Understanding your product’s price elasticity of demand is the key to set optimal prices. Managers need to understand how customers react towards a change of prices to tap the full potential of pricing.
Based on price elasticities you can…
- …identify products that are important to your customers and thus are crucial to build your price image.
- …identify products where your customers regularly compare prices.
- …calculate the profit and revenue scenarios for different price points for single products or your whole assortment.
- …enable automated and optimized demand driven pricing.
So what is price elasticity? How can it be measured and can you use it to optimize prices?
Definition of price elasticity
Price elasticity of demand measures the degree to which the demand for a product changes as its price changes. To be exact, price elasticity is calculated by dividing the percentage change of demand when there is one percent change in price. Most customers and most markets are sensible to price changes. The assumption is that more people will buy a product as it gets cheaper and less if it gets more expensive. That means that price elasticities are almost always negative. Generally, a product is said to be unelastic when the elasticity is less than one (in absolute value). On the contrary, a product is said to be elastic when the elasticity is greater than one.
Measuring price elasticity
Measuring price elasticity is hard. Generally, companies do not act under perfect test conditions. Often not only prices are changed but many other internal (e.g. marketing spend) and external factors (e.g. competitor prices or seasonality) change at the same time. Also there are many factors that influence price elasticity such as seasonality, competitor prices, weather or brand image. Also price elasticities are not constant along the whole price demand curve. For example, price elasticities are regularly higher (in absolute terms) close to prices of major competitors.
Recently, leading online retailers started to use machine learning algorithms to calculate price elasticities. This method is considered best practice to extract as much information as possible about price elasticity from available data. Compared to more simple rule-based pricing it yields better results and allows for a more differentiated pricing approach. To be able to extract price elasticity from existing transaction data prices must have been changed in the past. More advanced machine learning applications are able to learn across product groups or cluster. In this case not every product must have a history of price changes to determine its price elasticity. Therefore, measuring price elasticity is especially challenging for retail segments with changing assortments for each new season (e.g. fashion retail).
At 7Learnings we supported some of the leading fashion retailers (e.g. ABOUT YOU) to upgrade their pricing process. We excel in measuring price elasticities also from very sparse data. If you want to learn more about the potential of next generation dynamic pricing and see it in action, reach out to us and book a product demo.