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Understanding price elasticity lets companies optimize pricing strategies, maximize revenue, and maintain competitiveness. Incorporating AI capabilities in price elasticity strategies enables companies to make faster and more proactive data-driven decisions on revenue growth management.
In this first article on our series delving into the data science and AI behind revenue growth management, we explore the fundamentals of price elasticity. We also tackle its importance to businesses — particularly to CPG companies — and how an AI-enhanced, data-driven approach to price elasticity can improve commercial performance.
The fundamentals of price elasticity: Knowing the consumer response to pricing
Price elasticity is a measure of customer sensitivity to changes in price. It provides a basis for understanding and predicting consumer response to price. Per the law of demand, a certain movement in demand is expected with every change in price. This relationship — between price and quantity sold — is referred to by economists as the price elasticity of demand.
The price elasticity of demand is calculated by dividing the percentage change in quantity demanded by the percentage change in price:
To simplify, price elasticity (PE) values can be classified into three different buckets:
- Elastic (PE > 1): If the demand changes significantly in response to a slight change in price, the demand is considered elastic. In this case, consumers are sensitive to price changes, and a price increase would lead to a proportionally larger decrease in quantity demanded, and vice versa.
- Inelastic (PE < 1): If the demand changes only slightly in response to a substantial change in price, the demand is considered inelastic. In this case, consumers are not very responsive to price changes, so a price increase would lead to a proportionally small decrease in demand, and vice versa.
- Unit elastic (PE = 1): If the percentage change in demand is exactly equal to the percentage change in price (i.e., PED =1), the product is considered unit elastic. In other words, consumers are willing to buy more or less of a product exactly in proportion to the change in its price. This means the total spending on the product remains the same.
Price elasticity doesn’t just focus on price itself. The strength of price elasticity might also depend on these factors:
- Substitutes available on the market: The more substitutes available, the more elastic the product might be, as customers tend to switch.
- Essentiality and upmarket products: These would have various levels of price elasticity. Shoppers will buy a basic product despite of price where high-class products will have much higher price elasticity. Here, a chance for significant sales can be expected after a price change.
- Seasonality: The same product might react in diverse ways after price changes during different periods of time.
- Market trends: Certain market environments also need to be tracked as price elasticity might be changed due to certain macroeconomic events.
Figure 1. A sample visualization of a dashboard simulating what-if scenarios for a particular product’s revenue based on certain price elasticity
Understanding how price elasticity affects revenue
Imagine a company that sells a popular soft drink with a retail price of US$2 per can. The company has decided to conduct a pricing experiment to determine the impact of price changes on sales volume and total revenue.
Here’s a sample initial scenario:
- Price: US$2 per can
- Quantity sold: 10,000 cans per week
- Total revenue: US$20,000
Let’s consider a scenario with elastic demand. The company reduces the price of the soft drink to US$1.5 per can. As a result, the quantity sold increases to 15,000 cans per week. The total revenue (US$1.5 × 15,000) is US$22,500. Here, the demand is elastic because the percentage increase in quantity demanded (50%) is greater than the percentage decrease in price (25%). Despite the lower price, the increase in sales volume leads to higher total revenue.
To illustrate inelastic demand, let’s assume that the company raises the price of the soft drink to US$2.5 per can. As a result, the quantity sold decreases to 8,000 cans per week. The total revenue (US$2.5 × 8,000) is US$20,000. The demand is inelastic because the percentage decrease in quantity demanded (20%) is less than the percentage increase in price (25%). Despite the decrease in sales volume, the higher price leads to higher total revenue.
For unitary elasticity, let’s assume that the company adjusts the price of the soft drink to $2.25 per can. As a result, the quantity sold decreases to 8,888 cans per week. The total revenue (US$2.25 × 8,888) is US$20,000. In this scenario, demand exhibits unitary elasticity because the percentage decrease in quantity demanded (11.12%) is equal to the percentage increase in price (12.5%). The change in price has no net effect on total revenue.
These examples show how changes in price influence sales volume and total revenue depending on the price elasticity of demand. Understanding price elasticity helps the company make informed pricing decisions to maximize revenue and profitability.
Figure 2. A sample visualization of a dashboard showing comparisons of revenue over time based on certain price elasticity
The role of price elasticity in growing and managing revenue
By analyzing and understanding price elasticity, businesses can:
Optimize price strategy. By analyzing price elasticity, companies can determine the optimal price points that maximize revenue and profitability.
Maximize price sensitivity. Price elasticity analysis allows companies to identify price-sensitive customer segments and products. By segmenting customers based on their price sensitivity, companies can tailor pricing strategies and promotions to maximize revenue from each segment.
Forecast demand. By understanding how changes in price affect demand, companies can anticipate the impact of pricing decisions on sales volume and revenue. This enables better inventory management, production planning, and resource allocation to meet customer demand more efficiently.
Set pricing policies. For instance, companies may set pricing floors or ceilings based on price elasticity to ensure that prices are aligned with demand and market conditions. This helps prevent pricing decisions that might have a negative impact on revenue or profitability.
Optimize promotional strategies. By understanding the price sensitivity of customers during promotional periods, companies can design promotions that maximize incremental sales and revenue. This includes optimizing the timing, duration, and magnitude of promotions to achieve the desired impact on sales.
Enable competitive positioning. By comparing price elasticity with competitors, companies can determine whether they have pricing flexibility or need to adjust prices to remain competitive. This helps companies maintain or improve market share and profitability relative to competitors.
Mitigate revenue risks. Price elasticity analysis helps companies identify potential risks associated with pricing decisions.
Why business leaders need to have a price elasticity strategy
As shown earlier, price elasticity can directly affect a company's revenue, profitability, and competitiveness in the market. Its key success measure is finding the optimal level of price, which, in turn, shapes discount and promotional strategies.
Understanding price elasticity enables leaders to predict the impact of promotions on consumer demand and sales. It also allows decision-makers to manage their product portfolio more effectively by assessing the price sensitivity of each product.
Amazon, for example, is known for its dynamic pricing strategy, where prices for products can fluctuate based on factors such as demand, competitor pricing, and inventory levels. E-commerce companies can draw on an AI-driven price elasticity strategy to optimize prices in real time.
Ride-hailing companies like Uber, too, uses dynamic pricing to balance supply and demand. During peak times, Uber would increase prices to encourage more drivers to be available. This ensures that customers can get a ride when they need it. Transportation or logistics companies can adopt a similar approach by implementing a pricing model that adjusts shipping rates based on various factors such as demand intensity, shipment urgency, fuel costs, and competitor pricing.
Video-streaming platforms like Netflix also use a price elasticity strategy, adjusting subscription pricing based on factors such as content offerings, market demand, and subscriber behavior. Netflix, for example, periodically increases prices for new or existing subscribers as it adds more content and features to its platform. Similar businesses can use the same strategy to capture more value from its subscriber base while continuing to invest in content production and acquisition.
Knowing price elasticity is crucial to CPG companies, as it enables them to better understand the performance of their brands, learn more about their consumers, and adapt to regional and global market dynamics, such as inflation or volatilities in supply and value chains.
In fact, Lingaro’s commercial analytics practice worked with a multinational CPG company to revamp its revenue growth management strategy. It included integrating fragmented data sources and automating the calculation of price changes, which are then visualized in an interactive dashboard. Lingaro’s solution also enabled the CPG company to better understand the effectiveness of their trade promotions. With a new, data-driven approach to RGM, they were able to achieve a 7% year-on-year growth in total revenue. They also rolled out the solution to new markets in other countries.
Preparing the data for AI-enhanced pricing elasticity strategies
Assessing price elasticity requires analyzing vast amounts of data, which can be a struggle to do manually. With AI, price elasticity strategies can be automated and enriched with advanced analytics, predictive modeling, and real-time decision-making capabilities. AI also gives greater precision, agility, and profitability in pricing decisions.
Today’s AI has advanced such that it can model general price elasticities and also more detailed heterogenous effects to dynamic pricing. However, there are considerations:
- Data access and its scope
- Historical and planned pricing experimentation
- The company’s pricing strategy
Usually, retailers have access to very granular data for every single transaction in every point of sale (PoS). As long as there is enough information in the data (resulting from experimenting in the past with various price levels), the sky’s the limit. This gives the opportunity for making very advanced pricing decisions. However, personalized pricing decisions that focus solely on maximizing KPIs, without considering the bigger picture, can also come with certain cost that is difficult to quantify.
For instance, consumers might not be happy with frequent price changes. It may also be difficult to build a sustainable and strong brand image, especially if competitors don’t change their prices that often and if their pricing strategy is easier for consumers to follow. There is a trade-off between potential marginal gain from personalized pricing decisions and potential loss resulting from lack of consumer loyalty. This is especially true when combined with “bad marketing,” which can easily happen given social media’s impact. These considerations are why advanced AI capabilities should be developed to support a holistic pricing strategy and to strengthen the company’s brand.
In our next article, we’ll explore how AI can be effectively applied to price elasticity strategies as demonstrated by our case study on one of the world’s leading beverage companies.
