In This Article

  1. Introduction
  2. Determinants of Demand
  3. Elasticity Concepts
  4. Estimation Methods
  5. Forecasting Techniques
  6. Conclusion

Introduction

Demand estimation involves quantifying the relationship between demand for a product and factors that influence it. Demand forecasting uses this understanding to predict future demand. Both are essential for pricing, production planning, and strategic decision-making.

Determinants of Demand

Demand Function:

Qd = f(P, Ps, Pc, Y, T, E, N, A)

Where:

  • Qd = Quantity demanded
  • P = Price of the product
  • Ps = Price of substitutes
  • Pc = Price of complements
  • Y = Consumer income
  • T = Consumer tastes/preferences
  • E = Consumer expectations
  • N = Number of buyers
  • A = Advertising expenditure

Elasticity Concepts

Price Elasticity of Demand

Ed = % Change in Quantity / % Change in Price

Ed = (ΔQ/Q) / (ΔP/P)

ElasticityValueMeaning
Elastic|Ed| > 1Quantity very responsive to price
Inelastic|Ed| < 1Quantity not very responsive
Unit Elastic|Ed| = 1Proportional response

Income Elasticity

Ey = % Change in Quantity / % Change in Income

  • Ey > 0: Normal good
  • Ey < 0: Inferior good
  • Ey > 1: Luxury good

Cross-Price Elasticity

Exy = % Change in Qx / % Change in Py

  • Exy > 0: Substitutes
  • Exy < 0: Complements

Demand Estimation Methods

1. Consumer Surveys

Directly ask consumers about purchase intentions at various prices.

  • Pros: Direct, can test hypotheticals
  • Cons: Intentions ≠ actual behavior, costly

2. Market Experiments

Test different prices in controlled settings or test markets.

  • Pros: Actual behavior, not stated preferences
  • Cons: Expensive, competitors may react

3. Regression Analysis

Statistically estimate demand function from historical data.

Linear Demand Model:

Q = a + b₁P + b₂Y + b₃Ps + b₄A + ε

Demand Forecasting Techniques

Qualitative Methods

  • Expert opinion / Delphi method
  • Sales force estimates
  • Customer intention surveys

Quantitative Methods

  • Trend projection: Extrapolate historical patterns
  • Moving averages: Smooth out fluctuations
  • Exponential smoothing: Weight recent data more
  • Econometric models: Regression with economic variables

Conclusion

Key Takeaways

  • Demand depends on price, income, substitutes, complements, preferences
  • Price elasticity measures responsiveness to price changes
  • Income elasticity distinguishes normal, inferior, and luxury goods
  • Cross elasticity identifies substitutes and complements
  • Estimation methods: surveys, experiments, regression
  • Forecasting combines qualitative and quantitative approaches
  • Understanding demand is essential for pricing and planning