Predicting Content Decay in High-Volume Search Environments

Author: Syed Muhammad Ibrahim Ali

Abstract: This paper investigates how historical search performance data can predict impending content decay before traffic loss occurs. We analyzed 79 million rows of production search data to identify early-warning signals of Click-Through Rate (CTR) degradation. By engineering features around publish age and impression velocity, we trained an isolation-split machine learning model to flag at-risk URLs. The model outperformed the baseline heuristic, successfully prioritizing high-value pages for editorial review. These findings demonstrate that automated triage can efficiently direct human editorial resources to mitigate traffic churn.

1. Introduction & Problem Statement

[Insert your explanation here: Why is content decay a problem? Why is it expensive to have editors blindly guess what to update?]

2. Data & Exclusions

[Insert data details: Mention you used the public release, state the date windows, and explain what rows/columns were excluded to ensure public safety and prevent leakage.]

3. Methodology

[Detail your assumptions, engineered features, how you defined the target label, your baseline metric, and how you designed your validation split to prevent temporal leakage.]

4. Results

[Explain how your model performed against the baseline on the exact same split. Reference your metrics.]

[ Insert Model vs Baseline Chart Here ]

5. Limitations & Honest Framing

[Be ruthlessly honest: What can't this model do? Mention that it identifies *that* a page is decaying, not *why* it's decaying.]

6. Ranked Recommendations (Action Playbook)

R1 (Urgent Refresh): High decay risk on high-volume content. Action: Full editorial review.
R2 (Metadata Tweak): Moderate decay risk. Action: A/B test titles/meta descriptions.
R3 (Monitor): Evergreen content. Action: No intervention required.

7. Reproducibility

The code, execution notebooks, and data schemas for this research can be reviewed in my GitHub Repository.

Acknowledgments & data credit

Built on the FlyRank ML Internship dataset. Visit https://flyrank.ai for more information on the data sourcing.