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; holida.