What are the methods for predicting product demand elasticity?

Methods for predicting product demand elasticity include historical data analysis, market research, and economic modelling.

Historical data analysis involves examining past sales data to identify patterns and trends. This can provide insights into how changes in price have affected demand in the past, which can then be used to predict future elasticity. For example, if a company has previously increased the price of a product and seen a significant drop in sales, this suggests that the product has high price elasticity of demand. Conversely, if a price increase has had little impact on sales, this suggests that the product has low price elasticity of demand. However, this method assumes that past behaviour will predict future behaviour, which may not always be the case.

Market research involves gathering information directly from consumers. This can be done through surveys, interviews, focus groups, or observational studies. By asking consumers how they would respond to a change in price, companies can gain a better understanding of the potential elasticity of their products. However, this method relies on consumers accurately predicting their own behaviour, which can be influenced by a variety of factors.

Economic modelling involves using mathematical models to predict how changes in price will affect demand. These models can incorporate a variety of factors, including income levels, consumer preferences, and market competition. By adjusting these variables, companies can simulate different scenarios and predict how demand might change. However, these models are based on assumptions and may not accurately reflect real-world behaviour.

In practice, companies often use a combination of these methods to predict product demand elasticity. By triangulating the results from different methods, they can increase the accuracy of their predictions and make more informed pricing decisions.