n today's data-driven business environment, selecting the ideal location for a new store involves more than just intuition and basic demographic analysis. Businesses must delve into various metrics that provide strategic insights. This blog post explores a case study that demonstrates how a specialty coffee shop chain expands into a mid-sized urban area by combining the Purchasing Power Index (PPI), employment per workplace area, and competitor location analysis to select the best store location.
Understanding the Key Metrics
1. Purchasing Power Index (PPI)
The Purchasing Power Index is a measure of the economic capacity of a specific area. It reflects the average income of residents and their ability to purchase goods and services.
Key Components:
- Income Levels: Higher income levels often equate to greater purchasing power, making these areas attractive for premium products.
- Cost of Living: The index considers living costs, affecting disposable income and actual purchasing power.
- Spending Patterns: Insights into how residents spend their money can guide product or service offerings.
2. Employment per Workplace Area
Employment per workplace area provides insights into how many people work in a specific region. This metric is crucial for businesses that rely on the presence of a working population, such as restaurants, retail stores, and service providers.
Key Components:
- Workplace Density: High employment density often translates to a higher demand for nearby services.
- Industry Type: Different industries can affect consumer behavior and needs.
- Commuter Influx: Areas with significant commuter populations can present opportunities for additional customer bases.
3. Competitor Location Analysis
Understanding where competitors are located is essential for assessing market saturation and identifying opportunities for differentiation.
Key Components:
- Market Saturation: Identify areas with a high concentration of competitors, which might be oversaturated.
- Gaps in Service: Look for areas underserved by existing competitors where a niche can be filled.
- Competitive Advantage: Determine how your business can stand out in a competitive landscape.
Case Study: Specialty Coffee Shop Chain Expansion
Business Profile
In this case study, a specialty coffee shop chain seeks to expand its presence into a mid-sized urban area. The chain aims to attract affluent customers looking for high-quality coffee and a cozy environment for work or socializing.
Step 1: Analyzing Purchasing Power Index (PPI)
The first step in the process involves identifying areas with a high Purchasing Power Index. High-PPI areas indicate that residents have higher disposable incomes, making them more likely to spend on premium products.
- Data Analysis: The city is mapped out by PPI, focusing on neighborhoods with above-average indices, ideally over 110, indicating higher purchasing power than the national average.
- Target Neighborhoods: Affluent residential areas and business districts are targeted where the chain's target demographic resides and works.
Step 2: Assessing Employment per Workplace Area
Next, employment density is evaluated to understand where the working population is concentrated. This information helps identify locations with potential for high foot traffic and customer conversion during work hours.
- Data Collection: Data on employment density is obtained across various parts of the city, identifying business districts and areas with significant office spaces.
- Target Locations: Areas with high employment per workplace area are highlighted, ideally above the city’s average, indicating a bustling work environment with potential customers looking for coffee breaks.
Step 3: Competitor Location Analysis
Conducting a thorough competitor analysis is essential for understanding the competitive landscape and identifying areas with potential market gaps.
- Competitor Mapping: Existing coffee shops and cafes are plotted on a city map, identifying clusters and areas with fewer competitors.
- Gap Analysis: Neighborhoods with a high PPI and significant employment density but low competition are identified, presenting opportunities for a new entrant.
Combining the Metrics
By integrating these metrics, potential locations are narrowed down that align with the chain's target market and strategic goals.
Example Areas Identified:
- Downtown Business District: High employment density and significant foot traffic from office workers. Moderate competition but room for differentiation through premium offerings and a unique atmosphere.
- Affluent Suburb: High PPI with limited competitors. Potential to attract both local residents and commuters looking for quality coffee and a comfortable space.
- Emerging Urban Neighborhood: Moderate PPI with a growing population of young professionals. Some competitors, but an opportunity to capture market share by offering a distinctive experience.
Final Decision
After evaluating the data, the coffee shop chain chooses to open its first new location in the downtown business district. This choice is based on the area’s high employment density, promising foot traffic, and the potential to capture a sizable share of the professional market with a premium product.
Conclusion
Choosing the perfect store location requires a comprehensive analysis of various metrics. By leveraging the Purchasing Power Index, employment per workplace area, and competitor location analysis, businesses can make data-driven decisions that align with their strategic objectives. This case study demonstrates how these metrics can be combined to identify optimal locations and maximize the chances of success in a competitive market.
If you are interested in obtaining demographics and employment, income and purchasing power data, or Points of Interest (PoI) data, for the countries in Europe at various geography levels, or if you would like to explore our data and analytical capabilities further, please contact us at contact@geolocet.com. We would be happy to assist you in your location analysis and business strategy needs.