By Prem Bhatia, Co-Founder and CEO, Graas
Learn from the past if you want to predict the future. That’s a fairly common saying and it could apply to businesses selling online as well. If demand is strong, revenue follows.
The flip side though is that past performance is no guarantee of future results. One of the big challenges for eCommerce businesses is that customer behaviour doesn’t follow a fixed pattern. What sold well last month might now be sitting unsold in a warehouse. So, if you don’t forecast the demand for your products, you risk being left behind in the online marketplace.
But what is demand forecasting? It’s a predictive analysis of future customer demand based on historical sales data and real-time market trends and buyer behaviour. It involves using advanced statistical techniques and algorithms to anticipate how much of a product a retail brand will likely sell in the upcoming periods.
In eCommerce, demand forecasting is entirely centred on predicting online sales, requiring businesses to analyze sales records, customer orders, inventory levels, pricing, promotions, seasonality, and other external factors.
While it is impossible to predict demand with absolute certainty, making predictions as close to reality as possible is crucial. There are many factors that affect demand forecasting in eCommerce. External elements such as seasonality, competitors, geography, and the economy play significant roles.
For example, demand varies with the seasons; holidays and weather patterns can cause predictable shifts. The ever-growing number of competitors entering the market with new products can also affect future demand. Moreover, geographical differences mean that demand can vary depending on the location of your target audience. Economic conditions, too, influence consumer spending habits; during a recession, customers prioritize essential goods over luxury items, while economic booms typically lead to increased spending on non-essential products.
Then, there are internal factors at play. The type of products offered, pricing changes, promotions, and the introduction of new products, all have a direct impact on demand patterns.
For example, a well-timed promotion or price reduction can significantly boost sales, even for products that might not be in high demand during a particular season. Introducing new products can disrupt the demand for existing ones, requiring careful adjustment of inventory levels to avoid overstocking or understocking.
Accurate demand forecasting brings several benefits to an eCommerce business. Firstly, it helps reduce financial risk by providing a solid foundation for budgeting. Understanding changing customer demands allows businesses to make informed financial decisions.
For example, if historical data suggests that new products take time to gain traction, businesses can allocate a smaller budget to initial inventory, thereby reducing the risk of being stuck with unsold stock.
Secondly, demand forecasting ensures that customers can get the products they want, when they want them.
Over the years, customer behaviour has changed — they are not only savvy but also demanding. If you cannot meet their needs, they will quickly turn to your competitors. But with effective demand forecasting, you can stay ahead of the curve by ensuring that inventory levels align with predicted customer demand.
Furthermore, demand forecasting can lead to significant cost savings by optimizing inventory management. When you have the right amount of stock on hand, you avoid the costs associated with excess inventory, such as warehousing fees and the potential for unsold stock to become obsolete.
By predicting future demand, businesses can better manage their warehouse space, ensuring it is used efficiently. Additionally, robust inventory forecasting can help businesses explore new warehousing methods that further reduce costs.
Despite these benefits, demand forecasting is not without its challenges.
Shifting consumer behavior, market volatility, supply chain disruptions, and data management issues can all complicate the forecasting process.
Data blind spots, inconsistent data management, biases in data collection, and inadequate historical records are common obstacles that can undermine the accuracy of demand forecasts. To overcome these challenges, eCommerce businesses must leverage advanced forecasting tools that can consolidate data from various sources, apply sophisticated algorithms, and provide actionable insights.
With the eCommerce industry growing with more customers and more businesses — the eCommerce demand-supply equation will definitely change. This makes demand forecasting crucial for eCommerce businesses that desire to survive market fluctuations.
By predicting customer demand with a high degree of accuracy, businesses can reduce financial risks, meet customer expectations, optimize inventory levels, and create pricing strategies that reflect actual demand. In an industry where consumer behavior is constantly evolving, staying ahead through accurate demand forecasting is key to long-term success.
