Bus 640 Week 2 Consumer Demand Analysis And Estimation App

Bus 640 Week 2 Consumer Demand Analysis And Estimation Applied Problem

Assess and analyze consumer demand using provided scenarios involving attribute weightings, utility calculations, and demand functions. Complete calculations, interpret results, and discuss real-world applications and decision-making methods based on attribute weighing.

Paper For Above instruction

Consumer demand analysis is a vital component of strategic decision-making in business, providing insights into consumer preferences and how various factors influence their purchasing decisions. This paper explores two applied problems related to consumer demand analysis and estimation. The first problem involves evaluating venue choices for a restaurant based on consumer attribute preferences, while the second delves into demand function estimations for a specific product—Newton’s Donuts.

Problem 1: Venue Selection for a Restaurant Based on Consumer Attribute Preferences

Patricia’s decision-making scenario involves evaluating two restaurant options—steak and pizza—across two locations: suburban Los Angeles and the greater Los Angeles metropolitan area. The key attributes considered are taste, location, and price, with their importance varying based on location type. For the suburban area, taste is three times as important as location and twice as important as price. Conversely, in the metropolitan area, location is three times as important as taste and twice as important as price. Both restaurants are similarly priced, but they have different attribute ratings.

In the given scenario, for a suburban Los Angeles location, we assign weights based on the importance ratios: taste (T), location (L), and price (P). The relative importance of taste is three times that of location, and twice that of price, which leads to the following weights:

  • Taste: 6 units
  • Location: 2 units
  • Price: 3 units

Normalization is necessary to convert these to proportions for utility calculations:

  • Total weight = 6 + 2 + 3 = 11
  • Normalized weights:
    • Taste: 6/11 ≈ 0.545
    • Location: 2/11 ≈ 0.182
    • Price: 3/11 ≈ 0.273

Using the attribute ratings provided for each restaurant—steak restaurant (Taste=80, Location=55, Price=65) and pizza restaurant (Taste=70, Location=80, Price=50)—we calculate the total utility for each by multiplying each attribute rating by their respective weights and summing the results:

  • Steak Restaurant Utility (Suburban):

    = (80×0.545) + (55×0.182) + (65×0.273) ≈ 43.6 + 10.0 + 17.7 ≈ 71.3

  • Pizza Restaurant Utility (Suburban):

    = (70×0.545) + (80×0.182) + (50×0.273) ≈ 38.2 + 14.6 + 13.7 ≈ 66.5

Based on these calculations, Patricia should pursue the steak restaurant if she intends to operate in the suburban Los Angeles area, as it provides a higher expected utility of approximately 71.3 compared to 66.5 for the pizza option.

In the context of the Los Angeles metropolitan area, attribute importance shifts, with location now being the most influential. Using similar steps, the weights are assigned as follows:

  • Location: 6 units
  • Taste: 2 units
  • Price: 3 units

Normalized weights:

  • Total = 6 + 2 + 3 = 11
  • Location: 6/11 ≈ 0.545
  • Taste: 2/11 ≈ 0.182
  • Price: 3/11 ≈ 0.273

Utility calculations:

  • Steak Restaurant:

    = (80×0.182) + (55×0.545) + (65×0.273) ≈ 14.6 + 30.0 + 17.7 ≈ 62.3

  • Pizza Restaurant:

    = (70×0.182) + (80×0.545) + (50×0.273) ≈ 12.7 + 43.6 + 13.7 ≈ 70.0

In this case, Patricia should consider opening the pizza restaurant because it yields a higher utility (~70.0) than the steak restaurant (~62.3) in the metropolitan area, aligning with the shifted attribute importance.

Incorporating Probabilities in Decision-Making

When factoring in the probability of successfully establishing a restaurant in either location, the expected utility must be weighted by these probabilities. For example, if the probability of finding a successful venue in a suburban area is 0.7 and in a metropolitan area is 0.3, the overall expected utility (EU) for each option is:

  • EU (Steak): (0.7 × 71.3) + (0.3 × 62.3) ≈ 49.9 + 18.7 ≈ 68.6
  • EU (Pizza): (0.7 × 66.5) + (0.3 × 70.0) ≈ 46.6 + 21.0 ≈ 67.6

Thus, the steak restaurant remains the favored option with a slightly higher expected utility, explaining the importance of considering probabilistic success estimates in strategic location decisions.

Real-World Application and Advantages/Disadvantages of Attribute-Based Decision Making

This kind of decision-making methodology—assigning weights to attributes and calculating utilities—mirrors real-world scenarios like product selection, hiring decisions, or investment choices where multiple factors influence outcomes. Companies often analyze customer preferences, product features, or market conditions this way to optimize offerings and strategies.

Benefits of this approach include clarity in decision criteria, ability to incorporate both qualitative and quantitative data, and structured comparisons between options. However, drawbacks include potential bias in assigning weights, oversimplification of complex choices, and reliance on subjective ratings which can introduce errors or inconsistencies.

Problem 2: Demand Function Analysis for Newton’s Donuts

The demand function for Newton’s Donuts is: Qx = -14 – 54Px + 45Py + 0.62Ax, where all variables are defined as per the problem statement. Current values of the independent variables are given as Ax=120, Px=0.95, and Py=0.64.

First, substituting the known values into the demand function:

Qx = -14 – 54(0.95) + 45(0.64) + 0.62(120)

Calculating each term:

  • -14 remains as is
  • – 54 × 0.95 = – 51.3
  • 45 × 0.64 = 28.8
  • 0.62 × 120 = 74.4

Adding these:

Qx = –14 – 51.3 + 28.8 + 74.4 = (–14 – 51.3) + 28.8 + 74.4 = (–65.3) + 103.2 = 37.9

Thus, the current estimated demand for Newton’s Donuts is approximately 37,900 donuts (since demand is in thousands).

Calculating Price Elasticity of Demand

Price elasticity of demand (PED) is calculated as:

PED = (dQ/dP) × (P/Q)

From the demand function, the partial derivative of Qx with respect to Px is –54. Therefore,

PED = (–54) × (P / Q)

Using current values, P = 0.95 and Q = 37.9:

PED = (–54) × (0.95 / 37.9) ≈ (–54) × 0.0251 ≈ –1.36

This elastic demand indicates that a 1% increase in price would result in approximately a 1.36% decrease in quantity demanded, pointing to a relatively elastic demand.

Inverse Demand Curve Derivation

To derive the inverse demand curve, solve the original demand function for Px:

Qx = –14 – 54Px + 45Py + 0.62Ax

Rearranged for Px:

  • 54Px = –14 + 45Py + 0.62Ax – Qx
  • Px = (–14 + 45Py + 0.62Ax – Qx) / 54

Substituting the current values (Py=0.64, Ax=120):

Px = (–14 + 45×0.64 + 0.62×120 – Qx) / 54

= (–14 + 28.8 + 74.4 – Qx) / 54

= (89.2 – Qx) / 54

Pricing Decision and Advertising Expenditure

Considering the constant marginal cost of $0.15 per donut, profit maximization occurs where marginal revenue equals marginal cost. Given the elasticity and demand responsiveness, reducing the price might increase total revenue if the demand is elastic, as indicated by the elasticity measure of –1.36. However, since the current price of $0.95 exceeds the marginal cost of $0.15, increasing sales volume could improve profits. Nonetheless, the decision depends on detailed marginal analysis not fully provided here.

Regarding advertising, higher expenditure (Ax) associated with increased demand suggests that investing more could boost sales. The positive coefficient (0.62) indicates that additional advertising spending correlates with higher demand, and thus, strategic increases in advertising expenditures could be beneficial, assuming marginal costs and revenue projections justify the investment.

Conclusion

Consumer demand analysis, through utility calculations and demand function estimations, provides comprehensive insights for strategic decision-making. Proper understanding of attribute importance and elasticity helps firms optimize product offerings, pricing, and marketing efforts, ultimately influencing profitability and market positioning. Nevertheless, these quantitative methods should be complemented with qualitative assessments and market understanding to ensure robust decision-making.

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