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Accurate Seasonal Demand Forecasting: Methods & Tools [+Examples]
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For ecommerce businesses, one of the most critical tasks in optimizing inventory management is forecasting future product demand. This is the case for industries spanning the B2B, B2C, and DTC sectors.
Traditionally, demand forecasting analyzes historical sales data using an algorithm to predict future sales. However, complicating this forecast is a variable that can be hard to predict — seasonality.
Seasonal inventory metrics are more challenging to pinpoint, and they can wreak havoc on inventory averages.
For most ecommerce brands, forecasting seasonal demand can feel a bit like throwing darts in the dark, hoping they land somewhere within the vicinity of the board.
With the proper forecasting techniques, methods, and tools in place, ecommerce businesses can say goodbye to the guessing games surrounding forecasting seasonal demand.
Learn how you can take back control over seasonal demand, optimize your ecommerce business, and boost growth.
Seasonal demand forecasting is the process of analyzing historical sales data to estimate future sales. It accounts for variables in demand based on the seasonality of a product or the seasonal behaviors of a target audience.
Seasonal demand forecasting facilitates accurate inventory planning while accounting for seasonal demand fluctuations.
For any ecommerce business, it is critical to understand the ebb and flow of product demand based on seasonality. In some cases, this process can seem fairly straightforward.
For example, a brand that sells Christmas candy will undoubtedly expect an uptick in sales leading into the holiday season. Naturally, this means they will need a special holiday inventory strategy in place.
But in other cases, seasonality can be far more complex. An ecommerce business selling flower seeds, for instance, might have a more difficult time pinpointing specific times of the year when sales will increase.
Depending on the region of buyers, the weather that year, and the demand for particular flower types, seasonal demand could create a significant issue with inventory planning.
Regardless, seasonal demand forecasting is central to an ecommerce business’ success. Insights based on seasonal demand allow brands to:
Seasonality is not always easy to predict, particularly in segments of the industry that are less dependent on calendar markers for their product demand.
To forecast seasonal demand, your business will need to consider specifics around your industry and your own sales history while accounting for the variety of variables that can affect seasonality.
Before you begin analyzing the seasonal demand for your products, it is essential to differentiate between seasonality and cyclical effects.
Seasonality is traditionally considered regular changes in your sales data that occur every calendar year. This is generally measured over one year and remains somewhat predictable.
Cyclical effects, however, can span shorter time periods.
This can boost or lower sales due to global changes, such as the pandemic or a decline in unemployment rates. These changes are often harder to predict and can cause unusual spikes and drops in sales.
The process can essentially be handled via one of 2 primary methods for demand forecasting: qualitative and quantitative.
Quantitative forecasting methods take existing data sets, including sales, revenue, financial reports, and digital analytics, to forecast a future projection. Using this data, statistical modeling and trend analysis are used to estimate future seasonality.
The strength of quantitative demand forecasting is that it relies on historical data, which can be a key indicator of seasonal patterns. However, this can be challenging for new companies with little historical data or those that see large fluctuations in seasonal demand that are less tied to the traditional calendar year.
Going a step further, qualitative forecasting considers the wider economic climate. This method focuses on pairing historical data with industry expertise, predictions about future changes to a business’ specific industry, product life cycles, and more. The strength of this method is that it accounts for a wider breadth of variables. However, it requires extensive resources and relies on lots of future projections.
Regardless of what method you use to forecast seasonal demand, you will need to gather as much data as possible on your business historicals. You’ll need sales data, financials, inventory information, and storefront analytics.
Additionally, you will need to get granular with your analysis. If you sell across multiple regions and channels, you will need to analyze your data in segments. That’s because seasonality can affect these different segments of your audience in different ways. Too broad of analysis can leave you with watered-down data.
If you are a newly formed business or want to forecast demand for a new product, you will need to use demand patterns from available market data or similar products.
As you begin to plug your data into your forecasting model, it is critical to take the time to analyze the results properly. In some cases, what may look like seasonality is not the underlying driver. Be sure to look for additional variables that may be skewing your seasonal forecast.
Pay attention to marketing campaigns, media coverage, world events, and industry-specific variables.
Improving seasonal demand forecasting requires an extensive amount of data. This is what makes the process for many ecommerce businesses cumbersome and overwhelming.
This is where the Cogsy + Extensiv integration can help.
For starters, Cogsy allows you to replace manually updated spreadsheets with predictive sales and inventory intelligence. You can turn this data into automated demand planning and purchasing. This helps you ensure that you have the right stock on hand, just when you need it. Also, you can view how seasonality will affect revenue and your inventory levels.
Paired with this tool, Extensiv Order Manager learns from your inventory replenishment patterns to make smart recommendations, so you never miss a sale. Extensiv will automatically issue purchase orders based on current sales velocity, lead times, and seasonality.
To demonstrate, let’s take a look at how a business that recently implemented both tools improved demand forecasting with a creative backorder model to optimize seasonality.
Caraway, a DTC cookware brand, was launched in November 2019. The company’s products’ modern design and aesthetics made them instantly popular.
This allowed the company to raise $5.3M in seed funding to support the brand’s fast-paced growth right from the start.
Then the pandemic hit. Not only did online retail sales increase 32.4% year over year in 2020, but the home goods space saw an unprecedented 51% YoY increase.
For Caraway, this was great news, but with this good news came an incredible challenge.
Simultaneously, increased customer demand and pandemic-related limitations caused the global supply chain to hit major roadblocks leading to constant issues with products being out-of-stock.
Caraway rose to the occasion and decided to implement a backorder model.
However, they needed an inventory management infrastructure that could eliminate manual forecasting while selling effectively on backorder.
One of the Caraway team’s biggest struggles was manually updating spreadsheets that contained data about available inventory, products that needed to be reordered, and general forecasting.
This data was almost immediately outdated, causing them to learn about inventory issues too late.
To solve this data issue, they partnered with Cogsy. Cogsy helped Caraway monitor and accurately forecast its inventory and communicate with customers about when to expect products to ship.
With this information, the cookware brand achieved realistic expectations around delivery dates, encouraging customers to purchase out-of-stock products and fueling future growth.
During peak seasons, this tool continues to prove invaluable, allowing Caraway to reduce missed opportunities based on seasonal spikes.
When it comes to different demand forecasting methods, the most suitable one for seasonal products will depend on your specific industry. However, relying on statistics is often the most detailed, reliable, and cost-effective strategy. Statistical forecasting allows you to pair regression analysis with trend projections to predict demand based on seasonal changes.
The simplest forecasting method is trend projection. This forecasting method uses your past sales data to predict future demand and sales. However, relying on the trend projection method alone can lead to inventory forecasting errors because it does not account for the number of variables. This can lead to misidentified seasonal trends.
A seasonal demand curve represents the uptick and downward turn of product sales based on seasonality.
Matthew Rickerby is the Director of Marketing at Extensiv Order Manager (formerly Skubana), the leading solution for multichannel, multi-warehouse DTC brands. For the past 10 years, he’s covered ecommerce topics ranging from SEO to supply chain management.
Find out more at extensiv.com
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