Price elasticity describes the relationship between price and product uptake. While intuitive in theory, the measurement is often too simplistic for practical use in day-to-day pricing decisions, as it assumes a situation of a perfect monopoly that is too ideal, anticipates full customer knowledge and transparency about the offering, and neglects the value differentiation of offerings that usually occur in reality. It also largely disregards the fact that different customer segments may have different perceptions of value and benefit, and often neglects potential competitors’ responses. To make better use of it in daily pricing decisions, price elasticity should be assessed considering various factors, such as a limited number of the most recent transactions and the probability of increasing revenues and profits. This can only be achieved by applying algorithms in an artificial intelligence/machine learning system environment where the algorithm is programmed to evolve in a dynamic way around this key figure.
Cheaper for more sales or more expensive for more profit
Nearly every pricing professional has experienced the following at some point during their career: out of the blue, they receive an urgent call from the finance or marketing department or a C-level executive and are asked: “What is our price elasticity? Do we know it?”
Price elasticity is often considered a mystic word in pricing. If you know your products’ price elasticities, you likely have pricing under control. If not, you’re probably setting prices incorrectly, potentially giving money away, forfeiting sales growth, or, in the worst-case scenario, both. When it comes to price elasticity, the important question to ask yourself is: “Can we sell more products by decreasing our prices, or is it more profitable to stick to higher prices?”
But what does price elasticity actually mean? Do companies truly need to know their price elasticities? How can this knowledge be used? Are there potential limitations to the theory, and how can companies employ it correctly?
The math behind the principle
Ever since Alfred Marshall first cited the concept in his groundbreaking Principles of Economics in 1890, price elasticity has become a key measure in price modeling theory. In his work, Marshall emphasized that the price and output of a good are determined by supply and demand. Price elasticity is a measure of how responsive the quantity demanded of a good or service is to changes in its price.
Formula 1: price elasticity describes the change in quantities sold in response to the change in the unit`s price
Graph 1: The upper graph describes the demand curve with quantity uptake in response to price and below the integral of revenue, which shows at which price a revenue maximum would be reached
The mathematics of price elasticity are simple and intuitive (see formula 1). If your product has a very high price, you may find that only a small number of customers are able and willing to purchase it. If you lower the price, the number of interested customers may increase. Lowering the price even further may result in selling more of the product. The observed product uptake in response to changing prices draws a demand curve. The slope of that curve is then defined as price elasticity.
There are five types of elasticity. Perfectly Elastic is a horizontal line and means the price is fixed and quantity uptake could be infinite. This only occurs in regulated environments. Relatively Elastic is when small changes in price trigger large changes in quantity demand. Unit Elastic describes a situation where any change in price is matched by an equal change in quantity. Relatively Inelastic is when large changes in price cause small changes in quantity demand. Perfectly Inelastic appears as a vertical line in the graph and means that a change in price has no effect on the quantity uptake.
Prices will have an impact on revenues and profits, since revenues are a multiplication of price and quantity, with the integral under the demand curve drawing a revenue curve with a maximum. If a company’s goal is to maximize revenues, it should aim to target that price point. In addition to revenues, costs of the good could also be included in calculating the optimal price drawing a curve with a profit maximum. Revenue and profit curves are rarely identical. Ideally, companies should price for revenue, profit, or somewhere between the two. As such, price elasticity becomes a directional measure toward setting the optimal price. This is what the theory says, this is what is taught to MBA classes around the world, and this is where all the expectations to know price elasticity come from.
Theory versus practice
While the theory is clear, price elasticity may only have limited uses in practice. To find out whether determining your offering’s price elasticity is useful, ask yourself the following questions:
- Is the market informed and educated about your offering? If you have an innovative product, knowing whether customers know about its advantages and are interested in buying it is key. It’s common for companies to set a high price for those who know and value the offering and then continue to increase awareness of the product in the market by advertising and explaining it to more customers. Therefore, in this case, price isn’t the main driver of uptake but rather product promotion and value communication. For an established and commoditized product/offering, which may be known to everybody, only the price will matter. This, however, raises the next question.
- Are you a monopolist, or do you have competitors? If you are a monopolist with a fantastic offering, you may think changing the price to increase uptake is the only limitation that could affect customers’ ability to afford the product. But how likely is this in a commoditized market? Commoditized markets usually have high penetration and intense competition. Lowering the price in such a market often yields no benefit, as the competition will likely react with a price decrease, potentially leaving both you and your competitor worse off than before. This is commonly known as a price war. This triggers the following question.
- Do you have a complete understanding of your market and pricing? Many suppliers know the total market size for their products and have transparency into their customers and associated quantities and prices. However, they often know very little about their competitors’ customer bases. In addition, competitors’ offerings need to be comparable to or interchangeable with your own offering if this competitive information is to provide any value to deducing price elasticity. That’s rarely the case, with the exception of highly commoditized products. If you don’t know competitors’ prices related to associated quantities sold, you won’t be able to calculate price elasticity as you’ll only have an overview of your existing customer base. Lowering the price for those already buying your products will not make you better off.
- How penetrated is the market? This is a key question when it comes to price elasticities. If you are in a high-end market with a great offering that you can sell at high prices to a few customers, ask yourself whether there are more customers you could capture. If the answer is yes, you may consider lowering the price to gain these customers. This, of course, is only effective if the high price is the only reason why they haven’t purchased your product yet. This situation is very rare and may be the only time where you may apply a price elasticity model to the correct price.
When it comes to employing price elasticities in business decisions, the possibilities are very limited. The theory is built on a monopoly situation that assumes customers are fully aware of the value a product offers and that price is the only driver of demand. However, it usually doesn’t reflect the reality of business.
Researching and assessing price elasticities
If you are in a commoditized and transparent market, you may consider assessing the price elasticity of one of your offerings. To do so, it’s important to understand the specific parameters, including:
- Total market size for the offering
- The ratio of the market that is educated about your offering versus non-educated
- Your competition and price points of competitors related to their sales
- The difference between your offerings and your competitors’ in terms of perceived value (e.g., fair value line)
- Customer segments and each segment’s ability to purchase the offering
Quantifying price elasticities is no simple task. It requires assumptions, adjustments, and is generally more of a scenario-specific approach. Following a more intuitive approach to the question of whether demand is inelastic or elastic is much easier. This method offers more of a high-level view at an industry, business area, or product level. For example, medical procedures are generally fairly inelastic. However, the question here is what choice the user has. If you’re suffering from a toothache, it’s unlikely that you’ll decide against a procedure because of its price. But if the procedure is elective, such as cosmetic surgery, your answer might be different.
From inelastic to elastic demand
In general, inelastic demand describes a situation in which companies can typically extract higher margins due to customers’ lack of price sensitivity. This is naturally very attractive for many to engage in. However, over time, the more companies find themselves in an inelastic demand market, the more competition increases. As a result, prices and margins decrease since customers gain more power in terms of supplier choice. Therefore, competitive dynamics help regulate pricing to some degree, making necessary products more affordable to many and preventing the consumer from being overcharged for a basic commodity.
While competitive dynamics can often successfully balance demand and supply of essential products at market prices, it also has its limitations, particularly in situations where supply suddenly becomes limited. For example, a surge in demand for a certain product may overwhelm the competitive dynamics and nullify its effects. In an inelastic market, such a surge would cause the price to increase, and customers would start to compete for supply and not make the supplier compete to win over customers. Surging prices can result in some customers being able to pay more for a product and others being left out. In a market of essentials, this can have severe consequences as seen, for example, with face masks at the beginning of the COVID-19 pandemic.
Price elasticity – Trend versus quantification
To quantify a price elasticity, many assumptions, adjustments, and uncertainties need to be taken into account, which often leads to a rather inaccurate result that may be misleading. A simpler approach involves assessing a trend and compromising for just knowing whether demand is inelastic or elastic; that is, whether the market is insensitive to price or whether a price change would lead to more or fewer sales. Therefore, price elasticity serves more as a compass needle that can help guide price adjustments in order to achieve more revenues or profits without specifically revealing the size of the price increases or decreases. The direction the compass needle would move in depends on the business lifecycle. In other words, every additional transaction rebuilds the algorithm and makes it more accurate. If you close a deal at a better price, the model would suggest that you could eventually close even more deals at even better prices until you lose a deal to a competitor with a lower price. The model would then incorporate this lost transaction with quantities, price, and the lost revenues and profits. In an effort to maximize revenues or profits, the elasticity compass may point you toward decreasing the price again. Therefore, determining the exact point of balance, where the needle can shift to either side, is key.
To support this approach on a broad basis, you will need algorithms as well as an artificial intelligence or machine-learning system. In addition, you’ll need sufficient transactional data and proper segmentation. Geographic components may be an important factor, but there are more, such as channels and customer classifications, that should group transactions and deals into the best comparable micro-markets. This might not always be easy to achieve, as most companies struggle to segment their accounts properly. However, assuming a supplier has successfully segmented their account, adding price elasticity as a component for maximizing revenue and profit shouldn’t be a hurdle.
Algorithms and price experiments
To support revenue and/or profit maximization, you should build an algorithm as a function of price elasticity by analyzing the most recent deals, both won and lost, for a specific micro-segment where you have information on the price and quantities associated with the outcome. Once you’ve determined the price elasticity, the algorithm will provide guidance as to how to adjust the price of your offering in order to close a deal. To capture the most current trend, you will need to focus on the most recent transactions in a specific micro-market. For example, if you closed six deals in the last six months in a micro-segment, this six-month timeframe is the one to look at. By setting the parameters to only analyze transactions made within the past six months, your algorithms would constantly rebuild and will be able to direct your sales team toward a certain price level that will increase the probability of successfully closing deals.
With regard to machine-learning tools, some sales teams may argue that the recommendations such an algorithm makes would be too obvious to them. They may add that the approach they have been applying, namely their intuition and experience, is effective enough and that a new AI/ML system wouldn’t add value. This perspective, however, is too simplistic. The sales team’s job involves knowing the market, their customers, and their business, but opportunities may be lost if they under- or overestimate price since there are many factors to consider. Small deviations from the optimal price can have significant consequences. While sales teams’ intuition and experience should remain key drivers for successful transactions, the algorithm can be a beneficial addition to their assumptions and can help sales teams significantly sharpen their skills and gain more experience.