How to calculate price elasticity of demand

What is price elasticity?

Price elasticity is a measure of how reactive customers are to changes in price. It helps organizations weigh up the supply and demand of a product to help make decisions around pricing, ordering and stock levels. There are lots of factors that impact price elasticity:

• Availability: is the product available everywhere or only in certain stores/channels? Is there a similar product made by a competitor?
• Need: does the customer need the product (essential purchase), or can it be considered a luxury item (non-essential)?
• Cost: customers may be more willing to buy a product (especially non-essential items) if it is a small proportion of their income

Understanding price elasticity and how each of these factors impacts it helps businesses build smarter pricing strategies and accurately forecast demand fluctuations.

Traditional approaches to calculating price elasticity

Historically, businesses have relied on various traditional methods to estimate price elasticity. Some common approaches include:

Percentage change method

This straightforward method involves calculating the percentage change in quantity demanded resulting from a percentage change in price. While simple, this method may not capture complex demand patterns and may not consider other factors influencing demand.

Regression analysis

Regression analysis is a statistical method that helps identify relationships between variables. By analyzing historical data on price and quantity demanded, businesses can estimate the price elasticity coefficient using regression models. However, traditional regression may not be able to handle non-linear relationships and interactions between multiple variables effectively.

Consumer surveys and experiments

Businesses often conduct consumer surveys or experiments to directly assess how changes in price impact demand. These methods can provide valuable insights, but they may be time consuming, costly and may not fully capture real-world market dynamics.

Elastic vs. inelastic demand

How important price elasticity of demand is depends on the items you sell and, in particular, if it’s an elastic product or an inelastic product.

For an elastic product, you’d expect a proportionally large change in demand for a small change in price. For an inelastic product, you’d expect a proportionally small change in demand for a change in price.

Elastic and inelastic demand example

Here’s an example of elastic vs. inelastic demand. An online health and beauty retailer sells shampoo with argan oil, but sales with customers have stagnated. To add salt to the wound, the cost of ingredients has also risen, so the retailer has had to increase prices to maintain a healthy profit margin on the shampoo.

This is a classic case of an elastic product — where demand and cost fluctuate — and consumers decide whether this non-essential item is worth buying. Measuring the price elasticity of demand in this scenario will help the retailer decide whether to continue stocking the product, drop its price or discontinue the line.

On the flip side, a product like gas is an inelastic product. Even with demand fluctuations (like stagnation during COVID-19) and wild price fluctuations (most recently the Ukraine/Russia war), most people will still buy gas no matter what the price because it’s an essential item.

So, how do you calculate these formulas? 🤔

A (super simple) price elasticity of demand formula

Calculating the price elasticity of demand for each product in your inventory is a simple task if (and only if) you have all your data ready. Before you dive into a calculation, gather some information like:

• Current/past prices for each specific product
• Present quantities demanded vs. past quantities
• A specified time frame to make your measurement (e.g., Q1 of the year or seasonal peak periods)

Once you have this information ready, use the below. formula to calculate price elasticity of demand. Put simply, you can calculate price elasticity of demand by dividing the percentage change in quantity by the percentage change in price.

Now, this can be confusing the first time you attempt it, so let’s use the argan oil shampoo example from earlier.

The retailer wants to figure out how much demand has changed since it increased prices three months ago. Usually, 3,000 units would sell in this timeframe. But, the original price of the shampoo ($10) was increased to $14 to keep up with rising costs. This 40% increase led to just 2,400 units being sold over three months, representing a 20% drop.

Let’s fill that into the price elasticity of demand formula:

-0.2 / 0.4 = -0.5

In nearly all cases you’d expect a price elasticity of demand to be negative, as you’d expect demand to increase as the price reduces. This example, because it is less than one in magnitude, is considered to be “inelastic” — which intuitively means that, for a given change in price, you’d expect a smaller proportional change in demand e.g., demand change percentage < price change percentage.

But what does this number actually mean? Is it good, bad or ugly? 🤔

According to Gill Avery, a senior lecturer at Harvard Business School, the end value is what really matters. The higher the number, the more sensitive your customers are to price changes and fluctuations. However, it’s not an exact science.

It’s also important to be aware of some of the pitfalls of the simple calculation we’ve used above:

• Extra factors: this calculation doesn’t take into account extra drivers of sales. E.g., sales could have been low due to a stockout, not necessarily because customers didn’t like the price
• Seasonality: the calculation assumes that the relationship between demand and price will be constant over time. In reality, it will naturally fluctuate depending on the product. E.g., would you expect to get the same lift in demand for a 10% discount on swimming trunks in the winter compared to the summer?
• Data: the calculation assumes that you have data for a the same product at a number of different price points

How AI can calculate the price elasticity of demand

Ever had an item sitting in your Amazon basket, only to get a ping later down the line in your inbox to tell you the price has since been hiked up? I’ll let you in on a secret — Jeff Bezos didn’t change the price manually.

Amazon and many other companies like Uber, B&M and Walmart have ditched spreadsheets for artificial intelligence (AI) to automatically calculate price elasticity of demand. That’s because, on paper, pricing items seems easy (low price equals = high demand and high price = low demand), but most retailers know it’s not that simple.

Lots of factors (outside of cost) impact a customer’s decision to buy. Things like inflation and seasonality could push prices up, or other factors like promotions and product popularity.

AI-modeled price elasticity:

Let’s take a recent example of Fujifilm X100V’s camera, which launched in 2020. Vloggers decided they loved the product and started to plug it as a must-have item. Suddenly, it was impossible to find one, and TechRadar said the viral videos on TikTok were to blame. 

Although the camera was originally priced at $1,340, it was hiked to $2,900 during peak customer demand:

What these numbers tell us is price wasn’t the overwhelming factor around customers buying the camera. The lack of availability (Fujifilm temporarily suspended orders) pushed prices high, but consumers were willing to pay. This elasticity in pricing meant retailers could cash in on higher profits.

To truly understand these fluctuations and when to move prices, AI-modeled price elasticity calculations can be worth their weight in gold. For every Fujifilm X100V in an inventory, there could be hundreds of other non-viral SKUs that are surging in demand, but prices are staying the same. To solve this, AI can track every influence on product demand, from promotion to seasonality, and update prices automatically.

Consider your current product inventory. 

Do you know how factors like marketing, seasonality, inflation and website placement are impacting pricing elasticity for every product? 🤔

The reality is, a human can’t unpick this data and track it accurately. Every day (or even every hour), these factors can change and impact the demand for a product and how much a customer is willing to pay for it. 

With AI price elasticity tools, your team can make pricing decisions based on instant datasets that give you accurate pricing models. With price elasticity modeling and price simulations, every product in your inventory can be priced according to your target market’s appetite:

Use AI price elasticity to drive revenue for your business

Understanding how price elasticity of demand impacts a customer’s buying decision is essential to optimize stock levels, pricing and inventory. By looking at how quantity demanded is correlated to price fluctuations, retailers can determine what price customers will pay to maximize revenue.

The problem is that price isn’t the only factor that swings a customer’s buying decision. Everything from marketing to promotions, inflation and seasonality can impact whether or not a customer heads to the checkout. It’s an uphill battle for retailers to track every product, especially when selling online or to multiple different target markets.

AI price elasticity tools can track price elasticity in real-time and instantly give modeling and price simulations to help businesses gain a competitive edge. Using Peak’s AI pricing software, retailers can uncover the price elasticity of demand for every SKU in their inventory and make better pricing decisions to boost revenue.