Why is Seasonal Demand Forecasting important?
Traditionally retailers have relied on past sales history to make predictions for future demand.
Though this strategy has its limitations, it can work for staple inventory with consistent sales, like toothpaste or bread.
The volatile nature of seasonal inventory leaves traditional methods of forecasting ineffective, and in fact, harmful. Without an effective method to predict seasonal demand retailers will always run out of stock and lose sales or be forced to mark down products at the end of the season.
That’s why it’s important for all retailers to understand what seasonal demand forecasting is, why it’s so challenging and how retailers in 2021 are overcoming these challenges to increase their bottom line.
What is Seasonal Demand Forecasting?
Simply put, seasonal demand forecasting is a type of advanced demand forecasting that takes into account the many complex variables of predicting the performance of seasonal products (non-evergreen products).
It’s easy to understand evergreen products, these products have a steady pattern of sales from month to month and year to year.
Seasonal products, on the other hand, either have disproportionately high spikes in demand or only sell during particular periods of time.
The lifecycles of such inventory can range from a few months to a few days.
Let’s take a look at 3 examples of seasonal products in the image below:
Seasonality can depend on product type, on whether it is event-driven or weather-driven. It can also depend on whether it is a moving holiday like Easter.
Seasonal products can start selling earlier than expected or still have demand for longer than in previous years.
Why is Forecasting Seasonal Demand so Challenging?
These three examples hint at the forecasting challenge retailers face.
Some challenges of forecasting seasonal demand include:
Short Life cycles and seasonal peaks
By the time a retailer recognizes a problem with a seasonal forecast, it is too late.
Seasonal product lifecycles and the seasons themselves are too short to take corrective action.
Retailers either lose sales and customer satisfaction due to inventory shortages or face drastic and expensive markdowns.
Seasons are not the same year-to-year
Product seasons often do not follow perfect year-over-year patterns. Easter and other lunar holidays fall on different days, weeks, or even months.
In fact, there are some events that happen in some years, but not in others.
Using past sales data to forecast seasonal products is unreliable and may have an unexpectedly costly sales season.
No sales history
More often than not, this year’s seasonal lineup differs from previous years.
Changing customer tastes, for instance, requires new Halloween costumes each year or a brand new seasonal product or even entirely new product lines that are trending this year.
Without historical numbers, retailers can feel like they are planning in the dark.
Hidden seasonality
Sometimes a product category that does not have a seasonal peak during a particular time of the year, may still have individual products that do have demand spikes during this time.
Products you would otherwise consider evergreen may see sales uplifts because they are not obvious candidates as seasonal products as most retailers don’t work at the product level.
For example, electrical may not initially appear as a Christmas-driven category, however, power-bars certainly see a boost in demand during the holiday season
This leaves retailers with lost sales as not enough product is brought in to meet demand.
How Retailers are Forecasting Seasonal Demand in 2021
Today’s digital transformation has highlighted a growing gap between traditional retailers and those that are analytics-driven.
In fact, Mckinsey’s research found this gap is already over 68% and growing exponentially.
Let’s take a look at the different ways retailers are approaching seasonal inventory from least effective to most effective:
ERPs, Business Intelligence… and of course, Microsoft Excel
This strategy requires minimal financial investment and takes little time to implement since there is commonly a spreadsheet-based system (often Microsoft Excel) already in place.
This is a preferred approach for many retailers because it allows for flexibility as users can adapt a spreadsheet to their own operations process and unique constraints.
Retail buyers, planners, and inventory managers also use their ERP system along with business intelligence and visualization dashboards like Tableau to analyze trends and make decisions.
Unfortunately, consolidating multiple sources of data is very time-consuming forcing retailers to make decisions at a higher level. Moreover, this approach is not very dynamic which is not ideal when working with seasonal inventory.
Finally, this method can also be limiting as growing retailers will find it difficult to scale.
Analytics-Driven Seasonal Demand Forecasting
A leading strategy is investing in advanced analytics solutions.
Retailers use predictive analytics and AI to forecast seasonal demand more accurately.
These systems are able to account for the factors that influence product demand such as seasonality, price elasticity, geo-demographics, and dozens of other variables. In addition, they are also able to optimize seasonal products at a SKU/store level which is virtually impossible to do manually or with traditional retail systems
Retail AI can identify products that have hidden seasonal demand retailers never considered previously. In addition, retailers are able to lock in their limitations, preferences, and goals using business rules to configure the system to their own business.
The benefit of advanced analytics is that it is scalable because data is processed automatically, consistently, and accurately.
By optimizing for seasonal demand retailers are not suffering lost sales, nor grappling with unnecessary markdowns.
It is important to note that an advanced analytics solution requires some change management to deploy effectively.
What Modern Retailers are Using for Seasonality Forecasting
Simply put, meeting the challenges of seasonal demand forecasting requires careful consideration.
Regardless of where on the spectrum of traditional business intelligence and advanced analytics retailers find themselves, the right approach to forecasting seasonal products should:
- Fit the size and budget of the retailer
- Incorporate the goals and limitations unique to your organization.
- Dynamically adapt with the year-to-year changes in assortments, timing, and market conditions.
- Scale with your retail store counts, assortment sizes, and omnichannel strategies.
- Balance long-term investments with the reward of accurate seasonal demand forecasts.
As the old saying goes: “There are many ways to layout a garden, but the best way is to delegate it to a gardener.”
