In today’s competitive business world, it is essential to predict how many products/services the customer will buy over a specific period. However, it is one of the hardest analyses to get right. Companies that fail to forecast the demand may face losses across various sales channels. There is a great need for product demand forecasting as it helps in making the right management decisions, predict the budget, launch a new product or scale the business.
Demand forecasting is an important element in every retail business. It is a lot more than just predicting the demand for your products. It is impossible to have the right amount of stock in place without proper product demand forecasting.
Demand forecasting helps businesses to manage the cash flow and maintain lean operations. Every business model looks for ways to cut down the cost, as it is one of the easiest ways to maximize profits. You can cut costs in a few ways by implementing product demand forecasting in your business.
The use of data and insights helps in predicting how much specific products/services the customers want to purchase during a period. In the traditional method, demand forecasting is a bit difficult – as it is seen as a whole.
There is a wide range of forecast solutions that retailers can make use of today. Some of these methods lead to timely forecast to increase sales and profits. Here are a few cases of demand forecasting -
Usually, in the traditional method of product demand forecast - several flaws reduce the accuracy of data. To forecast the demand accurately – retailers may need to use internal and external data metrics such as – competitors, consumer trends, historic sales numbers, website traffic, ad spend and more.
Some of the best techniques to forecast demand include –
In this technique, the business anticipates the product demand based on qualitative data. This data is based on factors like – expert opinion, market research, surveys, focus groups etc rather than facts.
The causal model accounts for forecasting based on controllable (marketing, price, sales, location etc) and uncontrollable factors (weather, politics, competitors, seasonality etc)
This is a quantitative approach for forecasting the demand. It is based on exact facts and figures, trend analysis, graphical methods, and life cycle modelling etc rather than soft inputs.
The importance of product demand forecast is very high for the retail business. Organizations can reduce the risk and make informed decisions by making an accurate forecast of product demand.
If you are new to forecasting the demand, you should establish a baseline for data. The simplest way is to pull in the sales from previous years and plan your sales by day. It is also important to understand the customer and their shopping techniques with predictive data analysis.
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