content-performance-explainer

name: Content Performance Explainer description: Diagnose and explain why e-commerce content is or isn't performing against KPIs, using causal analysis frameworks, funnel decomposition, and competitive benchmarking to generate actionable improvement recommendations.

metadata: display_name: "Content Performance Explainer" short_description: "Explain e-commerce content performance drivers and KPIs" default_prompt: "Explain my content performance in simple words and next steps" version: "1.0.0" tags: - cpg-retail

icon_path: "assets/icon.png"

Content Performance Explainer

Overview

This skill transforms raw content performance data into clear, actionable explanations of why content is succeeding or failing. It moves beyond descriptive analytics ("CVR dropped 12%") to diagnostic and prescriptive analysis ("CVR dropped because the new title lost the primary keyword, reducing search-driven traffic quality by 18% — here's the fix").

Most teams drown in dashboards but starve for insight. This skill bridges the gap between data and decision-making for content teams, brand managers, and e-commerce leaders.

When to Use

• Explaining a sudden change (positive or negative) in content performance metrics.
• Conducting periodic content performance reviews (weekly, monthly, quarterly).
• Justifying content investment or optimization spend with clear ROI narratives.
• Diagnosing why a content update didn't produce expected results.
• Comparing performance across SKUs, categories, or time periods.
• Preparing executive-facing content performance reports.
• Post-mortem analysis after A/B test results.

Required Inputs

Methodology

Step 1 — Funnel Decomposition

Break content performance into a sequential funnel, isolating where changes occur:

Search Impressions → Clicks (CTR)
    → Detail Page Views → Add-to-Cart (ATC Rate)
        → Purchase (CVR) → Revenue
            → Repeat Purchase → LTV

Stage-by-Stage Diagnostic:

Step 2 — Change Attribution Analysis

When performance shifts, identify the most likely cause using the Attribution Hierarchy:

1. Content Changes (highest attribution certainty): Did any content element change during the period? Compare versions side-by-side.
2. Competitive Changes: Did key competitors change pricing, launch new products, or update content?
3. Algorithmic Changes: Did search rankings shift without content changes? Possible algorithm update.
4. Market/Seasonal: Is there a known seasonal pattern, category trend, or macroeconomic factor?
5. Advertising Changes: Did ad spend, bid strategy, or campaign structure change?
6. External Events: PR events, social media virality, influencer mentions, recalls, news coverage.

Apply the Counterfactual Test: "If this factor had NOT changed, would performance have remained stable?" The factor with the strongest counterfactual is the primary driver.

Step 3 — Content Element Impact Scoring

Score each content element's contribution to overall performance:

Step 4 — Performance Narrative Construction

Build a clear, stakeholder-ready explanation using the Situation → Analysis → Recommendation (SAR) framework:

Situation: State the performance change in business terms.

• "SKU X revenue declined 22% month-over-month ($45K → $35K), driven primarily by a 15% CVR drop."

Analysis: Explain the root cause with supporting evidence.

• "The CVR decline coincides with a title change on March 5 that removed the primary keyword 'organic protein powder.' Search impression share dropped 30%, and remaining traffic was less purchase-intent aligned. Competitor Y also launched a new SKU at $2 lower price point, capturing 8% of our branded search impressions."

Recommendation: Provide specific, prioritized actions.

• "Priority 1: Restore primary keyword to title (expected +20% impression recovery in 7-14 days). Priority 2: Add competitive comparison in A+ content to defend against competitor Y's price positioning."

Step 5 — Benchmark Contextualization

Frame performance within appropriate context:

1. Category Benchmarks: Compare against category averages — an 8% CVR might be excellent in electronics but poor in grocery.
2. Historical Trend: Is this a new decline or continuation of a long-term trend?
3. Seasonality Adjustment: Remove seasonal effects to see underlying performance.
4. Portfolio Context: How does this SKU perform relative to the brand's other SKUs?
5. Market Growth/Decline: Is the entire category growing or contracting?

Step 6 — Predictive Outlook & Action Prioritization

Project future performance under different scenarios:

Prioritize recommendations using the Impact × Speed Matrix:

Output Specification

output:
  executive_summary: string           # 2-3 sentence performance narrative
  performance_change:
    metric: string                     # Primary KPI analyzed
    current_value: float
    previous_value: float
    change_pct: float
    direction: string                  # "improved" | "declined" | "stable"
  funnel_analysis:
    impressions: { value: float, change: float, health: string }
    ctr: { value: float, change: float, health: string }
    cvr: { value: float, change: float, health: string }
    revenue: { value: float, change: float, health: string }
  root_cause_analysis:
    primary_driver: string
    contributing_factors: list[string]
    confidence: float                  # 0-100 confidence in attribution
    evidence: list[string]
  content_element_scores: dict         # Element → impact assessment
  benchmark_comparison:
    vs_category: string                # "above" | "at" | "below"
    vs_historical: string
    percentile: float                  # Category performance percentile
  recommendations:
    - priority: int
      action: string
      expected_impact: string
      timeline: string
      effort: string                   # "low" | "medium" | "high"
  projected_scenarios: dict

Analysis Framework

Content Performance Health Score: Aggregate metric combining multiple dimensions:

Health Score: 0-100. Traffic light system: Green (≥ 75), Yellow (50-74), Red (< 50).

Examples

Scenario: Organic granola bar on Amazon. Revenue dropped 35% in 4 weeks.

Executive Summary: "Revenue declined 35% ($28K → $18K) over 4 weeks, primarily driven by a 40% drop in search impressions after losing page-1 organic rank for 'organic granola bars.' Root cause: a title edit on Feb 1 replaced the primary keyword with a brand sub-line. Secondary factor: a key competitor launched a Subscribe & Save offer, improving their conversion and organic rank. Restoring the keyword to the title is the highest-priority fix, with expected recovery within 10-14 days."

Funnel Breakdown:

• Impressions: -40% (search rank dropped from #3 to #18)
• CTR: +5% (fewer but more brand-aware impressions)
• CVR: -8% (competitor's S&S offer pulled comparison shoppers)
• Net Revenue Impact: -35%

Recommendation Priority Stack:

1. Restore "organic granola bars" to title position 1-3. (Impact: High, Speed: Fast, Effort: Low)
2. Enable Subscribe & Save at 5% discount. (Impact: High, Speed: Medium, Effort: Medium)
3. Update A+ comparison chart to address competitor's value proposition. (Impact: Medium, Speed: Medium, Effort: Medium)

Guidelines

• Always separate correlation from causation — a content change and a performance shift occurring simultaneously doesn't prove causation without controlling for other variables.
• Present findings with appropriate confidence levels — don't overstate certainty when multiple factors coincide.
• Tailor the depth and language of the explanation to the audience (executive = high-level SAR; content team = detailed funnel with element-level recommendations).
• Include "what's working" alongside "what's broken" — reinforce winning content patterns to prevent accidental regression.
• Acknowledge data limitations — Amazon's attribution window, delayed reporting, and aggregated metrics all introduce uncertainty.
• Compare against the right benchmark — a luxury skincare brand should not benchmark against mass-market grocery.

Validation Checklist

• Funnel is decomposed stage-by-stage with metrics for each stage.
• Content changes during the analysis period are identified and version-compared.
• Non-content factors (competitive, seasonal, advertising) are assessed and accounted for.
• Root cause attribution uses the counterfactual test and is stated with confidence level.
• Performance is contextualized against category benchmarks, historical trends, and seasonality.
• Recommendations are specific, prioritized by impact × speed, and include expected effect sizes.
• Projected scenarios (do nothing / quick fix / full optimization) are provided.
• Executive summary follows the SAR framework and is stakeholder-ready.
• Content element impact scores are calculated for each PDP component.
• Analysis distinguishes between correlation and causation with appropriate caveats.