Investor Insights: How It Works (Simple Explanation)¶

This document explains in plain English how the Investor Insights page analyzes restaurant data to help investors make smart decisions.

Overview¶

The Investor Insights page uses three different methods to analyze restaurants:

Market Opportunity Analysis - Finds gaps in the market (places where there's demand but not enough restaurants)
Location Hotspots - Groups restaurants by location to find the best areas to open a restaurant
Competitor Benchmarking - Analyzes competitors to understand what it takes to succeed

Let's break down each method in simple terms.

1. Market Opportunity Analysis¶

What It Does¶

This analysis finds types of restaurants (like Korean food, Ramen, Vegan food) that customers love (high ratings) but don't have many competitors yet. These are the "sweet spots" for new businesses.

How It Works (Step by Step)¶

Step 1: Choose Your Area¶

First, the system lets you pick a city: Odessa, Midland, or Both. It then only looks at restaurants in that area.

Example: If you select "Odessa", it only analyzes restaurants in Odessa.

Step 2: Break Down Restaurant Categories¶

Here's the tricky part: Each restaurant can belong to multiple categories. For example, a restaurant might be listed as "Mexican, Tex-Mex, Restaurants".

The system splits these up so it can count how many restaurants exist in each individual category.

Example: - "El Taco Loco" has categories: "Mexican, Tex-Mex" - The system creates two entries: - El Taco Loco counted as "Mexican" - El Taco Loco counted as "Tex-Mex"

This way, the system can see how many restaurants exist for each type of food.

Step 3: Calculate Statistics for Each Category¶

For each food type (Mexican, Italian, Korean, etc.), the system calculates:

Average Rating: What's the average star rating for all restaurants of this type?
If there are 5 Korean restaurants with ratings of 4.5, 4.0, 4.8, 4.2, and 4.7 stars, the average is about 4.4 stars.
High average = customers are happy with this type of food
Average Review Count: How many reviews do restaurants of this type typically get?
If most Korean restaurants have 100-200 reviews, the average might be 150 reviews.
High review count = many customers are visiting these restaurants
Number of Competitors: How many restaurants of this type already exist?
If there are only 2 Korean restaurants, that's low competition.
If there are 50 Mexican restaurants, that's high competition.

Example Results: - Korean food: Average rating 4.5 stars, average reviews 145, only 2 restaurants exist - Mexican food: Average rating 3.8 stars, average reviews 245, 152 restaurants exist

Step 4: Find Opportunities¶

The system looks for categories that meet two conditions:

High Quality: Average rating of 4.0 stars or higher
This means customers are satisfied with this type of food
If customers are happy, there's proven demand
Low Competition: Fewer than 5 restaurants exist
This means there's room in the market
Less competition = easier to succeed

Example: - ✅ Korean food (4.5 stars, 2 restaurants) = OPPORTUNITY ✅ - ✅ Ramen (4.3 stars, 3 restaurants) = OPPORTUNITY ✅ - ❌ Mexican food (3.8 stars, 152 restaurants) = NOT an opportunity (too much competition)

Step 5: Score Each Opportunity¶

For each opportunity found, the system calculates an "Opportunity Score" to rank them from best to worst.

The Formula in Plain English: - Multiply the average rating by average review count (this shows how popular AND good the food is) - Then divide by the number of competitors plus 1 (this rewards categories with fewer competitors)

Why This Works: - Higher rating = customers love it - Higher review count = many people are visiting - Fewer competitors = easier to enter the market - The score combines all three factors

Example Calculation: - Korean: (4.5 stars × 145 reviews) ÷ 3 competitors = 217.5 score (BEST!) - Ramen: (4.3 stars × 98 reviews) ÷ 4 competitors = 105.2 score - Vegan: (4.0 stars × 67 reviews) ÷ 4 competitors = 67.0 score

The highest score wins - Korean food is the best opportunity!

Step 6: Show Results¶

The system displays the top 10 opportunities, ranked from highest to lowest score. This tells investors which food types are most promising.

2. Location Hotspots (Geographic Clustering)¶

What It Does¶

This analysis groups restaurants by their physical location (latitude and longitude coordinates) to find geographic "hotspots" - areas where restaurants tend to cluster together. This helps investors choose the best location for a new restaurant.

How It Works (Step by Step)¶

Step 1: Get Restaurant Locations¶

Every restaurant has GPS coordinates (latitude and longitude) - basically the exact spot on a map where it's located.

Example Coordinates: - Restaurant A: Latitude 31.8458, Longitude -102.3676 (somewhere in Odessa) - Restaurant B: Latitude 31.8460, Longitude -102.3678 (very close to Restaurant A) - Restaurant C: Latitude 32.0234, Longitude -102.1345 (much farther away, in Midland)

Step 2: Group Restaurants by Location¶

The system uses an algorithm called "KMeans Clustering" to automatically group restaurants that are physically close together.

How Clustering Works (in simple terms): 1. The system decides it wants to create 4 groups (clusters) 2. It finds restaurants that are close to each other and puts them in the same group 3. It does this for all restaurants until each one belongs to a group

Example: - Cluster 0: 312 restaurants in central Odessa (all near each other) - Cluster 1: 287 restaurants in northern Midland - Cluster 2: 234 restaurants in eastern Odessa - Cluster 3: 368 restaurants in western Midland

Each cluster represents a geographic area where many restaurants are located.

Step 3: Analyze Each Cluster¶

For each geographic cluster, the system calculates statistics:

Which City: Which city (Odessa or Midland) has the most restaurants in this cluster?
Example: Cluster 0 is mostly in Odessa
Average Rating: What's the typical rating for restaurants in this area?
Example: Cluster 0 has an average of 3.8 stars
High rating = customers like restaurants in this area
Average Reviews: How popular are restaurants in this area?
Example: Cluster 0 averages 145 reviews per restaurant
High review count = lots of customer traffic
Number of Restaurants: How many restaurants are already in this cluster?
Example: Cluster 0 has 312 restaurants
High count = mature dining district
Low count = underserved area (opportunity!)
Center Point: What are the exact coordinates of the cluster's center?
Example: Cluster 0 center is at Latitude 31.8458, Longitude -102.3676
This is the exact spot an investor might target

Example Results: - Cluster 0 (Odessa): 3.8 stars average, 312 restaurants, center at [31.8458, -102.3676] - Cluster 1 (Midland): 3.6 stars average, 287 restaurants, center at [32.0234, -102.1345]

Step 4: Generate Strategic Insights¶

The system identifies the best cluster by looking for: - High average rating (customers are satisfied) - Not too many restaurants (room for growth)

Example Insight: "Cluster 0 in Odessa has a high average rating of 3.8 stars but only 312 restaurants. This may be a target zone for expansion."

What This Means: - The area has good customer satisfaction (high ratings) - There's room for more restaurants (not completely saturated) - This is a promising location for a new restaurant

Step 5: Visualize on a Map¶

The system creates an interactive map where: - Each restaurant is shown as a colored dot - Different clusters have different colors (red, green, blue, yellow) - You can hover over dots to see restaurant details - You can see where geographic hotspots are located

This visual helps investors see exactly where they should consider opening a restaurant.

3. Competitor Benchmarking¶

What It Does¶

This analysis helps investors understand the competitive landscape for a specific type of restaurant in a specific city. It answers questions like: "If I want to open a Korean restaurant in Odessa, what am I up against?"

How It Works (Step by Step)¶

Step 1: Select Your Target¶

You pick: - Cuisine Type: What type of food? (e.g., Korean, Mexican, Italian) - City: Which city? (Odessa or Midland)

Example: "Korean restaurants in Odessa"

Step 2: Find All Competitors¶

The system finds all restaurants that match your selection.

Example: If you select "Korean" and "Odessa", it finds all Korean restaurants in Odessa (maybe there are 2 of them).

Step 3: Calculate Competitive Metrics¶

For those competitors, the system calculates five key numbers:

Metric 1: Average Rating¶

What it is: The average star rating across all competitors.

How to understand it: This is the rating level you need to beat to stand out.

Example: - Competitor 1: 4.5 stars - Competitor 2: 4.0 stars - Competitor 3: 3.8 stars - Average: 4.1 stars

What this means for you: If you open a Korean restaurant in Odessa, you need to aim for at least 4.1 stars (preferably higher) to compete.

Metric 2: Median Rating¶

What it is: The "middle" rating when all ratings are sorted from lowest to highest.

Why it matters: Sometimes one restaurant has an extremely high or low rating that skews the average. The median is a more stable number.

Example: - Ratings: 3.5, 3.7, 3.8, 3.9, 4.5 - Median: 3.8 (the middle value)

What this means: The typical Korean restaurant in Odessa has about 3.8 stars.

Metric 3: Average Review Count¶

What it is: The average number of reviews that competitors have.

How to understand it: This shows how visible/popular restaurants in this market are.

Example: If competitors average 150 reviews, this means the typical Korean restaurant in Odessa has been reviewed about 150 times.

What this means for you: You'll need similar visibility (around 150 reviews) to compete effectively.

Metric 4: Number of Competitors¶

What it is: Simple count of how many restaurants of this type exist in this city.

How to understand it: This tells you how competitive the market is.

Example Categories: - Less than 3 competitors: Low competition (easier to enter the market) - 3-5 competitors: Moderate competition (balanced market) - 6 or more competitors: High competition (saturated market)

Example: If there are 135 Mexican restaurants in Odessa, that's HIGH competition (very saturated market).

What this means for you: - Few competitors = easier to stand out - Many competitors = need to be really good or offer something unique

Metric 5: Most Common Price Tier¶

What it is: The price range ($, $$, $$$, $$$$) that most competitors use.

How to understand it: This shows what customers expect to pay in this market.

Example: If most Korean restaurants charge $$ (moderate pricing), that's what customers expect.

What this means for you: You should probably price similarly, or have a good reason if you price differently (much higher or lower).

Step 4: Generate Strategic Insights¶

The system combines all these numbers into practical advice.

Example Output: "If you open a new Korean restaurant in Odessa, you'll be competing mostly at the $$ price point. The average rating you need to beat to stand out is about 4.1★, and the market currently only has 2 direct competitors."

What This Tells You: - Price: Customers expect moderate pricing ($$) - Quality Target: Aim for at least 4.1 stars (ideally higher) - Competition Level: Only 2 competitors = LOW competition = Good opportunity!

Step 5: Competition Level Assessment¶

The system automatically categorizes the competition level:

🟢 Low Competition (< 3 competitors): "Few competitors means easier market entry"
🟡 Moderate Competition (3-5 competitors): "Balanced market with room for differentiation"
🔴 High Competition (6+ competitors): "Many competitors - focus on unique value proposition"

What This Means: - Low Competition: Go ahead! It's easier to succeed here. - Moderate Competition: You can succeed, but make sure you're good. - High Competition: Be prepared - you need to be excellent or offer something unique to stand out.

How All Three Methods Work Together¶

These three analyses complement each other:

Market Opportunity tells you WHAT type of restaurant to open (Korean? Ramen? Vegan?)
Location Hotspots tells you WHERE to open it (which geographic area)
Competitor Benchmarking tells you HOW to compete (what quality level, pricing, etc.)

Complete Example: 1. Market Opportunity Analysis: "Korean food is a great opportunity!" 2. Location Hotspots: "Open it in Cluster 0 in central Odessa" 3. Competitor Benchmarking: "Aim for 4.1+ stars, price at $$, only 2 competitors exist"

Together, this gives you a complete investment strategy.

Why These Methods Work¶

Market Opportunity Works Because:¶

High ratings = customers are satisfied = proven demand exists
Low competition = less competition = easier to succeed
The scoring formula balances quality, popularity, and competition

Location Hotspots Work Because:¶

Restaurants cluster in areas with good traffic, demographics, and accessibility
If restaurants in an area have high ratings, it's probably a good location
Geographic proximity helps restaurants benefit from nearby customer traffic

Competitor Benchmarking Works Because:¶

Understanding your competition helps you set realistic goals
Knowing the average rating tells you what quality level customers expect
Understanding price points helps you position your restaurant correctly
Count of competitors tells you how hard it will be to succeed

Limitations and Things to Keep in Mind¶

Market Opportunity:¶

The thresholds (4.0 stars, <5 competitors) are somewhat arbitrary - other thresholds might work better
A restaurant can be counted in multiple categories, which might slightly skew the numbers
High ratings don't always mean there's room for more restaurants

Location Hotspots:¶

Just because restaurants are close together doesn't mean it's a good location (could be a bad area)
Doesn't account for traffic patterns, demographics, or accessibility
The algorithm picks 4 clusters, but there might be natural groupings at different numbers

Competitor Benchmarking:¶

Category matching is based on text search (might miss similar restaurants with different category names)
Ratings can change over time, so the analysis is only accurate as of when data was collected
Doesn't account for restaurant size, capacity, or other factors that affect competition

Summary¶

The Investor Insights page uses three analytical approaches:

Market Opportunity Analysis: Finds food types with high customer satisfaction but few competitors
Location Hotspots: Groups restaurants by location to find the best geographic areas
Competitor Benchmarking: Analyzes competitors to understand what it takes to succeed

All three methods use statistical calculations (averages, counts, groupings) to turn restaurant data into actionable business intelligence for investors.

The end goal: Help investors make data-driven decisions about what type of restaurant to open, where to open it, and how to compete successfully.