Business Challenge

Making informed location decisions requires understanding the true audience potential of a site before significant investment. Traditional site selection relies on demographic estimates and theoretical trade areas, while population intelligence provides actual movement patterns, visitor demographics, and proven trade area boundaries based on real behavior.

Common Scenarios:

Retailers evaluating new store locations based on actual foot traffic potential
Restaurants analyzing neighborhood demographics and dining patterns
Healthcare providers understanding patient population and access patterns
Banks assessing branch locations based on customer movement and banking behaviors
Developers evaluating mixed-use project potential based on area activity patterns

Required Inputs

Potential Location Coordinates: Latitude/longitude for proposed sites
Market Boundary: Geographic area for analysis (radius, county, custom polygon)
Comparison Criteria: Similar locations or competitor sites for benchmarking
Business Requirements: Target demographics, visitor volume thresholds, trade area size

API Workflow

Step 1: Identify Target Demographics

Define the audience characteristics most valuable to your business model.

API Call: POST /cohorts/search
→ API Documentation: Popcast API

{
    "motionworks_data_product_name": "Popcast At Home",
    "search_text": "retail shopping"
}

Response Structure:

{
    "cohorts": [
        {
            "motionworks_segment_id": "b2862ed4-68c9-4124-971b-1c8e03362451",
            "cohort_description": "Frequent retail shopper past 30 days"
        }
        ...
    ]
}

Step 2: Analyze Market Demographics

Understand population characteristics in your target market area using Popcast At Home.

API Call: POST /measures/popcast/at_home
→ API Documentation: Popcast API

{
    "motionworks_segment_id": "b2862ed4-68c9-4124-971b-1c8e03362451",
    "geography_type": ["DMA"]
}

Key Analysis Points:

Population density by market (persons and composition_index)
Demographic concentration (composition_percent above market average)
Geographic distribution across DMAs or MSAs
Market penetration opportunities (composition_index > 100)

Response Structure:

{
    "motionworks_segment_id": "b2862ed4-68c9-4124-971b-1c8e03362451",
    "geographic_type": ["DMA"],
    "customer_segment_name": "Frequent retail shopper past 30 days",
    "year": 2022,
    "persons": 45200906,
    "all_persons": 333633487,
    "composition_percent": 0.13548072289278323,
    "results": [
        {
            "geography_id": "US2020XDMA501",
            "description": "New York, NY",
            "persons": 3917399,
            "all_persons": 22015625,
            "composition_index": 131,
            "composition_percent": 0.177937215046132,
            "geo_type": "DMA",
            "customer_segment_name": "Frequent retail shopper past 30 days",
            "vintage": "20230909"
        }
        ...
    ],
    "ids": [...],
    "vintage": "20230909",
    "pagination": {...}
}

Step 3: Find Similar Existing Locations

Identify comparable locations in your target market to understand performance benchmarks.

API Call: POST /msearch
→ API Documentation: Places API

{
    "filter": {
        "place_name": "shopping center",
        "city": "New York",
        "state": "NY"
    },
    "pagination": {
        "page": 1,
        "page_size": 20
    },
    "sort": {
        "active": "place_name",
        "direction": "asc"
    }
}

Response Structure:

{
    "status": "success",
    "data": {
        "results": [
            {
                "place_id": "318105",
                "place_name": "Manhattan Shopping Center",
                "place_type_name": "Shopping Mall",
                "street_address": "123 Broadway",
                "city": "New York",
                "state": "NY",
                "zip_code": "10001",
                "dma_id": "501",
                "dma_name": "New York, NY",
                ...
            }
            ...
        ],
        "pagination": {
            "page": 1,
            "page_size": 20,
            "total_results": 45,
            "total_pages": 3
        }
    }
}

Location Discovery Analysis:

Find existing locations with similar business models
Identify successful locations in target demographics
Map competitive landscape and market gaps
Collect place IDs for further analysis

Step 4: Analyze Comparable Location Performance

Use Placecast Profiles to understand visitor patterns at similar existing locations.

API Call: POST /measures/placecast/report
→ API Documentation: Placecast Profiles

{
    "place_id": [318105, 318107, 318108]
}

Response Structure (key performance metrics):

{
    "places": [
        {
            "name": "Aggregated Shopping Centers",
            "place_id": [318105, 318107, 318108],
            "activities": 2127778,
            "visits": 1693268,
            "visits_unique_persons": 1169734,
            "visits_avg_dwell": 184,
            ...
        }
    ],
    "place_count": 3,
    "segments": [
        {
            "type": "consumer",
            "category": "shopping_behavior",
            "description": "Frequent retail shopper",
            "id": "shop_001",
            "percent": 0.234,
            "index": 156,
            "motionworks_segment_id": "segment-uuid-here"
        }
        ...
    ],
    "county": [...],
    "dma": [...],
    "postal_code": [...],
    ...
}

Key Metrics:

Visitor volume: visits and visits_unique_persons
Engagement quality: visits_avg_dwell and activities
Demographic match: segments array with index scores
Trade area definition: Geographic distribution in county, dma, postal_code arrays

Step 5: Map Actual Trade Areas

Analyze visitor origin patterns to understand realistic trade area boundaries.

Trade Area Analysis (from Placecast response geographic arrays):
The response includes multiple geographic distribution arrays:

county array: County-level visitor origins
postal_code array: ZIP code visitor distribution
dma array: Media market visitor patterns
state array: State-level visitor origins

Each contains weekly_visits and daily_visits for trade area mapping.

Trade Area Definition:

Primary Trade Area: Geographic areas providing 60-70% of visitors
Secondary Trade Area: Areas contributing 20-30% of visitor volume
Travel Patterns: ZIP codes and counties with highest weekly_visits
Market Penetration: daily_visits density by geographic area

Expected Outputs

Site Evaluation Report

Market Opportunity Score: Quantified potential based on population and activity data
Trade Area Mapping: Geographic boundaries with visitor volume projections
Demographic Analysis: Target audience presence and characteristics
Competitive Assessment: Market saturation and differentiation opportunities

Strategic Recommendations

Location Ranking: Prioritized list of potential sites with performance projections
Market Entry Strategy: Optimal timing and positioning recommendations
Trade Area Optimization: Geographic focus areas for marketing and operations
Risk Assessment: Market challenges and mitigation strategies

Investment Intelligence

ROI Projections: Expected performance based on population intelligence
Market Validation: Data-driven confirmation of location viability
Expansion Strategy: Systematic approach to multi-location growth
Portfolio Optimization: How new locations complement existing sites

Business Application

Site Selection Process

Replace demographic assumptions with actual population movement and behavior data for location decisions.

Implementation Framework:

Market Screening: Use population density and demographic filters to identify viable markets
Site Evaluation: Score potential locations based on visitor potential and target audience match
Trade Area Analysis: Define realistic market boundaries for business planning
Competitive Intelligence: Understand market dynamics and positioning opportunities

Business Planning

Use population intelligence for accurate market sizing and business projections.

Planning Applications:

Revenue Forecasting: Base projections on actual visitor volume potential from comparable locations
Market Sizing: Quantify addressable market based on population and behavior data
Marketing Budget: Allocate resources based on trade area insights and audience concentration
Operational Planning: Staff and inventory planning based on projected visitor patterns

Risk Mitigation

Reduce location investment risk through data-driven market validation.

Risk Factors Analysis:

Market Saturation: Competitive density and market capacity
Demographic Shifts: Population trends that could impact long-term viability
Accessibility Changes: Transportation and development factors affecting visitor access
Economic Indicators: Market economic health and consumer spending patterns

Out-of-Home Advertising Applications

Evaluate billboard and digital display location potential using population intelligence.

Strategic Applications:

Billboard Site Selection: Assess new billboard locations based on actual traffic patterns and demographic alignment with advertiser targets
Market Entry Analysis: Use population density and composition_index data to identify high-value markets for billboard expansion
Competitive Gap Analysis: Identify underserved markets with high audience potential but limited existing billboard inventory
ROI Forecasting: Project billboard performance using comparable location visitor volumes and demographic match rates

Related Use Cases

Location Audience Analysis - Analyze performance of existing locations
Competitive Audience Analysis - Understand competitor strategies and market positioning
Finding High-Value Advertisers - Leverage location insights for business development

Technical Resources

Popcast API - Population analysis and demographic insights
Placecast Profiles API - Location visitor analysis
Places API - Location discovery and competitive analysis