demand by analyzing trends and sales patterns that repeat at the same time each year. It focuses on identifying recurring spikes or dips tied to seasons, holidays, or annual events rather than short-term sales trends.
Which forecasting system is used for seasonal marketing?
– Seasonal marketing typically uses demand forecasting systems that analyze historical sales data by time period and adjust for recurring seasonal patterns. These systems help teams anticipate when demand will increase or decrease and plan campaigns, inventory, and promotions accordingly.
Which demand forecasting method is best?
– The best demand forecasting method depends on your products, data quality, and sales patterns. Seasonal demand forecasting methods work best when demand follows predictable cycles, while trend-based or short-term forecasting may be more effective for non-seasonal or newly launched items.