‘Elasticity’ is something most of us did in economics 101. Why have we not used it more than the evidence of my eyes would suggest?
The price elasticity of demand is usually defined as the relationship between changes in price and the resulting changes in volumes sold.
Elasticity = % change in quantity/ % change in price.
For example, assume you raise the price of a widget from $100 to $120, which causes the volumes sold to go from 1,000 in each period to 900. The price increase is 20%, the volume decrease is 10%. Elasticity is therefore 10/20, or 0.5.
It is the absolute value of the metric that is important, the distance from zero, rather than if it is positive or negative. If the number of widgets sold had been 750 after the price increase, the elasticity would have been 1.25. (25/20) a more elastic response to the price increase than the 10% drop in the example.
It is crucial for marketers to understand the elasticity of their products if they are to optimise the price/volume relationship, as price is the most sensitive driver of profitability.
The challenge is that there are a whole bunch of psychological and competitive factors that weigh into the equation in a consumers mind, simply not accommodated by the simplistic price/volume curve we all saw in that economics 101 class.
You can speculate all you like about price elasticity, but the only way you will know is to evaluate it in the marketplace.
We are currently (September) in the season where there is a glut of avocadoes available. My local Coles store seems to be altering the prices daily, anywhere between 1.00 each to 1.69 each. It is probably that they are partly reflecting the deliveries into their distribution centres, but the data collected at the checkouts will give them a detailed view of the volumes at differing prices, and even the time of day. This data is invaluable market intelligence that can be used to optimise their profitability for the product category.
Given that cost is a lousy starting point upon which to base price, it may be that this Coles is leaving money on the table by reducing the prices below $1.49.
How many less avocadoes would be sold at $1.49 than at $1.10?
Someone in their data analysis system, somewhere, has the data to make this call with close to absolute certainty as it applies to this store.
Theoretical price research, outside of the real purchasing decision making, is at best inaccurate, at worst, misleading. A/B testing used to be a challenge, but increasingly we can use digital tools to interrogate the data that digital capture, in this case the checkout, that has become available to us.
Companies like Amazon with vast amounts of data are so good at it that they know the price elasticity of individuals in particular product categories. They display prices accordingly every time you search, in order to maximise the chance you will buy at the highest price they can charge, based on your history. ‘Dynamic pricing’ is the now common term being used to describe this process.
Once you understand the elasticity of the price/volume profile of your product, you are in a better position to maximise profitability, while delivering value to your customers.
Header cartoon credit: Scott Adams. Not sure the analogy is a great one, but the idea was amusing.
Trackbacks/Pingbacks