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Using Price Elasticity in Dynamic Pricing Models

Posted by PriceBeam on June 12, 2017

price elasticity dynamic pricing


The employment of dynamic pricing models allows firms to adjust prices continuously as demand determinants change, with the ultimate goal to set prices that are optimized for customers’ willingness to pay.
As dynamic pricing involves frequent price changes, it is highly relevant to examine how a price change will affect sales. Consequently, many dynamic pricing models include price elasticity as one of the factors that determine the optimum price.

Price elasticity (PE), or price elasticity of demand (PED), describes how the quantity demanded by consumers will respond to a change in price. Dynamic pricing models utilize vast amounts of historical data on demand and prices to determine how the nature of the relationship between these two variables. Generally speaking, price elasticity is determined by factors such as availability of substitutes; the proportion of the consumer’s budget that must be allocated to purchasing the item; degree of necessity; and brand loyalty.
While historical data can, in theory, give an accurate estimate of consumers’ price elasticity, the predictive ability of such estimate is often weak, and moreover, it requires more data points than most firms have available to obtain a statistically valid result. However, to increase the level of sophistication, it can be relevant to include an analysis of patterns in price elasticity determinants, to which heuristics and qualitative interpretation can be applied.


Availability of Substitutes
The degree to which the consumer considers a substitute to be available depends on both switching costs, the price of the substitute, and how this substitute compares in quality, functions and attributes. To get a more comprehensive insight into the consumer’s price elasticity, competitor monitoring makes a useful component in a dynamic pricing model, especially in determining how competitor prices compare. Again, this requires that a substantial amount of data is available, but it is often found that it can give the model a stronger predictive ability.

Consumer Budget
Firms rarely have this information directly available, but it is possible to undertake market research on your target group’s income level; perhaps more importantly, you need to clearly define your target group, and adjust this definition to changes in your customer base. 

Generally, this is one of the key problems with relying solely on historical price and sales data; it doesn’t consider the changes in your customer segment. For instance, if low-cost retailers would use pre-financial crisis data to determine post-crisis elasticity, it would be highly inaccurate as their customer segment would comprise a bigger proportion of high-income individuals than previously. Of course, this is an extreme example, but in many industries the characteristics of customer segments vary greatly with changes in consumer trends.
Including consumer budget in a dynamic pricing model will, indeed, require that you make some assumptions, such as a rough characterization of your customer base and respective income levels - however, it does help to better understand changes in price elasticity.


Degree of Necessity
Currently, drug pricing has turned into a heated debate due to the enormous pricing power that pharmaceutical firms enjoy. The reason behind this pricing power is two-fold: the fact that these firms’ patents restrict new firms from entering; and the degree of necessity of the products they sell. Determinants of degree of necessity differ greatly between industries; for instance, Coca Cola’s infamous attempt to surge price vending machine products on a hot day used temperature to determine the degree of necessity associated to their products; on the other hand, temperature is completely irrelevant the necessity of accounting software, while factors such as complexity of regulations will be more relevant in this regard.

Including variables that impact the degree of necessity of a product can strengthen predictive abilities, too: the challenge for firms lies in deciding which variables are relevant, and quantifying these. Especially the latter can be difficult in some instances, e.g. determining the level of complexity of accounting regulations. Here are some examples of how firms successfully quantified qualitative components.


Brand Loyalty
Brand loyalty is often defined as the degree of consumer faithfulness towards a specific brand.
To effectively incorporate a brand loyalty parameter into price elasticity estimation in a dynamic pricing model, an estimation of brand loyalty drivers must be conducted.
While brand loyalty is highly difficult to measure accurately, a few popular metrics easily available are customer satisfaction, trust, esteem, and perceived quality and value. Typically, this involves regularly issuing questionnaires and get customers to answer questions such as how likely they are to recommend the brand to others, whether they trust the company, and how familiar they are with said brand.


Time-Dependent Price Elasticity
One of the biggest limitations of current price elasticity models is that they fail to account for variations across time. For instance, airlines face increasing price elasticity with time (i.e. the closer you get to your date of departure, the higher price you are willing to pay), while fashion retailers will see a declining price elasticity from product launch. Also here, detecting patterns and incorporating them into the dynamic pricing model is crucial: however, this is often neglected due to the difficulty in determining the exact nature of variations across time. Yet, as with price elasticity in general, it is merely an input, and scholars have found that time-dependence should, indeed, be considered in the model to increase its predictive ability.


Summary: Price Elasticity Is an Input
While using price elasticity in dynamic pricing models often fosters great results, it is important to recognize the limitations of the measure in terms of predictive ability: at the end of the day, as so many other variables, they are based on past events, and therefore, it is sensitive to changes in consumer trends. The suggestions in this article do suggest ways in which this sensitivity can be reduced, yet it is still advised that it is used merely as an indicator for the success of new prices.

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Written by PriceBeam

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