Price elasticity, and a process for elasticity accuracy

There are a number of elements that comprise a solid pricing strategy for retailers and eCommerce players, and one such factor is elasticity. Brands and retailers both online and offline need to not only understand pricing elasticity, but have the tools, knowledge and data to calculate and incorporate this vital factor into their pricing strategy. Furthermore, in times of high inflation, widespread job losses or lagging economies, brands need to know how much consumers are willing to pay for their needs and wants. 

How can we define price elasticity? What impacts it? What process does Omnia follow to achieve true accuracy when it comes to price elasticity? According to Marc Helmold, pricing management is the process of integrating all perspectives and information necessary to consistently arrive at optimal pricing decisions. With this in mind, we will share how pricing elasticity calculations can be leveraged in the eCommerce context and how knowing the price elasticity of a product can be incorporated into a pricing strategy.

Defining price elasticity 

Quite simply, price elasticity is the percentage change in demand divided by the percentage change in price for a specific product. The result of this calculation is referred to as price elasticity of demand (PED) for a specific product or service after a price increase or decrease. The price change must be at least 1% for observers to notice and quantify the demand, “the law of demand”  or the elasticity of the product.

There are 5 stages of price elasticity:

1. Perfectly inelastic: PE value = 0
2. Relatively inelastic: <1
3. Unit elastic: 1
4. Relatively elastic: >1
5. Perfectly elastic. Infinite

Note that the change in demand - or the elasticity - of a product doesn’t necessarily mean that sales of the product are going well. Elasticity doesn’t refer to how well a product is selling. It is simply a measurement of demand. For example, the price of a product could increase by 3% and the demand could fall by 10%, making this product highly elastic. As a consumer on the other end of the e-commerce table, our reactions and decisions whether to buy a product or not based on an increase or decrease is what comprises elasticity.

When knowledge becomes strategy

In retail, we most often deal with relatively elastic products. For most products of your assortment, one could say that a price decrease will increase the sales numbers. The key question is: What do you do with this knowledge? How do you apply it and turn it into profit?

For example, one measures a relatively high elasticity for a product (PE > 1), let's say a travel bag. This means that price decreases in the past led to higher sales. Now, one has the same travel bag, currently selling at RRP, with the intention of increasing sales. First of all, knowing the elasticity will not reveal what price change one needs to apply. Knowing the elasticity level will only reveal that a price decrease will more likely lead to an increase in sales. Let's assume the elasticity calculations result in a value of 2 for this travel bag. This would mean if one only worked with historical data and would apply a predictive pricing algorithm without including market data, the model would predict a 20% rise in sales for a 10% price decrease. But, what if the market price is already at 25% below RRP? In this case, one would need to decrease the price even further to achieve the desired sales rate. What is important to note here, is that one needs to include the market as a variable when it comes to applying price elasticity calculations in setting the optimal selling price. 

Cause and effect: What impacts price elasticity

In the following section, we will have a look at examples of how price elasticity can be influenced.

Length of the price change

If a consumer is searching online for a new pair of cycling shorts, and they come across a well known brand offering a 20% discount, their reaction to the price will be different in the following scenarios:

• If the discount ends within a few hours
• If the discount is on for the week ahead
• If the discount is seasonable, and the customer knows other, more premium brands will be offering discounts at this time too

These scenarios, where the length of the price change is pertinent to the consumer’s reaction, will, in turn, affect price elasticity. 

How discretionary a product is

If a product is not a need and more of a want, they are called discretionary products. These may include luxuries, treats, favourites and items that make life easier and more cushioned. For example, a consumer who wants a wine cooler fridge instead of putting the wine in the fridge they already own; or a consumer looking for an extra-length king bed instead of a queen sized bed. 

A consumer might see the price of a luxury item increase, which will sharply affect the demand for it. The more a discretionary item is increased by, the more its demand will fall, making it a highly elastic item. Non-discretionary items are considered necessities, such as soap or cooking oil, and their demand doesn’t change much despite a change in price, making them inelastic.

Discretionary items are considered wants and not needs, which means consumers can easily reason themselves out of buying it if the price increase is too high. However, this doesn’t apply to addictive consumers who can convince themselves that a want is a need. A consumer obsessed with techy gadgets or flashy belongings may be able to convince themselves that they need the latest iPhone or smartwatch, despite the rise in cost.

Usually, when retailers are looking to determine a price or price change, they will add the input factors of the product, price, time frame, and quantity, as well as the output, which is the elasticity sum (Price elasticity of demand (PE) = % change in quantity demanded / % change in price). This forms the price calculation they will apply to a product.

However, the saying that there is an accurate answer and a more accurate answer applies here. 

While you can calculate a product’s elasticity by simply using a notebook (or Excel) where you note down the price change and then measure the change in demand, Omnia can take advantage of the e-commerce context with much more pricing data being available in our database. Here are five ways Omnia does things differently:

• Omnia can calculate PE for large assortments, rather than only a small subset of top sellers, as long as enough price points are available and the results are statistically significant.
  
  
• PE is calculated on the product level, if statistically significant. If not, it will be calculated at the category level, moving from the lowest to the highest category level until the results are statistically significant.
  
  
• Calculations use a price ratio, which is the relation of your own price against the market average, instead of a simple % price change. This helps to produce data even if you have steady prices. This includes the market situation, i.e. your competitor prices, into your elasticity calculations.
  
  
• The change in demand is operationalised as an indexed sales per week. In doing so, Omnia accounts for seasonal changes in demand and makes it comparable. For example, a jacket will have different demand in winter than in summer, independent of its price level.
  
  
• As Omnia includes multiple price points in the elasticity calculation (the price ratio of a product and its demand changes frequently), we do a regression analysis. What has been referred to as “statistically significant” above is how much each individual point deviates from the regression. In doing so, we know how certain the results of the elasticity calculation are.

How can clients use this to improve their pricing strategy?

• Knowing the elasticity of your products is not an end in itself. You can only gain value from it if you include it into your pricing strategies. There are two ways to apply elasticity calculations:
• 1. One dimensional product groups and
• 2. Multidimensional product clusters
  
  
• Groups: As in retail we most often always deal with elastic products, Omnia suggests to classify the assortment into groups of high, medium and low elasticity products. One would then implement a competitive pricing strategy for highly elastic products, and a less competitive pricing strategy for products with low elasticity value. Using the price-to-demand ratios, as well as historical data from consumers regarding their reactivity, eCommerce retailers can create a comprehensive PED strategy to maximise revenue.
  
  
• Clusters: In practice, we see that retailers often combine elasticity levels with other product attributes such as brand and category. As an example, one would only have a competitive pricing strategy for highly elastic products from certain brand or product categories. This process can be referred to as “clustering”, which involves more than just one parametre in defining what role a product plays when it comes to applying pricing strategies. Additionally, the page visits and conversion rates from Google Analytics can help to refine the cluster.

Own the competitive advantage

As McKinsey suggests in an article, a 1% increase in prices can yield up to 8.7% in operating profits for American companies. Yet, interestingly enough, a large 30% of the total pricing decisions a company makes each year do almost nothing for gaining the best possible price. This demonstrates the importance of PED in pricing management.

As a brand or retailer operating in an overly saturated market where e-commerce has only but exploded in the last two years, having the best possible data for your pricing strategies is pertinent to its success and consistent growth. Moreso, it is important to have a partner in pricing software who understands the necessity of a robust pricing strategy in times of high inflation and over competitiveness. Learn more about us by following us on LinkedIn!