Differences between Demand & Sales Forecasting

We`ve broadly discussed sales forecasting in our data analytics blog. But there is something we didn’t cover up to date and this is demand forecasting. Are there any differences between them? And if yes, how they complement each other.

Sales forecasting

Modern sales forecasting techniques are based on qualitative approach relying on historical sales sets of data. This means that an established retailer or wholesaler is capable of amazingly accurate sales forecasting powered by mathematical and statistical modeling. Sales forecasting can identify just formed trends, seasonality, recurrent behavior, promotional activity impact, etc.

It is a really important tool in terms of planning. Accurate sales forecasting might prevent overstock, hence locking cash or leaving the inventory space empty long before new delivery is scheduled to be done.

Demand forecasting

Demand forecasting can be both qualitative and quantitative and unlike of sales forecasting is not based solely on historical sales data. In fact, demand forecasting is projecting the demand for a particular product, product group or retail location which differs from sales forecasting with missed sales opportunities. Hence, demand forecasting is the sum of sales forecasting with the forecast of missed sales.

Demand Forecasting and Sales Forecasting are different and their respective uses should not be the same.

Missed sales opportunities

Missed sales opportunities can be estimated by sum up the time frame when a particular product was not available. Then we add the expected sales for the abovementioned period of time and the final value is the total of missed sales opportunities.

Demand forecasting for a new product or retail location

Forecasting the performance of something which is not existing yet is quite hard in terms of accuracy but there are few approaches that might be helpful. The quality and relevance of the data strings used for forecasting are crucial for its accuracy. In a case like forecasting the demand for a new product or retail location, there are few more variables.

New product:

  • Competition. If competitors offer the same or similar product, its performance is a strong indicator.
  • Products correlation. Tracking the performance of a product which easily can be matched in a single purchase is worthy.
  • Distribution. The demand might be generated by distribution and this should be kept in mind.
  • Brand. The strongest is the brand the more loyal customers it has.

New retail location:

  • Foot traffic. This is a tedious task but it should be done.
  • Location attitude. If you are selling coffee close to a park your demand will be entirely different if you are selling coffee in the mall.
  • Competition. The performance of nearby competitors is a strong indicator.


For best results, employ a combination of data, analytics and customer awareness to forecast demand for a new product or business.

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