Introduction
To grasp the concept of price elasticity of demand, it's crucial to first comprehend consumer demand. At its core, demand refers to the quantity of a specific good that consumers are both willing and able to purchase at various price points along a spectrum. Economists and business professionals commonly utilize the demand curve to illustrate and quantify demand. This curve graphically depicts the relationship between the price of a good and the quantity demanded at any given time. According to the law of demand, all other factors being equal, there is an inverse relationship between price and quantity demanded; higher prices result in lower quantities demanded, while lower prices lead to higher quantities demanded.
With this foundational understanding of consumer demand, we can explore the price elasticity of demand (PED). PED gauges the percentage change in quantity demanded in response to a percentage change in price. This is calculated by dividing the percentage change in quantity demanded by the percentage change in price, producing the PED ratio.
Why estimate price elasticity?
Understanding customer price sensitivity is a critical concept, especially for sales and marketing teams. It provides insight into how price changes influence demand, profitability, and market share of our products or services. Moreover, it serves as a valuable signal to determine if a product or product family is perceived as a premium offering, as brand managers and marketers intend, or if it is considered below-premium. Causalysis, a platform meticulously designed by economists, tackles price elasticity within a business framework, ensuring the approach maintains academic rigor.
Customer price sensitivity, expressed quantitatively as price elasticity, is an essential metric for both B2B and B2C companies. The accurate modeling of price elasticity coefficients continues to be a prominent data science application within sales, marketing, and revenue management domains. At its core, price elasticity indicates the percentage change in unit sales—demand for our products or services—resulting from a specific percentage change in price.
Addressing hypothetical market scenarios requires quantitative measures to understand how choices respond to changes in prices or other product characteristics, assuming all other factors remain constant. While these measures alone may not fully answer all economic questions related to market outcomes, they are often indispensable. Some practical examples of the importance of price elasticity estimation include:
· Serving as a critical input in complex price or trade promotional optimization efforts, enabling organizations to systematically determine the optimal price for each product, and decide when and how to apply discounts to maximize specific financial objectives.
· Providing strategy teams with insights into how changes in the prices of other products in the portfolio, or those of competitors' substitute or complementary products, impact overall business performance.
· Equipping finance teams with the ability to conduct detailed what-if analyses regarding specific price changes or discount actions, thereby improving financial forecasting.
· Allowing marketers to segment products and consumers into precise behavioral clusters, enhancing targeted marketing efforts.
· Informing sales operations and management on the extent to which discounts can be applied while still ensuring profitability, leading to more strategic pricing decisions.
· Enabling revenue management teams to formulate more impactful pricing and promotional strategies, optimizing revenue potential.
· Giving supply chain teams visibility into how promotional actions influence inventory levels, and how significant clearance pricing discounts need to be to deplete overstocked inventory, thus optimizing stock management.
It becomes evident that price elasticities are vital inputs for strategic planning, product and customer segmentation, what-if scenario analyses, and optimization exercises. They play a crucial role in enabling swift and informed managerial decision-making regarding pricing actions.
Causalysis integrates these critical insights seamlessly, empowering businesses to harness the power of price elasticity for enhanced strategic planning, optimized pricing strategies, and overall operational efficiency. By leveraging the sophisticated analytics provided by Causalysis, companies can stay ahead of market trends and drive sustainable growth.
How to estimate price elasticity?
Creating accurate models of price elasticities is a complex task due to the crucial importance of precise estimates. For instance, when deciding on the allocation of promotional funding across your entire distribution or retail channel, achieving maximum accuracy is essential.
Many businesses lack access to specialized economists who can apply state-of-the-art methods for deriving precise price elasticity estimates. Therefore, in practice, businesses typically rely on three main approaches to estimate price elasticities: historical sales data analysis, survey-based methods, and econometric modeling:
1. Customer survey methods: These survey methods are typically used to estimate acceptable price ranges for new products and to estimate consumer willingness to pay and optimal price points. An expansion of these methods, Conjoint analysis, is a more comprehensive research approach that lets us understand consumer preferences to optimize product features, including pricing. These methods suffer from the same problems as all survey approaches that estimate numerical parameters. While well designed surveys are useful for identifying the directional effect of a change, surveys can also be prone to measurement errors, such as ambiguous, leading, or confusing questions, or inappropriate or inadequate response options. Surveys can also have limited depth and flexibility, as they may not capture the nuances, motivations, or contexts of the respondents' answers.
2. Simplistic regression/machine learning models: The log-log regression is still the most popular method for estimating price elasticities in most traditional industries, with the log of units regressed on the log of price and other variables, the price coefficient, in this case, is roughly the price elasticity. Other approaches include models like ensemble models or deep learning. The main issue with these approaches is delineated in the economic literature: using price changes to predict demand changes is problematic because it is not only that price affects demand but the demand for a product can affect its price too. For example, a successful ad campaign can make the demand for a product skyrocket, allowing its producer to charge a higher price. These methods get lost in the causality puzzle and are generally unable to derive an unbiased estimate.
3. Causal analysis models: These models use institutional knowledge to infer the causes and effects with an organization (e.g., an increase in price to increase marketing spending will increase churn, therefore revenue, but on the other hand, it will expand the customer base bringing new clients in, especially in the markets where the marketing campaign was held. This narrative makes clear that there is a sequential logic in what causes what. In the previous example with price and demand, one needs to use an “instrument”: An instrument is a variable that affects price and not demand, such as cost of inputs. This way you can make sure that when price is moving is not due to demand, and the simultaneity puzzle breaks. As a result, the bias is removed from the estimation. Causalysis is able to employ all the aforementioned techniques (graph analysis, instrumental analysis) and additional approaches to remove bias (A/B test) making it a necessary tool for every data scientist who desires results of academic quality, but at a minimal effort.
A Case Study
Estimating price elasticity via, say, A/B test is the first step towards maximizing returns. Having a complete picture of the causal chain of events, allows the business to apply the price elasticity estimates to derive policy. Causalysis can help an analyst understand how consumers react to changes in different variables. The analyst, then, can recommend the what the optimal price setting is, even in case where the result would be counterintuitive for a human: Uber leverages its extensive real-time data to continually refine its understanding of price elasticity, using this insight to dynamically adjust pricing strategies across different customer segments. Traditionally, raising prices is expected to reduce demand, a principle upheld by economic theory. However, Uber's experience revealed a curious anomaly: increasing the surge multiplier from 2.0x to 2.1x at times led to an increase in ride bookings. This unexpected outcome stemmed not from customers preferring to pay higher prices but rather from their perception that a slight increase indicated a sophisticated algorithm at play, making the pricing strategy seem more reasonable.
This case study illustrates the complexity of applying theoretical price elasticity concepts in practical, multivariate settings. It highlights how empirical observations often diverge from conventional economic predictions, showcasing the nuanced interplay between data-driven insights and theoretical frameworks in real-world scenarios.
Uber's approach underscores the evolving nature of economic principles in application, demonstrating that effective pricing strategies require a blend of empirical validation and theoretical understanding. In navigating these complexities, businesses must balance data-driven decision-making with the dynamic nature of consumer behavior and market conditions. Thus, while economic theory provides a foundational framework, its adaptation to real-world contexts often necessitates a more flexible and empirical approach.
Conclusion
Calculating price elasticities is a basic component of most policy design efforts. Therefore, getting it right matters! Utilizing Causalysis tools is crucial to achieving your overarching objective of establishing a robust pricing and promotional optimization capability that distinguishes your company within the industry.
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