The report results include the pipeline used, date created, when the report started, and when the report completed. The output is separated into 7 sections:

1. Summary
2. Dashboard
3. Lookalike
4. Personas
5. Explorer
6. Charts
7. Downloads

This information provides a complete audit trail and transparency into the model, allowing users to understand how insights are generated and tie them back to audience behavior and performance, rather than relying on a black-box approach.

Summary

The Summary section provides a high-level overview of the analysis, including the Report ID, Advertiser ID, and Date Range, along with an automatically generated Executive Summary.

This summary explains:

• Key demographic drivers of conversions
• Audience composition and persona breakdown
• Geographic performance and distribution
• Lookalike expansion opportunities (if applicable)
• Strategic recommendations and action items

Tip: You can input the Executive Summary into your preferred LLM (e.g., ChatGPT or Claude) to quickly generate a presentation deck or case study based on the results.

Dashboard

The Dashboard section provides a visual overview of audience composition and demographic drivers of performance. It highlights which census-based features influence conversions, how audiences are distributed geographically, and how different demographic segments perform.

Dashboard Sections:

• Tile Metrics
• Most Important Demographic Categories
• Feature Impact Distribution — SHAP Beeswarm
• Demographic Sub-Feature Impact
  • Shows the top three most important features based on your audience dynamically.
• Geographic Performance — Click a State to See ZIP Codes
• Cost per Conversion by Demographic Category

Tile Metrics

• Analysis Model: Campaign Mode — results are based on ad-attributed conversions. Impressions served by your lines that led to pixel fires within the lookback window. The model learns which demographics respond to your ad.
• Optimization: Conversions or Clicks
  • Conversions: Optimizing for conversions (pixel fires). The model predicts which demographics drive conversion events.
• R² (Coefficient of Determination): Measures how much of the conversion rate variance is explained by demographics. 5–15% is typical. Demographics are just one signal among many.
• RMSE (Root Mean Squared Error): Lower is better. Measures the average prediction error in the same units as the target (conversion rate).
• MAE (Mean Absolute Error): Average absolute difference between predicted and actual conversion rates. Less sensitive to outliers than RMSE.
• Scored ZIPs: Total ZIP codes scored with demographic affinity. Example: 1,850 positive, 25,483 negative or zero (this figure changes dynamically).
• Total Spend: Total ad spend across all campaign ZIPs in this analysis period.
• Avg Cost/Conv: Average cost per conversion across all campaign ZIPs. Lower is more efficient.
• Samples: Number of ZIP codes included in the analysis after filtering for (>300 events) and outlier removal.
• Features: Number of census demographic features used by the model.
• Reliability: Pipeline quality assessment. Passed 3/4 checks — the results change dynamically.

Most Important Demographic Categories

This section shows the total SHAP importance aggregated by demographic category, helping identify which broad demographic dimensions have the greatest influence on the model’s conversion rate predictions. Use this to understand which broad demographic dimensions matter the most.

Chart View

What It Shows:

• Each bar represents a demographic category (e.g., Household Income, Age, Housing, Transportation)
• Bar height reflects total SHAP importance across all features within that category

Interpretation:

• Taller bars indicate categories that have a greater impact on the model’s predictions
• Categories at the top represent the strongest drivers of conversion likelihood
• Lower bars indicate categories with less influence on performance

Table View

Provides a detailed breakdown of individual features within each demographic category, including:

• Category: High-level demographic group
• Feature: Specific sub-feature (e.g., income bracket, age range)
• Mean Abs SHAP: Overall importance of the feature
• Mean SHAP: Direction of impact (positive or negative influence)

Users can download this table as a CSV file for further analysis.

Feature Impact Distribution — SHAP Beeswarm

This section shows how each demographic feature impacts conversion predictions across all ZIP codes.

What It Shows:

• Each dot represents a single ZIP code prediction
• X-axis shows SHAP value (impact on predicted conversion rate)
• Color represents the feature value:
  • Red = higher values
  • Blue = lower values
• Features are ordered by importance (top = most important)

Interpretation:

• Points to the right increase predicted conversion rate
• Points to the left decrease predicted conversion rate
• Color helps identify whether higher or lower values drive performance
• Clusters indicate consistent impact patterns across ZIP codes
• Wide spread shows variable impact, while tight clusters show consistent behavior

Example Interpretation: This audience skews toward non-married individuals and people aged 30 to 40, who are more likely to convert, while higher-density housing areas tend to underperform.

• Households Marital Status: Never Married
  • Takeaway: Prioritize ZIPs with higher concentrations of never-married populations as this is a strong positive signal for conversion.
    • Many points extend to the right, indicating this feature often increases predicted conversion rate
    • Higher values (red) are more concentrated on the right, showing that higher concentrations of never-married populations drive performance
    • The distribution is relatively wide, meaning the impact varies across ZIP codes
    • Visible clustering suggests consistent positive patterns in certain regions
• Housing Type 10 to 19
  • Takeaway: Deprioritize areas with high concentrations of this housing type (mid- to high-density housing such as apartments or condos). Lower concentrations tend to perform more consistently.
    • Points are distributed on both sides of zero, indicating this feature can both increase and decrease predicted conversion rate
    • Red values (higher concentrations) extend more to the left, showing that higher values tend to decrease conversion likelihood
    • Blue values (lower concentrations) are more clustered on the right, indicating lower values tend to increase conversion likelihood
    • The spread is moderately wide, suggesting variable impact across ZIP codes
    • Clusters near the center indicate more neutral or mixed performance overall
• Age 30 to 40
  • Takeaway: This is a stable, reliable positive signal and a good baseline demographic to include in targeting.
    • Points lean slightly to the right, indicating a generally positive impact on conversion rate
    • Higher values (red) tend to appear more on the right, suggesting higher concentrations in this age group improve performance
    • The distribution is more tightly clustered, indicating a more consistent effect across ZIP codes
    • Less spread means this feature behaves more predictably compared to others

Demographic Sub-Feature Impact

This section breaks down a top demographic category into its individual sub-features to show how each one influences conversion predictions.

Household Income Example

This chart is a SHAP Beeswarm for the Household Income category.

What It Shows:

• Each dot represents a ZIP code prediction
• Each row represents a specific sub-feature
• X-axis shows SHAP value (impact on predicted conversion rate)
• Color represents the feature value:
  • Red = higher values
  • Blue = lower values
• Features are ordered by importance (top = most important)

Interpretation:

• Points to the right increase predicted conversion rate
• Points to the left decrease predicted conversion rate
• Color helps identify whether higher or lower values drive performance
• Clusters indicate consistent impact patterns across ZIP codes
• Wide spread shows variable impact, while tight clusters show consistent behavior

Example Interpretation: This audience skews toward low to mid-income households, which are more likely to convert than higher-income segments.

• Household Income $125,000 to $149,999
  • Takeaway: Higher concentrations of this income group tend to decrease conversion likelihood, while lower presence performs better.
    • Red points (higher values) are concentrated left of zero, indicating a negative impact
    • Blue points extend more to the right, showing lower concentrations improve performance
    • Wide spread indicates variable impact across ZIP codes
• Household Income $25,000 to $29,999
  • Takeaway: This income bracket is a strong positive signal when present at higher levels.
    • Red points cluster to the right, indicating higher values increase predicted conversion rate
    • Blue points appear more on the left, showing lower values reduce performance
    • Moderate spread suggests some variability, but generally consistent direction
• Household Income $50,000 to $59,999
  • Takeaway: A strong and reliable positive segment worth prioritizing.
    • Red points are clearly right-skewed, showing higher concentrations drive conversions
    • Blue points are more left or neutral, indicating weaker performance when absent
    • Wider spread shows impact varies, but direction is consistently positive

Geographic Performance — Click a State to See ZIP Codes

This section provides an interactive map of conversion performance by geography, helping identify where your audience is performing best.

What It Shows:

• States and ZIP codes colored by conversion rate
• Visual distribution of performance across regions and local markets
• Ability to analyze performance at both state and ZIP-level
• Toggle between Heatmap and Boundaries view:
  • Boundaries: Clearly outlines geographic regions
  • Heatmap: Highlights performance intensity across regions

Boundaries View

Heatmap View

Interactions:

• Hover over a state to view:
  • State Name
  • Conversion Rate
  • Impressions
  • Attributed Impressions
  • Unique Conversions
  • Unique Users
• Click a state to drill down into ZIP level performance – Zip code view:
  • Zip code
  • Zip code name
  • Conversions
  • Impressions
  • Attributed Impressions
  • Unique Conversions
  • Unique Users
  • Predicted Rate

Zip Code View

Table View

This table contains the underlying data used to power the Geographic Performance map, providing detailed metrics at the ZIP code level. Users can download this table as a CSV file for further analysis.

Each row represents a ZIP code and its associated performance, which is aggregated to render state-level views in the map.

What It Includes:

• Geographic identifiers
  • Geo Zip
  • Geo Region Name
  • City
  • State ID
• Delivery Metrics
  • Impressions: Total number of times ads were served
  • Attributed Impressions: Impressions tied to users who later converted within the lookback window
  • Unique Users: Number of distinct users exposed to ads
• Performance Metrics
  • Conversions: Total number of conversion events attributed to the campaign
  • Conversion Rate: Attributed Impressions / Impressions
  • Attributed Conversion Rate: Conversions / Impressions

Example insight:

• 07103 (NJ) shows a Conversion Rate (2.20%) and Attributed Conversion Rate of 0.30%, indicating performance well above average (0.077% conversion rate from Executive Summary for this data).

Cost per Conversion by Demographic Category

This section shows how cost efficiency changes based on how strongly a demographic category is represented in a ZIP, helping identify which types of areas deliver the best ROI.

What It Shows:

• Each demographic category (e.g., Income, Age, Marital Status) is broken into quartiles based on its most important sub-feature
• Each bar represents a quartile segment (Low to High)
• Bar height represents cost per conversion (CPA)

Interpretation:

• Lower bars = cheaper conversions (more efficient)
• Higher bars = more expensive conversions (less efficient)
• Compare across quartiles to see how performance changes as the concentration of the demographic increases or decreases within a ZIP
• Differences across categories show which demographic dimensions drive efficiency vs. cost

Example Interpretation: Balance scale and efficiency by prioritizing ZIPs with strong marital status and age signals, while reducing spend in areas where income signals increase CPA.

• Households Marital Status
  • Takeaway: ZIPs where this category has a stronger presence are more cost-efficient, while weaker presence is expensive.
    • The Low quartile has the highest CPA ~$4.05, indicating poor efficiency
    • CPA decreases steadily across quartiles, with High segments being much cheaper to ~$1.21
    • Indicates areas where marital status signals are stronger perform more efficiently
• Household Income
  • Takeaway: ZIPs where income-related signals are stronger (based on the model’s top feature) tend to be less cost-efficient.
    • CPA increases from Low to High quartiles (bins) (~$1.2 to ~$3.15)
    • Indicates stronger income signal areas are more expensive to convert
    • Lower-signal areas are more efficient
• Age
  • Takeaway: ZIPs with stronger age-related signals are more cost-efficient.
    • CPA decreases from Low to High quartiles
    • Indicates areas where age is more predictive deliver cheaper conversions

Table View

The table provides the underlying data behind the chart, showing how cost and performance vary across demographic category bins. Users can download this table as a CSV file for further analysis.

What It Shows:

• Each row represents a demographic category and its top contributing sub-feature
• Data is broken into quartile (bins): Low to High based on how strongly that feature is represented within each ZIP
• Columns included:
  • Category: High-level demographic group (e.g., Household Income, Age, Marital Status)
  • Top Feature: The most important sub-feature within that category driving the segmentation (e.g., a specific income bracket or age group)
  • Bin: Quartile grouping (Low, Medium-Low, Medium-High, High)
    • Represents relative concentration of that feature within a ZIP, not the value itself.
    • Bins are calculated independently for each category based on that category’s top feature.
  • ZIP Count: Number of ZIP codes in that bin
  • Impressions: Total impressions delivered across those ZIPs
  • Conversions: Total conversions generated in those ZIPs
  • Total Spend: Total media spend across those ZIPs

Lookalike

Lookalike targeting uses the trained demographic model to predict conversion rates for all ~33,000 US ZIP codes not in your campaign. ZIPs with demographics similar to your high-performing areas are identified as expansion targets. Confidence tiers reflect prediction reliability — high confidence ZIPs are within the model’s training distribution with consistent predictions across all decision trees.

Tile Metrics

• Lookalike ZIPs: Total ZIP codes in the U.S. not in your campaign that were scored for demographic similarity.
• High Confidence: ZIPs within the training distribution, with low prediction variance and a positive score. Most reliable for targeting expansion.
• Medium Confidence: ZIPs within the training distribution but with higher prediction uncertainty or neutral scores. Consider for testing.
• Low Confidence: ZIPs that are demographically different from your campaign/analysis ZIPs (out-of-distribution). Predictions are unreliable. Shown on the map in gray for context, but not recommended for targeting.
• States Covered: Number of U.S. states with at least one lookalike ZIP.
• Est. Additional Reach: Estimated additional unique IPs reachable in high-confidence lookalike ZIPs. Based on average IPs per campaign ZIP — treat as a rough estimate.
• Model Confidence: Model quality weight (e.g., 3.4%). All lookalike scores are dampened by this factor to reflect model uncertainty.

Lookalike Expansion Map — Click a State to See ZIP Codes

This section provides an interactive map of lookalike opportunities, showing where to expand based on demographic similarity to your best-performing ZIPs.

What It Shows:

• ZIP codes colored by confidence tier:
  • High (green): Strongest expansion opportunities
  • Medium (yellow): Test-and-learn opportunities
  • Low (gray): Out-of-distribution, not recommended
• Geographic distribution of lookalike audiences across the U.S.

Toggle Tiers

Show or hide High, Medium, ad Low confidence ZIPs for easy investigation and comparison.

Toggle Map View

Toggle views:

• Boundaries: Clearly outlines ZIP/state regions
• Heatmap: Highlights density and intensity of lookalike opportunities

Boundaries View

Heatmap View

Zip Code View

For each state, click onto it so see how the ZIP codes within that specific state map to the confidence tiers.

Lookalike ZIP Details

Full table of lookalike ZIPs sorted by score. Filter by confidence tier, search by city or ZIP. High confidence ZIPs are the safest expansion targets. Users can download this table as a CSV file for further analysis.

Columns:

• Geo ZIP: ZIP code being evaluated
• Predicted Rate: Model-predicted conversion rate for that ZIP
• Prediction Std: Standard deviation of predictions across decision trees
  • Lower = more consistent predictions
  • Higher = more uncertainty
• Confidence: Model confidence score (0–1) based on prediction stability and similarity to training data
• Score: Final model score used for ranking ZIPs
  • Combines predicted performance and confidence
  • Higher = better expansion opportunity
• Lift vs Avg: Predicted performance relative to campaign average
  • Positive = above average
  • Negative = below average
• Unique IPs: Estimated number of unique users in that ZIP (if available)
• Confidence Tier:
  • High: Reliable, within training distribution
  • Medium: Moderate confidence, test recommended
  • Low: Out-of-distribution, not recommended
• Observed Impressions: Number of impressions seen in this ZIP (if any historical data exists)
• Needs More Data:
  • TRUE: Limited data, predictions less stable
  • FALSE: Sufficient data for more reliable estimates
• City: Associated city name
• State ID: State abbreviation

Interpretation:

• Prioritize:
  • High confidence, high score, and positive lift are the best expansion targets
• Be cautious with:
  • High score but low confidence should be tested before scaling
• Use:
  • Prediction Std and Needs More Data to gauge reliability
  • Lift vs Avg to benchmark against current campaign performance

Personas

Tile Metrics

• Audiences: Number of distinct audience personas identified by K-means clustering. Each persona represents a group of ZIP codes with similar demographic profiles.
• Total ZIPs: Total number of converting ZIP codes that were clustered into audience personas.
• Avg. Audience Size: Average number of ZIPs per audience persona.
  • Large variance may indicate a mix of broad (dominant) and niche (specialized) personas
• Largest Audience: Identifies the largest persona segment and its share of total clustered ZIPs.
  • Includes the top defining trait for that persona
  • Helps quickly understand the most dominant audience type in your campaign

Additional Documentation Coming Soon!

Explorer

The Explorer tab allows you to browse and interact with all available datasets used in the model. Select a dataset to view its chart and table. Use the metric buttons above charts to switch between available metrics.

What It Does:

• Lists all datasets in the left-hand panel
• Displays each selection as a chart and table

This section is intentionally extensive and exploratory. Users are encouraged to navigate different datasets and metrics to uncover additional insights.

Charts

The Charts tab provides access to all visualizations generated by the model, organized into categories for easier navigation and analysis.

This section is intentionally extensive and exploratory. Users are encouraged to explore different chart visualizations to uncover additional insights and include in presentations.

Downloads

The Downloads section provides full access to all model outputs, enabling deeper analysis, reporting, and sharing across teams. These outputs can be used for investigation and validation, but more importantly, the model can be applied directly to campaigns or line items, allowing insights to seamlessly translate into real-time bidding and optimization.

Available Downloads (JSON & CSV):

• Executive Summary: High-level narrative of key findings and recommendations
• Scored Campaign ZIPs (Targeting): ZIP-level performance and model scores for areas included in the campaign
• Lookalike ZIPs (Expansion): Scored ZIPs outside the campaign identified as expansion opportunities
• Audience Persona Profiles: Clustered audience segments with demographic and geographic characteristics
• Category Comparison: Performance comparison across key demographic categories
• All Combined: Full dataset including all outputs for comprehensive analysis