We consider each click on one of your tracked products to be a discrete 'demand unit'.
Once our script identifies that one of your products was 'demanded' multiple times by an indecisive customer, we augment your product prices for the purposes of questioning the user about their preferences (these augmented prices are not representative of your true prices, they are simply used as a proxy for sensitivity).
The prices themselves are augmented with the principle of unitary demand, where we expect a 1% change in the price to lead to a 1% change in quantity. This forces the customer to choose between two products that they value highly, while holding their relative discounted prices equal, which allows us to infer their true underlying preferences.
Asking this kind of closed-form question allows us to calculate price-elasticity with the general intuition of:
Which measures how a change in price affects the demand of a product, for both the product the user selected, and the product the user rejected (if there exists enough data). The result ranges from 0 to ∞, with 0 to .99 being inelastic (insensitive to price changes), and 1.01 to ∞ being elastic (sensitive to price changes).
We aggregate all of this data for you into your insights dashboard, where you can see the trend of price sensitivities among your products. This allows you to make actionable changes to prices to improve margins and satisfy consumers.