Imagine this: we are approaching peak seasons like White Friday, National Day, or year-end sales, and you are restocking your inventory. You order the same amount you always do when replenishing stock – but when the season begins, your products almost immediately sell out.
The following year, around the same time, you anticipate that the season will be as busy as the previous year and definitely don’t want to run out of stock again.
So, you review your records to see how many sales you made and how many backorders you had last season, and you use this data to estimate how much stock you will need. In the end, you order twice the usual amount and successfully get through the holiday season without any issues.
This is a classic example of demand forecasting. By making data-driven predictions about sales, customer interest, and order volume (rather than guessing blindly), e-commerce brands set themselves up to satisfy customers, save money, and streamline their entire supply chain.
In this article, we will dive into the concept of demand forecasting, the factors that influence it, how to forecast accurately, and how experts like Diggipacks can help.
What is demand forecasting?
Demand forecasting is the process of estimating how much demand there will be for a product in the future. Demand is typically measured in sales, so the goal of demand forecasting is to predict how many units of a particular product you will sell in a given period.
If you forecast demand accurately, you will have enough stock to fulfill all customer orders without overstocking (which increases inventory holding costs) or understocking (which can lead to stockouts and backorders). You will also be able to improve decision-making across your supply chain, warehousing operations, and inventory management.
To forecast demand as accurately as possible, many brands track historical sales and order data and analyze it for patterns that can help them predict what might happen in the future.
Types of demand forecasting techniques
There are several methods businesses can use to forecast demand. All demand forecasting models rely on data and analytics over specific time periods.
Macro-level
Macro-level demand forecasting focuses on general economic conditions, external forces, and broader factors affecting commerce. These factors help companies understand portfolio expansion opportunities, market research insights, and other market shifts.
Micro-level
Micro-level demand forecasting is specific to a particular industry, company, or customer segment (such as studying the demand for perfumes among customers).
Short-term
Short-term demand forecasting looks to predict demand over a short period, usually less than 12 months. These forecasts are often used for day-to-day operations and inventory management.
Long-term
Long-term demand forecasting aims to predict demand over a longer period, usually a year or more. Long-term forecasts help a brand identify seasonal patterns, plan its growth trajectory, and invest in its overall business strategy.
Importance of demand forecasting for e-commerce
Without demand, there is no business. And without a thorough understanding of demand, businesses cannot make the right decisions about marketing spend, production, staffing, and more.
Budgeting
When you know how much inventory you will need to meet demand, you can create a budget and stick to it. Demand forecasting helps brands make strategic financial decisions that protect profit margins, improve efficiency, and decrease overall spending.
Demand planning and production scheduling
Accurate demand forecasting and inventory control help plan production so that the right amount of stock is available when customers need it.
Storing inventory
The more inventory you have, the more expensive it is to store. With accurate demand forecasting, you can avoid overstocking and the high costs of holding excess inventory.
Developing a pricing strategy
Demand forecasting not only helps maintain optimal stock levels, but it can also enable you to price products based on demand.
Factors influencing the customer demand life cycle
Many factors can influence demand or cause it to fluctuate. These factors vary from one business to another or across industries.
Seasonality
Seasonality refers to changes in order volume during specific periods. Seasonal brands may experience peaks and dips in demand around certain periods or events.
Competition
Competition affects demand by providing more options for your customers to choose from. When a new competitor enters the market, it can unexpectedly affect demand. A flexible demand forecasting model helps you respond quickly.
Types of products
Demand forecasting varies significantly between different products and services. Therefore, it is essential to understand your customers’ lifetime value and average order value to improve demand forecasting.
Geography
The geographical location of your customers can significantly affect demand forecasting. For example, if your brand sells agricultural tools, you will likely see higher demand in areas with extensive farming activities.
With this understanding of the market and potential opportunities, businesses can grow, formulate competitive pricing, employ the right marketing strategies, and invest in their growth.
Setting objectives
Demand forecasting should have a clear purpose. At its core, it predicts what customers will buy, how much they will buy, and when they will buy it.
With this in mind, start the forecasting process by choosing:
- The time period for which you are forecasting demand (e.g., the upcoming fiscal quarter)
- The specific product or general category you are focusing on
- The audience and geography (e.g., customers in the Eastern or Southern regions)
Collecting and recording data
With your parameters set, it’s time to gather your data.
In addition to recording historical sales data, such as the time and date of all orders, the SKU(s) included in each order, and the sales channel from which each order originated, you should also track:
- SKU Velocity: how frequently a SKU is picked over a certain period
- Inventory Turnover Rate: how many times your entire inventory has been sold and replaced within a given timeframe
- Average Order Value (AOV): the average dollar amount a customer spends on each order
- Return Rate: the frequency with which each SKU is returned
- Stockout Rate: how often your business sells out of a particular SKU, depleting available units to 0
By tracking these inventory metrics over time, your business can forecast growth and identify trends at a more granular level, allowing you to compare how well your forecasts matched reality.
You may also need to gather other data, such as market conditions, obsolete stock, stockout frequency, and other order details. Ensure that any data you use is collected accurately to achieve the most reliable forecasts.
Measuring and analyzing data
With the right data in hand, the next step is to analyze it for patterns, trends, and other insights that could improve your future demand forecasts.
Start by comparing your predicted sales performance to your actual performance. Whether you perform this analysis manually or automate it, this repeatable data analysis can help you adapt your next forecast.
Budgeting accordingly
Once you have established a feedback loop, you can set your next forecast (hopefully more accurately) and update your budget to allocate funds based on your growth goals. If your demand forecast is accurate, it will save you a lot of money on inventory holding costs, marketing spend, labor costs, and production.
Demand forecasting helps brands make informed business decisions that affect everything from the inventory planning process to supply chain optimization. With customer expectations changing faster than ever, businesses need a method to forecast demand accurately.
If you’re looking for an e-commerce fulfillment solution to help you improve demand forecasting, learn more about how Diggipacks can help you replenish stock and deliver the experience customers want. Request a pricing quote for our fulfillment services below.
