Elasticity
A product's price elasticity of demand measures how strongly customer demand reacts when its price changes.
How to interpret Elasticity values
Why is elasticity always negative?
Because demand and price move in opposite directions: when price goes up, demand goes down; when price goes down, demand goes up. The model's demand curve is monotonically decreasing, meaning higher prices never predict higher sales volume.
Rule of thumb
Elasticity relates percentage price changes to percentage demand changes:
% Change in Demand ≈ Elasticity × % Change in Price
- Example: An elasticity of -2.5 means that a 10% price decrease is predicted to increase demand by approximately +25% (
-2.5 × -10% = +25%). Conversely, a 10% price increase would reduce demand by approximately -25% (-2.5 × +10% = -25%).
Interpretation guide
| Elasticity Range | Price Sensitivity |
|---|---|
| -1.0 to -1.5 | Low sensitivity |
| -1.5 to -3.0 | Moderate sensitivity |
| -3.0 to -5.0 | High sensitivity |
| below -5.0 | Very high sensitivity |
When is the value missing / not displayed?
If an optimization proposes keeping the current price unchanged (i.e. price change = 0), the elasticity cannot be evaluated using the transition formula and will be omitted.
Mathematical Definition
There are multiple definitions for measuring elasticity.
Example:
Our Elasticity KPI is using the second elasticity definition, which is often referred to as "Mid Point Elasticity". The advantage of this definition is that going back from to will give you the same elasticity, so it is symmetric. Also, the Simple Elasticity often comes up with very large elasticity outliers which are difficult to interpret.
We learn Elasticity on market, channel and product level based on product specific price change history and based on product attributes. If we don't have any information on the product specific price history, we approximate it using similar products. To ensure that we capture the true relationship between price changes and sales, we automatically detect seasonal patterns - like summer or holiday trends - and correct the data accordingly. For example, the sales of a seasonal product like sandals, which sells well in summer but less in winter, are smoothed out to account for their summer peak before calculating elasticity.