Comment Metrics Reporting Template

WEEKLY COMMENT PERFORMANCE REPORT
Period: [Start Date] — [End Date]
Prepared by: [Name]

VOLUME METRICS:
• Total comments received: [number]
  - Instagram: [number] ([X]%)
  - Facebook: [number] ([X]%)
  - TikTok: [number] ([X]%)
  - YouTube: [number] ([X]%)
  - Other: [number] ([X]%)
• Week-over-week change: [+/- X]%
• Average comments per post: [number]

SENTIMENT BREAKDOWN:
• Positive: [number] ([X]%)
• Neutral: [number] ([X]%)
• Negative: [number] ([X]%)
• Sentiment score: [X/10] (previous week: [X/10])

RESPONSE METRICS:
• Comments responded to: [number] ([X]% response rate)
• Average response time: [X hours X minutes]
• Fastest response: [X minutes]
• SLA compliance: [X]%
  - Within 1 hour: [X]%
  - Within 4 hours: [X]%
  - Over 4 hours: [X]%

MODERATION ACTIONS:
• Comments auto-moderated by AI: [number]
• Comments manually reviewed: [number]
• Comments deleted (spam): [number]
• Comments hidden: [number]
• Comments escalated: [number]

TOP ISSUES THIS WEEK:
1. [Description with count]
2. [Description with count]
3. [Description with count]

ACTION ITEMS FOR NEXT WEEK:
☐ [Action item]
☐ [Action item]
☐ [Action item]

MONTHLY COMMENT MODERATION — EXECUTIVE SUMMARY
Month: [Month Year]

KEY HIGHLIGHTS:
• [Total comments] comments managed across [X] platforms (+/- [X]% vs last month)
• Average response time: [X hours] (target: [X hours])
• [X]% SLA compliance (target: [X]%)
• Estimated revenue protected: $[amount]
• Cost per moderated comment: $[amount]

COST EFFICIENCY:
• FeedGuardians subscription: $[amount]/month
• AI auto-moderation rate: [X]%
• Manual review hours: [X hours]
• Equivalent manual labor cost: $[amount]
• Net savings: $[amount]/month

SENTIMENT TREND (Last 6 Months):
[Month 1]: [X/10]
[Month 2]: [X/10]
[Month 3]: [X/10]
[Month 4]: [X/10]
[Month 5]: [X/10]
[Month 6]: [X/10]

NOTABLE INCIDENTS:
• [Incident description and resolution]
• [Incident description and resolution]

RECOMMENDATIONS:
1. [Strategic recommendation with expected impact]
2. [Strategic recommendation with expected impact]
3. [Strategic recommendation with expected impact]

NEXT MONTH PRIORITIES:
1. [Priority]
2. [Priority]
3. [Priority]

COMMENT-TO-REVENUE ATTRIBUTION
Period: [Date Range]

OVERVIEW:
This report tracks how comment interactions contribute to measurable revenue.

COMMENT-DRIVEN CONVERSIONS:
• Product questions answered: [number]
• Of those, tracked to purchase: [number] ([X]% conversion rate)
• Average order value from comment-driven sales: $[amount]
• Total estimated revenue from comment engagement: $[amount]

AD COMMENT IMPACT:
• Total ad comments managed: [number]
• Ads with positive comment sentiment: [X]% lower CPA
• Negative comments hidden/resolved: [number]
• Estimated ad spend saved from moderation: $[amount]

DISCOUNT CODE TRACKING (Comment-to-DM flows):
• Discount codes delivered via DM: [number]
• Codes redeemed: [number] ([X]% redemption rate)
• Revenue from redeemed codes: $[amount]

CUSTOMER RETENTION:
• Complaints resolved via comments: [number]
• Customer retention rate for resolved complaints: [X]%
• Estimated lifetime value retained: $[amount]

ROI CALCULATION:
• Total moderation cost: $[amount]/month
• Total attributable revenue: $[amount]/month
• ROI: [X]x return on moderation investment

METHODOLOGY NOTES:
• Revenue attribution uses [UTM tracking / last-touch attribution / survey data]
• Ad spend savings calculated by comparing CPA on moderated vs unmoderated ad sets
• LTV retention estimates based on industry averages and internal data

SPAM & THREAT INTELLIGENCE REPORT
Period: [Date Range]

SPAM METRICS:
• Total spam comments detected: [number]
• Auto-blocked by AI: [number] ([X]%)
• Caught in manual review: [number] ([X]%)
• Spam that reached public view: [number] ([X]%)
• New spam patterns identified: [number]

NEW SPAM PATTERNS THIS PERIOD:
1. Pattern: [Description — e.g., "Comments containing WhatsApp numbers with crypto investment offers"]
   Frequency: [number] occurrences
   Action taken: Added to auto-filter

2. Pattern: [Description]
   Frequency: [number] occurrences
   Action taken: [Description]

BOT ACCOUNT ACTIVITY:
• Suspected bot comments: [number]
• Bot accounts reported to platforms: [number]
• Bot patterns: [Description of common bot behaviors observed]

THREAT INCIDENTS:
• Threats of violence: [number]
  - Reported to platform: [Yes/No]
  - Reported to law enforcement: [Yes/No if applicable]
• Doxxing attempts: [number]
• Phishing links detected: [number]

MODERATION FILTER PERFORMANCE:
• Total keywords in filter: [number]
• Filter accuracy rate: [X]%
• False positives (legitimate comments caught): [number] ([X]%)
• False negatives (spam not caught): [number] ([X]%)

RECOMMENDATIONS:
• Add to auto-filter: [list new terms/patterns]
• Remove from auto-filter (high false positive): [list terms]
• Platform reports to file: [list]

MODERATION TEAM PERFORMANCE — [Period]

TEAM OVERVIEW:
• Active moderators: [number]
• Total shifts covered: [number] hours
• Coverage gaps: [number] hours ([X]% coverage)

INDIVIDUAL PERFORMANCE:

[Team Member 1]:
• Comments handled: [number]
• Average response time: [X min]
• Accuracy rate: [X]%
• Escalations: [number]

[Team Member 2]:
• Comments handled: [number]
• Average response time: [X min]
• Accuracy rate: [X]%
• Escalations: [number]

[AI / FeedGuardians]:
• Comments auto-handled: [number]
• Accuracy rate: [X]%
• False positive rate: [X]%
• Saved manual review hours: [X hours]

QUALITY METRICS:
• Audit sample size: [number] comments reviewed
• Policy compliance rate: [X]%
• Common errors:
  1. [Error type] — [count]
  2. [Error type] — [count]
• Training needs identified: [description]

CAPACITY PLANNING:
• Current volume: [number] comments/week
• Current capacity: [number] comments/week
• Utilization: [X]%
• Projected volume next month: [number]
• Additional capacity needed: [Yes/No — details]