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|Price Sensitivity Analysis : Analysing the Effects of Pricing|
Price Analysis - Sensitivity of Demand to Prices - Causal Analysis
Understanding the sensitivity of demand to price changes is fundamental to the formulation of pricing strategy. Price analysis fulfills that need by seeking to quantify the effect of pricing on demand through a causal analysis of historical data. If a pricing model can be constructed by analysis of historical demand, then a pricing strategy can be defined with much lower risk than would be the case with live experimentation.
Forecast Solutions makes use of specialist statistical software to construct pricing and other econometric models using linear and nonlinear regression techniques. Because the effect of the company's prices is also dependent on competitors prices it is often helpful to carry out the price analysis using a causal factor calculated as the ratio of company price relative to total market or key competitor prices.
Price elasticity is one way of describing price sensitivity. Price elasticity is measured as the % change in sales likely to take place as a result of a 1% change in price. The price elasticity of demand is always expected to be a negative figure as an increase in price will in most circumstances result in a decrease in demand. Unit price elasticity refers to the unusual situation where a 1% change in price causes exactly a 1% decrease in sales.
In most price models the price elasticity will change depending on the particular point of reference on the price response curve. So the elasticity from a point where the price is 8.00 may vary from the elasticity when the price is 9.00. The elasticity is sometimes then referred to as the point price elasticity.
Pricing Models to describe Price Sensitivity
A wide variety of mathematical models can be used to describe price sensitivity. The price sensitivity analysis often starts by exploring linear (straight line) relationships and this may well be helpful provided one is only exploring the effect of small price changes from a point close to the historical norm.
As it is unlikely that that price effects will truly occur in the fashion of a straight line, it is often useful to investigate alternatives in the form of nonlinear models. The exponential curve and power curve are popular because, by applying logarithmic transforms, they can be fitted exactly using linear regression techniques. Other forms of curve are likely to require nonlinear regression including the use of optimisation techniques.
Exponential curve: Sales = aebp
where e = mathematical constant (approx. 2.7183)
a = constant, b = constant, p = price
Power curve: Sales = apb
a = constant, b = constant (elasticity)
So the power curve exhibits a constant price elasticity of b.
Econometric Modelling and Causal Analysis
In causal analysis of any sort the aim is to quantify the effect of factors which are suspected of causing shifts in sales volume or market share. Price effects are one good example. Other causal factors such as the unseasonal weather or economic indices may also play a part. So a full analysis incorporating the effects of other factors as well as pricing may lead to a fuller understanding and a better platform for the formulation of pricing strategy and other business strategy
When a causal relationship has been identified and quantified it can immediately help to explain variations that has been experienced in historical demand. That is in itself very helpful, but to make full use of a pricing or other causal model in future forecasting it is necessary to forecast the causal factor itself. If the causal factor is a leading indicator and/or some well known index such as GDP or RPI for which other organisations publish forecasts, this can ease the task.
Need for expert help
Specialist software is invaluable in carrying out price sensitivity analysis and other econometric modelling. Care is needed to avoid confusion of the results with natural seasonality or inherent trends in market size or share. Forecast Solutions can expertly carry out the work using specialist statistical software, analysing the price effects and the influence of economic indices, sales force calling or other potential causal factors. The results can then be taken into account in defining pricing strategy, other business strategy and the demand forecasting process.