Business Challenge

Proving campaign effectiveness and measuring true audience impact requires more than impressions and reach estimates. Marketers need to demonstrate actual audience behavior change, foot traffic lift, and ROI using real population movement data to validate media investments and optimize future campaigns.

Common Scenarios:

Out-of-home advertisers measuring foot traffic lift to advertised locations
Retail brands tracking store visitation increases during advertising campaigns
Restaurants measuring customer acquisition from targeted media campaigns
Event promoters analyzing attendance lift from advertising efforts
Media agencies proving campaign effectiveness to clients with actual audience behavior data

Required Inputs

Campaign Details: Flight dates, media placements, target audience definitions
Measurement Locations: Place IDs for stores, venues, or destinations being promoted
Baseline Period: Pre-campaign timeframe for comparison analysis
Target Demographics: Audience segments most likely to respond to campaign
Success Metrics: KPIs such as visitation lift, new customer acquisition, or audience engagement

API Workflow

Step 1: Establish Baseline Performance

Measure pre-campaign audience behavior and visitation patterns to establish performance benchmarks.

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

{
    "place_id": [1234, 1235, 1236]
}

Baseline Metrics from Response:

Visitor Volume: visits, visits_unique_persons, activities
Audience Demographics: segments array with percent and index values
Geographic Distribution: county, dma, state arrays with weekly_visits
Temporal Patterns: days array with hourly patterns and dwell distribution
Engagement Metrics: visits_avg_dwell and visits_frequency_per_person

Response Structure (baseline metrics):

{
    "places": [
        {
            "name": "Campaign Measurement Locations",
            "place_id": [1234, 1235, 1236],
            "activities": 2127778,
            "visits": 1693268,
            "visits_unique_persons": 1169734,
            "visits_avg_dwell": 184,
            "visits_frequency_per_person": 1.45,
            ...
        }
    ],
    "place_count": 3,
    "segments": [
        {
            "type": "consumer",
            "category": "shopping_behavior",
            "description": "Frequent retail shopper",
            "id": "shop_001",
            "percent": 0.234,
            "index": 156,
            "motionworks_segment_id": "abc123-segment-id"
        }
        ...
    ],
    "county": [...],
    "dma": [...],
    "state": [...],
    "days": [...],
    "dwell": [...],
    ...
}

Step 2: Define Target Audience Cohorts

Identify specific audience segments that align with campaign targeting for precise measurement.

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 3: Track Campaign Period Performance

Monitor audience behavior and visitation patterns during active campaign flights using the same measurement approach.

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

Note: The Placecast Profiles API provides the most recent data available. For historical period analysis, you would make this same call during the campaign period to capture that timeframe's data.

{
    "place_id": [1234, 1235, 1236]
}

Campaign Period Monitoring:

Daily Visitation Changes: Compare current visits to baseline
Audience Composition Shifts: Changes in segments percentages and indices
Geographic Response: Variations in county and dma visitor distributions
Engagement Pattern Changes: Differences in dwell and temporal patterns

Step 4: Analyze Target Cohort Geographic Presence

Use Popcast At Home to understand where your target audience is concentrated geographically.

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

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

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 5: Measure Post-Campaign Impact

Assess sustained impact and behavior changes following campaign completion using the same Placecast measurement approach.

Implementation Note: Campaign lift measurement requires collecting baseline data before the campaign, during the campaign, and after campaign completion, then calculating the differences to determine attributed lift.

Lift Calculation Framework:

Baseline Performance: Pre-campaign visitor metrics
Campaign Performance: During-campaign visitor metrics
Post-Campaign Performance: Sustained behavior changes
Statistical Validation: Compare results to control markets or historical trends

Expected Outputs

Campaign Performance Report

Visitation Lift: Quantified increase in foot traffic during campaign period
Audience Impact: Demographic and behavioral changes in visitor composition
Geographic Performance: Market-by-market campaign effectiveness based on DMA analysis
Temporal Analysis: How campaign impact varied over time and sustained post-campaign

ROI Analysis

Cost Per Visit: Campaign cost divided by incremental visits generated
Customer Acquisition Cost: Cost to acquire new visitors/customers during campaign
Audience Quality Assessment: Changes in visitor engagement and dwell time
Media Efficiency: Performance comparison across different campaign elements

Strategic Insights

Audience Optimization: Which demographic segments (segments with highest index scores) responded best
Geographic Effectiveness: Markets showing strongest visitation increases
Engagement Patterns: Changes in visits_avg_dwell and temporal visit patterns
Long-term Impact: Sustained changes in visitor behavior post-campaign

Business Application

Campaign Optimization

Use real-time performance data to optimize active campaigns and improve results.

Optimization Strategies:

Geographic Reallocation: Focus budget on markets showing strongest response
Audience Refinement: Target demographic segments with highest engagement
Timing Optimization: Adjust campaign flights based on optimal visitor response periods
Location Selection: Prioritize measurement locations showing strongest lift

Future Campaign Planning

Apply learnings from measured campaigns to improve future media investments.

Strategic Applications:

Audience Targeting: Refine target segments based on proven response patterns
Market Selection: Prioritize markets that showed strongest campaign lift
Budget Allocation: Distribute investment based on demonstrated ROI by market
Campaign Structure: Optimize flight patterns based on visitor response data

Client Reporting and Business Development

Use population intelligence to demonstrate campaign value and win future business.

Reporting Framework:

Executive Dashboards: High-level campaign performance and visitor lift metrics
Detailed Analysis: Granular audience behavior and response patterns
Competitive Benchmarking: How campaign performance compares to market baselines
Success Stories: Case studies demonstrating proven campaign effectiveness

Out-of-Home Advertising Applications

Measure billboard and digital display campaign effectiveness using population movement data.

Strategic Applications:

Foot Traffic Attribution: Measure incremental store visits generated by billboard campaigns using baseline vs. campaign period visitor volumes
Audience Delivery Verification: Validate billboard audience delivery against campaign targets using actual demographic data from segments arrays
Cross-Media Attribution: Analyze how billboard campaigns complement digital and traditional media using geographic visitor pattern changes
Campaign Optimization: Use real-time visitor lift data to optimize billboard creative rotation, scheduling, and geographic allocation during active campaigns

Related Use Cases

Location Audience Analysis - Understand baseline audience for campaign planning
Finding High-Value Advertisers - Use campaign performance data for prospect development
Competitive Audience Analysis - Benchmark campaign performance against competitors

Technical Resources

Placecast Profiles API - Visitation measurement and audience analysis
Popcast API - Cohort identification and geographic audience analysis