Price Elasticity in Marketing Analytics

Understanding what is Price elasticity

Price elasticity measures how sensitive the demand for a product is to changes in its price. In other words, It is a crucial tool for understanding how changes in price affect customer demand for your products, it quantifies the percentage change in demand for a product resulting from a 1% change in its price. This knowledge empowers you to set optimal pricing strategies that maximize revenue and profitability.

The formula for price elasticity of demand (PED) is:

PED = (% change in quantity demanded) / (% change in Price)

Let's say you're analyzing the price elasticity of demand for a Pen Drive. Data on the price of the Pen Drive and the quantity sold over a period of time as follows.

Price of Pen Drive:

• Initially: INR 1000

• After price increase: INR 1100

Quantity sold:

• Initially: 800 units

• After price increase: 750 units

Case Study Example:

Using the formula for price elasticity:

PED = (% change in quantity demanded) / (% change in Price)

We can calculate the percentage changes:

We can calculate the percentage changes:

$\mathrm{\%}$$\text{ change in quantity demanded}$$=$$\frac{(}{}$$\frac{800}{}$$\frac{-}{}$$\frac{1000}{}$$\frac{)}{}$$\frac{1000}{}$$\times$$100$$\mathrm{\%}$$=$$-$$20$$\mathrm{\%}$% change in quantity demanded = (750 - 800)/ 800 

                                                = -(5/80) 

                                                = -6.25%

$\mathrm{\%}$$\text{ change in price}$$=$$\frac{(}{}$$\frac{600}{}$$\frac{-}{}$$\frac{500}{}$$\frac{)}{}$$\frac{500}{}$$\times$$100$$\mathrm{\%}$$=$$20$$\mathrm{\%}$% change in price = (1100 - 1000)/ 1000 

                           = 10%

Now, substitute these values into the PED formula:

PED = (-6.25%/10%) = -0.625

Interpretation:

• PED > 1 (Elastic Demand): A small price increase leads to a larger decrease in demand, and vice versa. Think luxury goods or substitute products.

• PED = 1 (Unit Elastic Demand): A 1% price change results in a 1% change in demand. This is uncommon but indicates proportionate movement.

• PED < 1 (Inelastic Demand): A price change has a relatively small impact on demand. This applies to necessities or products with few close substitutes.

Benefits:

$�$$�$$�$$=$$\frac{-}{}$$\frac{20}{}$$\frac{\mathrm{\%}}{}$$\frac{20}{}$$\frac{\mathrm{\%}}{}$$=$$-$$1$

• Identify price points that maximize revenue or profit.

• Understand customer sensitivity to price changes.

• Develop targeted pricing strategies for different customer segments.

• Make informed pricing decisions during promotions and discounts

Machine Learning Models

• Linear Regression: This is a foundational technique that estimates a straight-line relationship between price and demand. It's a good starting point but might not capture complex non-linear relationships.

• Log-Log Regression: This transforms both price and quantity data into logarithms, often leading to a more linear relationship for elasticity calculations.

• Decision Trees: These create a tree-like structure to segment data based on price points and other factors, revealing how elasticity varies across different customer groups or price ranges.

• Support Vector Regression (SVR): This technique finds a hyperplane that best separates price and quantity data points, providing a good estimate of the overall elasticity but might not capture local variations.

• Random Forests: This ensemble method combines multiple decision trees, leading to more robust and accurate elasticity estimates that account for non-linearities.

• Conjoint Analysis: This technique involves creating hypothetical product bundles with varying prices and features to understand customer preferences and price sensitivity. It's particularly useful for new product launches.

• Bayesian Analysis: This probabilistic approach incorporates prior knowledge or beliefs about elasticity into the model, leading to more nuanced results when historical data is limited.

• Data Availability: Simpler models work well with limited data, while machine learning techniques require a larger dataset.

• Desired Model Complexity: Basic models are easier to interpret, while advanced models capture more complex relationships but can be less transparent.

• Business Goals: The chosen algorithm should align with your specific goals, such as maximizing revenue, targeting specific customer segments, or understanding long-term price effects.