Creative Fatigue Detection in Digital Advertising

CreativeDynamics Library v0.9.8.1

Definition and Mathematical Framework

Creative fatigue: advertising effectiveness degradation over time due to repeated audience exposure. The library employs path signature analysis from rough path theory to detect creative performance degradations in digital advertising campaigns.

Mathematical Formulation: For performance metric time series Y = {y_1, …, y_T}, the library detects fatigue by:

  1. Computing path signatures S_t for sliding windows of size w

  2. Calculating signature distances d_t = ||S_t - S_{t-1}||_2

  3. Identifying change points where d_t > μ_d + k·σ_d

  4. Classifying post-change-point trends to identify declining performance

Captures non-linear pattern changes preceding visible performance drops, enabling proactive campaign optimisation.

Dual-Metric Analysis Framework

Dual-metric approach analyses both Click-Through Rate (CTR) and Cost Per Click (CPC) for detailed fatigue detection.

Metric Complementarity

CTR (Click-Through Rate):

  • Measures audience engagement (clicks/impressions)

  • Reflects creative relevance and appeal

  • Early indicator of audience saturation

CPC (Cost Per Click):

  • Measures cost efficiency (spend/clicks)

  • Reflects competitive dynamics and quality score

  • Indicates platform algorithm adjustments

Rationale for Dual Analysis

  • Different Response Patterns: CTR and CPC exhibit distinct temporal dynamics under fatigue

  • Detailed Detection: Pattern changes may appear in one metric before the other

  • Correlation Context: Correlation analysis highlights when CTR and CPC move inversely; metrics are not combined

  • Strategic Insights: Different metrics align with different campaign objectives (engagement vs. efficiency)

Metric Interpretation

CTR: Measures audience engagement over time. Declining CTR suggests diminishing interest or relevance.

CPC: Indicates cost efficiency per engagement. Rising CPC signals decreasing efficiency from declining relevance or increasing competition.

Detection Algorithm

Four-phase detection process for each metric:

Phase 1: Change Point Detection

  • Sliding window size w (default=7) captures weekly patterns

  • Signature depth d=4 balances detail vs. computational cost

  • Threshold multiplier k=1.5 provides precision≈0.7, recall≈0.6

Phase 2: Trend Classification

  • Stable: |slope| < threshold

  • Improving: slope > threshold (CTR↑ or CPC↓)

  • Declining: slope < -threshold (CTR↓ or CPC↑)

Phase 3: Benchmark Establishment

  • Identifies longest stable/improving segment

  • Computes average performance as benchmark

  • Validates benchmark reliability (minimum 3 data points)

Phase 4: Impact Quantification

  • Measures deviation from benchmark during declining periods

  • Quantifies operational impact (engagement_gap_clicks) and financial inefficiency (actual_overspend_gbp)

  • Provides correlation risk context; metrics are reported separately and not combined

Performance Characteristics

Empirical validation across multiple advertising datasets:

  • Early Detection: Identifies fatigue 3-5 days before traditional methods

  • False Positive Rate: ~30% (controlled by threshold parameter k)

  • Computational Efficiency: O(T·d²) complexity enables real-time analysis

  • Robustness: Handles missing data and outliers through normalisation

Impact Metrics

Translates detected fatigue into separate operational and financial impact metrics:

Financial (actual_overspend_gbp):

actual_overspend_gbp = Σ_t∈decline max(0, CPC_t - CPC_benchmark) × Clicks_t

Actual overspend due to increased cost-per-click during fatigue periods.

Operational (engagement_gap_clicks):

engagement_gap_clicks = Σ_t∈decline max(0, CTR_benchmark - CTR_t) × Impressions_t

Lost clicks due to decreased engagement rates. A GBP reference value may be shown separately as:

engagement_gap_gbp_reference = engagement_gap_clicks × CPC_benchmark

Correlation Risk Context (metrics not combined)

Correlation analysis provides context when interpreting CTR and CPC together:

Correlation Coefficient:

ρ(CPC, CTR) = Cov(CPC, CTR) / (σ_CPC × σ_CTR)

Risk Classification:

  • Low Risk (ρ > -0.2): Independent or weak correlation

  • Medium Risk (-0.5 < ρ ≤ -0.2): Moderate negative correlation

  • High Risk (ρ ≤ -0.5): Strong negative correlation

Operational and financial metrics are reported separately and are not added regardless of risk level.

Detailed methodology and visualisation interpretation: Example Application: Advanced Analysis - Quantifying Impact Using Library Outputs.