Exercise 1 Reilly Relative Draw Bonus 2 Exercise 3 Huff D
Exercise 1 Reilly Relative Draw Bonus 2exercise 3 Huff D
Exercise 1: Reilly – Relative Draw Bonus 2 Exercise 3: Huff – Destination Odds Bonus 9 Exercise 2: Converse –Indifference Points Bonus 5 City Population A 13,250 B 68,700 A 13mi Customer 33mi B 31mi Customer 15mi 60mi 55mi 50mi 50mi 50mi North Platt 6.000 Northeast 22,000 Lake Loch 54,000 Portsmouth 35,000 Central City 80,000 Bay Shores 5,000 Student Union Barnes Crossing Kroger Wal-Mart 65 Minutes 9 minutes 12 minutes Kroger 100,000 Wal-Mart 250,000 Barnes 650,000 Tupelo Oxford K W B - K W B 15 miles fm B __________ 33 miles fm B __________ Mark distances & draw Trade Area
Paper For Above instruction
The assignment requires analyzing a series of location-based and trade area problems using various quantitative methods such as Reilly’s Huff Model and Converse’s Indifference Point technique. These methods help determine optimal trade areas for retail locations based on population, distance, travel time, and consumer behavior. This paper will systematically address these tasks through detailed calculations, trade area mapping, and geographic analysis to illustrate how these models inform retail site selection and market potential estimation.
Introduction
Understanding customer distribution and trade areas is fundamental for effective retail site planning and market analysis. Retailers leverage models like Reilly’s Law of Retail Gravitation, Huff’s Model, and Converse’s Indifference Point analysis to evaluate potential store locations and their market coverage. These models integrate geographic, demographic, and consumer preference data, allowing strategists to optimize retail placement, maximize market share, and improve profitability.
Reilly’s Relative Draw Model
Reilly’s Law of Retail Gravitation posits that consumers will patronize the closest store, balancing distance and store attractiveness (Reilly, 1931). The formula compares the distances to competing stores, adjusting for their respective sizes or sales potential. The model predicts that the trade area boundary is located where the "pull" from each store balances—essentially where the consumer's preference shifts from one retailer to another based on proximity and store attractiveness.
In this scenario, with population data for two cities—City A with 13,250 residents and City B with 68,700 residents—and customer data indicating their distances of 13 miles from City A and 33 miles from City B, Reilly’s Law can be employed. The formula for the boundary point involves the ratio of populations and the distance ratio, often expressed as:
\[
\frac{d_{A}}{d_{B}} = \sqrt{\frac{Q_{A}}{Q_{B}}}
\]
where \(Q_A\) and \(Q_B\) are the store attributes (or population sizes), and \(d_A\) and \(d_B\) are the distances from the customer to each city.
Huff Model for Destination Odds
The Huff Model estimates the probability that a consumer located at a certain point will patronize a specific store based on store size and the distance to the consumer (Huff, 1964). The model's formula is:
\[
P_{i} = \frac{S_{i} / d_{i}^{\beta}}{\sum_{j} S_{j} / d_{j}^{\beta}}
\]
where \(P_i\) is the probability of patronage to store \(i\), \(S_i\) is the store size or attractiveness, \(d_i\) is the distance from the consumer to store \(i\), and \(\beta\) is a distance decay parameter, often set around 2.
Applying this model to the given retail locations such as Kroger and Wal-Mart, with respective store sizes of 100,000 and 250,000, and customer travel times/distances (e.g., 65 minutes, 9 minutes, 12 minutes), allows estimation of market share likelihoods. For example, customers 15 miles from B or 33 miles from B can be analyzed to determine their probable store choices.
Converse’s Indifference Points
Converse’s model helps establish the boundary line where consumers are indifferent between two stores. It involves setting the utility or attractiveness equal for both options, leading to an indifference point where the two stores exert equal attraction on the consumer.
Assuming store attractiveness is proportional to store size or sales potential, and considering consumer travel distances or times, the indifference point can be calculated by equating the utilities. This involves solving for the distance where a consumer prefers one store versus the other, factoring in travel time or distance decay effects.
Using the sample data (e.g., 15 miles from B and 33 miles from B), the exercise prompts marking the distances and drawing trade areas accordingly, which visually represents the market boundaries and potential customer catchments.
Mapping the Trade Areas
The practical application involves plotting the mentioned locations and their surroundings on a geographic map, illustrating the trade areas derived from the models. Distances and times are used to draw boundaries, showing which segments of the population are more likely to patronize either store based on proximity and attractiveness. For example, trade areas around the Student Union, Kroger, Walmart, and Barnes Crossing are identified by their respective travel times and population sizes.
Conclusion
This analysis demonstrates how Reilly’s Law, Huff’s Model, and Converse’s Indifference Points together provide insights into retail market dynamics. Each model contributes uniquely: Reilly’s law defines the spatial boundary based on population and distance; Huff’s Model estimates customer patronage probability considering store size and distance decay; Converse’s method identifies indifference points where consumer preferences shift. These tools are essential for retail site selection, trade area analysis, and strategic planning.
Accurate geographic and demographic data, combined with these models, enable retailers to optimize store locations, enhance customer reach, and increase competitive advantage. As urbanization intensifies and consumer preferences evolve, these analytical techniques will remain vital for informed retail decision-making.
References
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