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CONTENT & MEDIAData Science

±5%

AHT FORECAST CONFIDENCE INTERVAL

AHT Prediction for Content Moderation Policy Changes

Global Social Media Platform — Operations & BPO · 2024

[THE CHALLENGE]

Every Policy Change Was a Blindside.

A global social media platform regularly updated its content moderation policies — adding new violation categories, adjusting enforcement thresholds, changing review workflows. Each policy change materially altered the complexity and volume of content reviews handled by operations teams. But no one could predict by how much.

When a policy change increased Average Handle Time (AHT) by 15–25%, workforce plans built on last week's baselines became immediately incorrect. The result: understaffed queues, SLA breaches, and emergency headcount escalations that took 1–2 weeks to resolve — by which time the backlog had already compounded.

The operations team needed a way to forecast AHT impact before a policy change went live — not react to it after the fact. With 2 weeks' notice, headcount could be right-sized. Without it, every policy update was a workforce crisis.

Forecast the AHT impact of any content moderation policy change within ±5% — at least 2 weeks before the policy goes live.

[OUR APPROACH]

Four Phases.
One Forecast.

PHASE 01

Policy Change Feature Analysis

Catalogued 24 months of historical policy changes — categorised by type (new violation category, threshold adjustment, workflow change), scope (content vertical, region, severity level), and measured AHT impact post-implementation. Built a structured policy change taxonomy as the foundation for feature engineering.

Policy TaxonomyHistorical AnalysisFeature Design

PHASE 02

Policy Complexity Scoring

Developed a policy complexity score — a composite index encoding: number of new decision nodes added to the review workflow, expected content volume affected, violation category novelty (new vs. refined), and language/region scope multipliers. Complexity score became the primary engineered feature in the predictive model.

Feature EngineeringComplexity IndexPython

PHASE 03

XGBoost Regression Model

XGBoost regression trained on historical policy × AHT outcome pairs. Policy complexity score, historical AHT baseline, content vertical, region, and agent experience cohort all included as features. Monte Carlo simulation added to generate confidence intervals around point estimates — producing ±5% forecast bands at 90% coverage validated on held-out test set.

XGBoostMonte Carlo SimulationConfidence IntervalsSHAP

PHASE 04

Automated Retraining Pipeline

Weekly retraining pipeline on AWS Lambda + Airflow ingesting the latest post-policy AHT actuals to continuously update model weights. Policy change inputs submitted via a lightweight Streamlit interface by operations planning teams — outputs delivered within seconds. Accuracy tracked in Power BI ops dashboard with MAPE and coverage rate by policy type.

AWS LambdaApache AirflowStreamlitPower BI

PHASE 05

Scenario Simulation Web Application

Built an interactive planning application exposing the model's feature set directly to operations and workforce planning teams. Users configure the same policy attributes used during model training — complexity score, content vertical, scope, and region — to simulate multiple rollout scenarios before committing to a plan. Each scenario returns an AHT forecast with confidence intervals, enabling planners to identify the policy configuration that minimises workforce impact. What was previously a black-box prediction became a hands-on planning tool — shifting the team from reactive headcount adjustment to proactive scenario-driven decision-making.

StreamlitScenario SimulationWhat-If PlanningMonte Carlo

[KEY OUTCOMES]

The Results.

±5%

Forecast Confidence Interval

90% coverage achieved on held-out test set — exceeding the ±10% target set at project inception.

2wk

Planning Lead Time

Operations teams now plan headcount 2 weeks ahead of every policy change with model-backed confidence.

Emergency Escalations

Post-deployment: zero unplanned headcount escalations triggered by policy-change AHT variance.

Months Historical Training

Model continuously retrained on weekly actuals — improving coverage rate by 3% per quarter post-launch.

Policy changes went from workforce crises to planned events. Operations leadership gained a reliable, model-backed AHT forecast for every moderation update — submitted and returned in under 60 seconds.

[TECHNOLOGY USED]

Stack.

ML & MODELLING

XGBoostMonte Carlo SimulationSHAPScikit-learnPython (NumPy, Pandas)

PLATFORM & ORCHESTRATION

AWS LambdaApache AirflowAmazon S3

INTERFACE & MONITORING

Streamlit (policy input UI)Power BI (accuracy monitoring)

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