Restaurant Location Gap Analysis: Unlock Opportunities with Data Scraping

In the dynamic food service industry, location is not merely a point on a map; it serves as a crucial factor in determining a restaurant’s success or failure. When restaurants open up in the wrong neighborhood, foot traffic could be poor, revenue targets will be unmet, and the last few places will eventually close their doors. However, identifying an underserved or growing area can be a goldmine for both new entrants and established brands.

This is exactly what Restaurant Location Gap Analysis determines. It identifies geographic properties that have unmet demand or a lack of sufficient restaurant options. When combined with data scraping methodologies from Google Maps, restaurant listing platforms, and demographic data sources, gap analysis for restaurant locations can yield great insight and accuracy.

In this guide, we will give an overview of how restaurant businesses can utilize data scraping to conduct a gap analysis for restaurant locations and tap into high-potential opportunities. We will also showcase a couple of uses and examples of demographic, competitive, and geographic property interactions, along with actionable insights.

What is Restaurant Location Gap Analysis?

Restaurant location gap analysis is a strategic method to identify potential locations where restaurant demand exceeds current supply. Restaurant location gap analysis is often used by businesses that are looking to scale, like restaurant chains, franchise concepts, and aspiring entrepreneurs in the food business. The ability to find markets where certain types of specific cuisines or restaurant formats are underserved can help businesses to enter into markets that are more focused on demand potential and less about competitive saturation.

In the past, doing an analysis of supply and demand was mainly through lengthy methods like field surveys or manual data collection efforts. Today, automated data scraping and cloud-based tools can allow businesses to acquire large-scale real-time data in a more efficient and extremely efficient manner. The mining of sourdough, with comprehensive back-end analytics, enables us to build better insights into consumer behavior and market demand.

Why Location Is Essential in the Restaurant Industry

In the restaurant industry, location is crucial. The quality of food, atmosphere, and pricing will play very important roles in a restaurant’s success; however, the actual location of the restaurant will have a direct effect on its accessibility, visibility, and potential customer base when opening a restaurant. A well-located restaurant will experience consistent foot traffic, increased brand awareness, and exceptional performance in terms of supporting delivery and item suppliers.

On the other hand, poor location decisions might leave you with little or no visibility, a low level of customer engagement, and lost operational costs, which will take away from your bottom line. In approaching this issue with a data-driven gap analysis for the location and restaurant type, the operator is allowed to examine location offerings that speak directly to the location profile, ultimately giving a restaurant a greater chance to survive in a community since there are competing choices and factors in the marketplace.

The Role of Google Maps in Gap Analysis

Google Maps is a robust database of geographic and business intelligence information, which makes it a powerful support for doing location gap analysis. By utilizing the data from Google Maps, businesses can pull a wide range of location intelligence that can include restaurant location, business type, ratings, review volume, and open and closing hours. They can even capture user-generated data, including uploaded photos, to visit peak times.

When this data is mapped, you can more easily observe neighborhoods that have density while lacking diverse dining. You may also identify areas that reflect a specific cuisine type that are overrepresented, poorly rated, or met with poor consumer report ratings, which could indicate gaps for some level of differentiation. Heat maps visualizing consumer sentiment may allow restaurant planners to see competitive density mapping in a quicker fashion.

Scraping Restaurant Listings from Directories

Restaurant directory sites are great options for finding restaurant listings that include detailed categorizations of restaurants, including reviews and user-generated tags that provide a more detailed understanding of preferences. Businesses scrape restaurant directory sites to pull different data types, such as data on restaurants’ names, cuisines, price ranges, menus, features, and reviews. The true insights come not just from scraping the directory’s listings but by conducting analysis on restaurant patterns in totality, like the kinds of restaurants in a particular area or what menu items were most mentioned.

This structured and unstructured data can show businesses lower-populated neighborhoods, trending cuisine types, and frequently recurring customer complaints. This information is valuable when you supplement it with scraped data from maps and demographics, because they provide a more complete, well-rounded analysis of dining and the market space for where they plan to enter.

Using Demographics & Behavior Data

Demographic data can illuminate factors like population density, demographics, or population makeup, as well as levels of income, education, and types of employment. A lot of demographic data can often be scraped or gathered through public APIs or government records, enabling companies to align their restaurant concepts with the desired population profile based on the local audience.

Behavior data primarily includes the usefulness of food delivery, online search behavior, and social media sentiments. These data patterns provide context to consumer preferences, like whether a neighborhood prefers dine-in options, takeout fast food, or health-based food items. Together, data demographics & behavior offer a full picture to assess the actual potential of a location.

Competing Density and Mapping Market Saturation

Understanding the business density of competitors in a certain area is essential to avoid entering an oversaturated market. If conducting a city or regional study by scraping restaurant data, one can map competitors (by type, price point, and customer rating) in the area. Then, by overlaying the competitors with demographic information and estimated foot traffic, one can assess if the targeted market is oversaturated with one type of cuisine or simply has limited overall variety.

For example, if one district has numerous pizza stores, it might be a hyper-competitive area, while if a district has none of Mexican food, you might be looking into an opportunity. Hence, a market saturation map based on competitor mapping can help you identify white spaces and gaps in the market, may help you consider or not have a certain strategic position in mind, and can help you minimize risk for planning an expansion.

Hyperlocal Targeting Based on Micro-Geography

Micro-geography looks at the details of specific locations, down to the level of blocks or streets. This type of geography allows businesses to truly understand where the opportunities arise, beyond general neighborhood trends. For instance, an area around multiple IT parks may lack restaurants that are open late, creating a great opportunity for a 24/7 diner, or a location near schools with a kid-friendly snack offering that’s affordable.

Scraping data at this level can help identify food deserts, provide insights into how many apartments or flats are around, identify foot traffic around important maps and destinations, or generate location recommendations for food and beverage outlets, etc. Hyperlocal targeting allows businesses to access information not just about the locations they want to target but also to reinterpret the product they wish to offer, the price they want to target, and what type of messages they want to use to target consumers.

Case Study: Finding the Ideal Location for a Vegan Restaurant

If a vegan restaurant brand were looking to expand in Mumbai, the first step in a data-informed approach would be to scrape Google’s map to find all vegan and vegetarian restaurants, mapping the locations, ratings, and reviews. In addition, it is possible to scrape directories of restaurant listing websites to analyze what plant-based dishes are trending, price sensitivity, and delivery preferences.

Using publicly available demographic data, the second phase of this approach would be to source suburbs with younger, more health-conscious, and higher-income demographics. By overlaying demographic information with the competitive landscape, the brand could identify suburbs with high demand but few vegan options. The final analysis would produce the most viable suburbs with both unmet demand and a target customer.

Challenges and Limitations to Consider

Data scraping has limitations, even if it can offer an abundance of current data. Data pulled from websites such as Google Maps and online restaurant directories can be old, scarce, or formatted inconsistently. Certain types of platforms use anti-scraping techniques, slowing down your presentation or analysis, hindering access, or otherwise skewing your data. Finally, it is unrealistic to expect to find all significant variables, such as local rents, zoning restrictions, and other factors found in the physical world, digitally.

Remember that raw data is not context-rich either. Community sensibility, civic regulations, and seasonal restrictions can limit companies or organizations from successfully operating a restaurant, but may not be easily measurable using data scraping. For example, a project examining how competition in the region impacts long-term sustainability would at some point be dependent upon the research team and members’ perceptions, community knowledge, and interviews with restaurant experts. Data scraping and digital measures will still be useful; however, survey data, consultations with experts, and local intelligence may be equally important, or even more important than the digital measure in the overall analysis.

Conclusion: Data-Driven Location Strategy Is the Future

The restaurant opening process has changed. No longer do companies simply depend on instinct to evaluate viable locations for a new outlet. As the costs associated with an opening can be reflective of stagnant or lost revenue from a closed location, it has become essential to approach restaurant location gap analysis through data scraping. While it remains important to engage in the regular due diligence of analyzing competition and demographics, companies are now anticipating potential Main Street locations and utilizing digital tools to evaluate consumer behavior and ownership lifecycle, and assess future patterns. This will determine if a brand has found a site that has unrealized high-yield potential.

Progressive restaurant brands utilize this process to achieve value in growth and expand their brand while improving customer satisfaction and establishing their resilience in the business cycle. As both the tools for gathering intelligence and visualization improve, a data-first approach to location planning will become the smartest long-term investment, with the heavy pressures and competition on food service entities.

If you’re keen to adopt this data-first approach and gain a competitive advantage, Foodspark offers sophisticated web scraping and data analytics solutions for the food and restaurant industries. From finding gaps in the market to estimating demand and determining optimized delivery zones, Foodspark allows restaurant businesses to grow progressively using data-first principles.