Pricing is a powerful performance lever. In retail, price optimization helps players to increase margins and sales. It makes a major contribution to improving their profitability.
However, the extent of the potential gains depends very much on how you define your pricing strategy.
At present, pricing is essentially based on competitive alignment. This strategy involves adjusting your prices in line with those of your competitors, in order to maintain or expand your market share. However, it does not take into account the customer and his sensitivity to price.
And yet, in many cases, putting the customer at the heart of the equation helps optimize prices and boost profitability. To do this, you can rely on the notion of price elasticity.
In this article, find out why it makes sense to measure and exploit price elasticity to obtain more relevant prices.
Why measure price elasticity?
What is price elasticity?
The price elasticity of a productrefers to the relationship between the variation in price and the variation in demand.. To calculate it, we use the following formula:
Price elasticity = % change in demand / % change in price
Using this formula, you can identify the extent to which a price change affects demand.
Putting the customer back at the heart of pricing strategies
In practice, there are several ways to calculate prices.
The most simplistic approach is to use costs as the basis for defining a price based on the cost and margin you wish to achieve. In this case, you don’t take competitors or consumers’ price sensitivity into account. You’ll soon come up against a number of pitfalls:
- Prices that are out of line with those of your competitors
- An unreadable and confusing price image for your customers
- A competitive deficit
This is why, today, retailers rely mainly on rules engines to calculate their prices. They establish calculation rules that apply according to different input variables: competitors’ prices, variations in purchase prices and costs, inventory levels, etc.
In this operating mode, the price is the consequence of the chosen rule. Upstream, this method involves using the right influencing factors. In a context where data is omnipresent, input variables tend to multiply. We can add seasonal variables and weather data to try and optimize prices.
Here again, however, prices remain uncorrelated with demand and the value perceived by customers.Customer sensitivity to price variations remains a blind spot as long as we don’t rely on the measurement of elasticity. And yet, taking the customer into account when calculating prices makes it easier to achieve sales targets.
The advantage of measuring price elasticity lies in the combined optimization of price and demand. Elasticity helps you define the ideal selling price to maximize your sales or margin.
The operational challenges of price elasticity
In theory, price elasticity is a powerful lever for calculating prices that guarantee an optimum level of performance. In practice, however, it is often difficult to translate this into operational terms, for a number of reasons.
Understanding the evolution of short- and long-term price elasticity
One might expect lower prices to lead to higher demand. And, conversely, one might think that higher prices would have a negative impact on sales. But this is not necessarily true in practice.
Firstly, some products are particularly inelastic.. Price variation has little or no effect on demand. This is the case, for example, for luxury products, but also for products based on a strong brand such as Coca-Cola or Nutella.
Moreover, elasticity is not linear.. In the case of a product with high price elasticity, the slightest drop in price leads to a significant increase in demand, but only up to a certain point. If you keep lowering the price, you’ll reach a plateau with stagnant sales.
Therefore, when integrating price elasticity into your price calculations, it is essential to monitor it continuously and consider its impact on various performance indicators.
Take cross-elasticity into account
If we consider price elasticity on a specific product, we can indeed detect a relationship between price variation and demand variation. But this approach overlooks the impact of price variations on other substitutable or related products.
Let’s imagine, for example, that you drastically increase the price of a product when there are several other substitutable products in your ecosystem. The absence of price increases for these substitutes also has an impact on demand for the original product.
In other words, we’d have to extend the elasticity calculation to all related products.. This is what we call cross-elasticity.
Let’s take a concrete example to illustrate this point.
You lower the selling price of the jar of strawberry jam. You maximize your sales of this product, as expected. But at the same time, some customers who bought the strawberry jam did not buy the raspberry or apricot jam they usually buy. So you’ve had a negative impact on demand for your other products.
How can you incorporate price elasticity into your pricing strategy?
When to use price elasticity?
Using price elasticity, you can maximize your performance by optimizing both the price level and the demand level of a product.
To take full advantage of this, however, elasticity needs to be measured on an ongoing basis, and contextual factors that can change the situation need to be taken into account. Indeed, even with multifactorial predictive analysis, a crisis or unexpected event can profoundly alter customers’ sensitivity to price.
Consumer price sensitivity helps optimize sales performance over the long term. However, it makes price trends less predictable than competitive alignment.
Indeed, competitive alignment may offer less potential for profitability, but offers greater security, provided we don’t get drawn into a price war.
In practice, it’s best not to choose, but to rely on the complementary nature of the two approaches. For example, you can opt for either strategy depending on your product segmentation. It is also necessary to use performance indicators such as price image, market share and sales to optimize pricing strategy.
Integrating price elasticity into a multifactor approach
The price elasticity approach to pricing helps to take into account the customer’s perceived value. Finally, it raises the question of the right price that the customer is willing to pay for a product.
But this perceived value is personal, relative and not fixed in time. A pricing strategy based on price elasticity alone would be difficult to understand, as price variations would be incessant. And the brand’s price image would suffer. This could be considered if the retailer had a monopoly on the product. In practice, however, it is part of an ecosystem that requires it to take other factors into account.
This is why the choice of a pricing strategy is not a partisan quarrel. The sensible solution today is to include all influencing factors, including price elasticity, in price calculations.
Measuring and analyzing price elasticity adds a further dimension to the definition of pricing strategies.
The price elasticity approach puts the customer back at the heart of pricing strategies by focusing on his sensitivity to price. Elle offre la possibilité d’optimiser à la fois les niveaux de prix et de demande d’un produit.
However, its integration generates operational challenges:
- How can we understand the short- and long-term evolution of elasticity?
- What about cross-elasticity?
- How can strategy be steered when elasticity is so sensitive to changes in context?
All these questions suggest that price elasticity is not destined to become the alpha and omega of pricing strategies, but rather a key to further reading and analysis. By hybridizing it with other approaches such as competitive alignment, you gain a better understanding of the impact of pricing and optimize your pricing process.
Would you like to incorporate price elasticity into your price calculations to improve your profitability? Discover how our Optimix XPA pricing solution helps you optimize your prices thanks to its multi-factor approach.