Initial commit
This commit is contained in:
Zhongwei Li
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# Example: Product Launch Announcement
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## Scenario
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**Context:** Launching "Analytics Insights" - automated report generation tool that saves customer success teams 10-15 hours per week. Announcing to existing customers via email + blog post.
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**Audience:** Customer success managers and directors at mid-size B2B SaaS companies
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- Expertise: Moderate technical literacy, very familiar with pain points
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- Concerns: Time savings, ease of adoption, cost, disruption to current workflow
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- Time available: 2-3 minutes to read email, 5-10 minutes for blog
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**Purpose:** Drive adoption of new feature (target: 40% adoption in 30 days)
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**Tone:** Empathetic (acknowledge pain), excited (benefits), supportive (easy transition)
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---
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## Story Structure Used
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**Before-After-Bridge (BAB):**
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1. Before: Painful current state (audience's lived experience)
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2. After: Improved future state (what becomes possible)
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3. Bridge: The solution (how to get there)
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4. Call to Action: Next step to access the "after" state
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---
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## Draft: Communication
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### Before (Weak - Feature Announcement)
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> Subject: New Feature: Analytics Insights
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>
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> Hi there,
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>
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> We're excited to announce Analytics Insights, a new feature that automatically generates customer health reports. This feature uses machine learning to analyze usage patterns and create personalized insights for each customer.
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>
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> Key features:
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> - Automated report generation
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> - Customizable templates
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> - Email scheduling
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> - Data visualization
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> - 10+ pre-built metrics
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>
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> Analytics Insights is available now in the dashboard under "Reports." Check out our documentation for setup instructions.
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>
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> Let us know if you have any questions!
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**Problems:**
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- ❌ Leads with feature ("Analytics Insights") not benefit
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- ❌ Doesn't acknowledge current pain (assumes audience knows why they need this)
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- ❌ Feature list without context (why do I care about "customizable templates"?)
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- ❌ No proof (claims time savings but doesn't quantify or show evidence)
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- ❌ Weak CTA ("check it out") without urgency or clear next step
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- ❌ No storytelling - just a list of features
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### After (Strong - Storytelling)
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> Subject: Spend 15 hours/week on customers, not spreadsheets
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>
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> # Before: Your Weekends Belong to Excel. After: They Belong to You.
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>
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> If you're a customer success manager, I don't have to tell you how your Fridays go.
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>
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> **Your current Friday afternoon:**
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> - 2:00pm: Start pulling usage data for your top 20 accounts
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> - 2:45pm: Export to CSV, clean up formatting issues (why does every export break?)
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> - 3:30pm: Build pivot tables and charts in Google Sheets
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> - 4:15pm: Copy-paste into your customer email template
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> - 4:45pm: Personalize each email (change "Hey Sarah" to "Hey Michael", update account-specific context)
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> - 6:30pm: Send last email, realize you spent 4.5 hours on reporting that should have taken 30 minutes
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> - Saturday morning: Customer replies asking for clarification on a metric - back to your laptop
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>
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> You became a CS manager to build relationships and drive retention. Instead, you're a data janitor.
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>
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> We built Analytics Insights to fix this.
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>
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> ## After: What Your Friday Looks Like Now
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>
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> **Your new Friday afternoon:**
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> - 2:00pm: Open Analytics Insights, click "Generate Reports"
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> - 2:03pm: Review automatically generated insights for your 20 accounts (already personalized, already visualized, already in email format)
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> - 2:15pm: Click "Send" or make minor tweaks if needed
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> - 2:20pm: Done. You just freed up 4 hours.
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>
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> **What you do with those 4 hours:**
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> - Proactively reach out to at-risk accounts (the system flagged 3 with declining usage)
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> - Prepare for Monday's executive retention review (with confidence, not scrambling)
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> - Actually leave at 5pm on Friday
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>
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> This isn't about automation for automation's sake. It's about spending time on what matters: your customers.
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>
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> ## How It Works (The Bridge)
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>
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> **Setup (5 minutes):**
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> 1. Go to Reports → Analytics Insights
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> 2. Choose your template (we have 5 pre-built, including "Executive Summary" and "Feature Adoption")
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> 3. Select accounts and schedule (weekly Fridays at 2pm, or on-demand)
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> 4. Done.
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>
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> **What Happens Next:**
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> Every Friday at 2pm, Analytics Insights:
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> - Pulls usage data for each account (sessions, feature adoption, health score trends)
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> - Generates insights using the same patterns you'd manually identify ("Sarah's team adoption is up 40% this month, driven by the new workflow builder")
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> - Creates beautiful visualizations (no more manual charting)
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> - Drafts personalized emails (including customer name, account-specific context, relevant metrics)
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> - Sends to you for review (or auto-sends if you enable it)
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>
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> **Customization:**
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> - Add your own metrics (we start with 12 standard ones)
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> - Edit templates (match your voice and brand)
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> - Override insights (AI gets it 90% right, you perfect the last 10%)
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>
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> ## Why We Built This (Proof You Can Trust It)
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>
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> We didn't build this in a vacuum. Over the past 6 months:
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> - We interviewed 40 CS managers like you
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> - 38 of them mentioned "manual reporting" as their #1 time sink
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> - Average time reported: 12 hours per week
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>
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> We piloted Analytics Insights with 12 beta customers for 8 weeks. Results:
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> - **Time saved:** Average 10.6 hours per week (range: 8-15 hours depending on account size)
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> - **Accuracy:** 94% of AI-generated insights matched what CS managers would have written manually
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> - **Adoption:** 11 of 12 beta users now use it weekly (one still tweaking template preferences)
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>
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> **What beta users said:**
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>
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> > "This is the feature I didn't know I needed until I had it. Now I can't imagine going back. I left at 4pm last Friday for the first time in a year." — Sarah Chen, CS Director, TechCorp (120 accounts)
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>
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> > "I was skeptical about AI writing my customer emails. But it nails the tone—I only change 1-2 sentences per email now. Total game changer." — Michael Rodriguez, CS Manager, GrowthCo (45 accounts)
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>
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> > "The ROI calculation is simple: I save 12 hours per week. That's 48 hours per month. If my time is worth $100/hr (conservative), this saves my company $4,800/month. It's already included in my plan. This is a no-brainer." — Jamie Lee, VP Customer Success, DataCo (8-person CS team)
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>
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> ## This Is Included (No Extra Cost)
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>
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> Analytics Insights is included in your current plan. No upgrade, no add-on, no surprise invoice.
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>
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> Why? Because your success is our success. If you spend less time on manual work and more time with customers, retention improves. When retention improves, we both win.
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>
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> ## Get Started Today (Your Friday Is Waiting)
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>
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> **Here's what to do next:**
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>
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> 1. **[Try it now - 5 minute setup](link)** → Set up your first automated report
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> 2. **[Watch 3-minute demo video](link)** → See it in action before you commit
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> 3. **[Join office hours this Thursday 2pm PT](link)** → Ask questions, see advanced tips
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>
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> **What to expect:**
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> - **This week:** Set up and generate your first report (test with 5 accounts before full rollout)
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> - **Next week:** Roll out to all accounts, customize templates to match your voice
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> - **Week 3:** Sit back and enjoy your Fridays (seriously, that's the goal)
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>
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> **Need help?**
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> Reply to this email and I'll personally help you set up. Or book time with our CS team [here](link).
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>
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> ## A Note from Our CEO
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>
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> "We started this company because we believe customer success teams are undervalued and overworked. You're expected to be data analysts, relationship managers, product experts, and therapists—all at once. This feature is our attempt to give you back time to focus on what you do best: building relationships that drive retention. I hope it makes your life a little easier."
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>
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> —CEO Name
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>
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> **P.S.** Set a reminder to try this before next Friday. Future you will thank present you when you leave work at 5pm instead of 7pm. [Set up Analytics Insights now →](link)
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**Why This Works:**
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✅ **Headline:** "Spend 15 hours/week on customers, not spreadsheets" - benefit-focused, quantified, relatable
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✅ **Empathy:** Opens with painfully specific current state that audience lives ("Friday 2pm, start pulling usage data...")
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✅ **Show don't tell:** Detailed timeline of current Friday (4.5 hours of manual work) vs new Friday (20 minutes)
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✅ **Specificity:** 10.6 hours saved (not "saves time"), 94% accuracy (not "highly accurate"), 11/12 adoption (not "popular")
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✅ **Social proof:** 3 customer testimonials with names, companies, account sizes (credibility through specificity)
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✅ **Proof:** Beta results (12 customers, 8 weeks, quantified outcomes) not just claims
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✅ **Stakes:** Humanized ($4,800/month = 48 hours × $100/hr) and emotional (leave at 5pm on Friday)
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✅ **No-risk:** Included in current plan (removes cost objection)
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✅ **Actionability:** 3 clear next steps (try now, watch demo, join office hours) with timelines
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✅ **Multiple CTAs:** Try now (for action-oriented), watch demo (for cautious), office hours (for question-askers)
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✅ **Tone:** Empathetic ("I don't have to tell you..."), supportive ("I'll personally help"), excited but not over-the-top
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✅ **Structure:** BAB (Before → After → Bridge) creates clear transformation narrative
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---
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## Self-Assessment Using Rubric
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**Headline Clarity (5/5):** "Spend 15 hours/week on customers, not spreadsheets" - crystal clear benefit
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**Structure (5/5):** BAB (Before painful, After aspirational, Bridge actionable) - perfect fit for product launch
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**Evidence Quality (5/5):** 12 beta customers, 8 weeks, 10.6 hours saved, 94% accuracy, 11/12 adoption, 3 named testimonials
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**Audience Fit (5/5):** Deep empathy with CS manager pain points, appropriate detail level, addresses concerns (cost, accuracy, adoption)
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**Storytelling (5/5):** Hyper-specific current state (Friday timeline), vivid future state (leave at 5pm), concrete bridge (5-minute setup)
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**Accountability (4/5):** Acknowledges AI isn't perfect (90% right, you perfect last 10%), CEO note shows commitment
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**Actionability (5/5):** 3 tiered CTAs (try/watch/ask), weekly timeline, support offers (personal help, office hours)
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**Tone (5/5):** Empathetic + excited + supportive - matches product launch for existing customers
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**Transparency (5/5):** Shows beta results (not just cherry-picked wins), admits AI needs 10% human refinement
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**Credibility (5/5):** Customer testimonials with full names/companies, quantified beta results, CEO commitment
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**Average: 4.9/5** ✓ Production-ready (very strong)
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---
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## Key Techniques Demonstrated
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1. **Empathy Opening:** Start with painful specificity audience recognizes ("Your Friday afternoon: 2:00pm...")
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2. **Transformation Narrative:** Contrast current painful state (6:30pm still working) with aspirational future (2:20pm done)
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3. **Humanization:** Time saved → emotional benefit (leave at 5pm Friday for first time in a year)
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4. **Social Proof:** 3 testimonials from different seniority levels (director, manager, VP) with specific results
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5. **Risk Removal:** Included in current plan (no cost), 5-minute setup (low effort), personal help offered (low barrier)
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6. **Multiple CTAs:** Try/Watch/Ask - accommodates different audience personas (action-takers, cautious evaluators, question-askers)
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7. **Proof Stack:** Interviews (40 CS managers) + beta (12 customers, 8 weeks) + testimonials (3 named) = comprehensive evidence
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8. **Specificity:** Not "saves time" but "10.6 hours/week", not "accurate" but "94%", not "popular" but "11/12 beta users"
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9. **CEO Voice:** Adds weight and shows company commitment (not just product team shipping feature)
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10. **PS Technique:** Reinforces CTA with emotional hook (future you will thank present you)
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---
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## Alternative Version: Internal Announcement (to CS Team)
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If announcing internally to your own CS team (not customers), adjust:
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**Headline:** "Reclaim Your Fridays: New Auto-Reporting Tool Launching"
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**Changes:**
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- More emphasis on how to get support (training sessions, dedicated Slack channel)
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- Call out change management (optional first month, required after pilot)
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- Acknowledge concerns ("I know change is hard when you have a system that works")
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- Add metrics we're tracking (adoption rate, time saved, quality scores)
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- Make CEO note about supporting the team through transition
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**Tone shift:** Still empathetic, but more collaborative (we're in this together) vs selling (you should use this)
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---
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## Alternative Version: Blog Post (Public)
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If publishing as public blog (not just customers), adjust:
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**Headline:** "Why We Built Analytics Insights: Giving CS Teams Their Time Back"
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**Changes:**
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- Add "Why this matters for the industry" section (CS burnout crisis, data janitor problem universal)
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- Include more behind-the-scenes (how we built it, technical challenges overcome)
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- Broaden appeal (useful for any CS tool provider, not just our customers)
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- End with industry call-to-action (other CS tools should solve this too)
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**Tone shift:** Thought leadership (here's what we learned) vs product marketing (here's what you get)
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@@ -0,0 +1,184 @@
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# Example: Technical Incident Postmortem
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## Scenario
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**Context:** Major database outage affected 80% of customers for 4 hours on a Saturday. Need to communicate to executive team on Monday.
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**Audience:** CEO, CTO, VP Product, VP Customer Success
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- Expertise: Mixed (CEO non-technical, others technical)
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- Concerns: Customer impact, reputation, revenue risk, engineering capability
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- Time available: 15 minutes in emergency exec meeting
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**Purpose:** Build trust through accountability, explain root cause, commit to prevention
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**Tone:** Serious but measured, accountable not defensive, solution-oriented
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---
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## Story Structure Used
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**Crisis Communication Arc (Trust Building):**
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1. Acknowledge problem honestly
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2. Show accountability and action taken
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3. Explain root cause and prevention
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4. Commit to transparency and improvement
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---
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## Draft: Communication
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### Before (Weak - Data Dump)
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> Subject: Database Incident - Saturday Nov 2
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>
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> On Saturday November 2nd from 2:14am to 6:47am PT, our primary database experienced corrupted indexes causing query failures. 80% of customer requests returned errors. The incident was triggered by a combination of factors including a schema migration deployed on Friday evening and increased load from a batch job. We rolled back the migration at 5:30am which partially restored service. Full recovery occurred at 6:47am after rebuilding indexes.
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>
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> Impact: 4h 33min outage, 847 customers affected, 12,400 failed requests. Root cause was insufficient testing of migration under production load patterns. We are implementing better testing procedures.
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>
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> Let me know if you have questions.
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**Problems:**
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- ❌ Buries emotional acknowledgment (no empathy for customer impact)
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- ❌ Technical jargon obscures key insights ("corrupted indexes" - what does that mean for business?)
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- ❌ Passive voice avoids accountability ("was triggered by" - who's responsible?)
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- ❌ Vague prevention ("better testing" - what specifically?)
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- ❌ No explicit commitment to transparency
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### After (Strong - Storytelling)
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> Subject: Saturday Outage: What Happened, Why, and What We're Doing About It
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>
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> # We Failed 80% of Our Customers on Saturday
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>
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> On Saturday at 2am, our main database went down, affecting 80% of customers for 4.5 hours. This is unacceptable. Our customers trust us to keep their businesses running, and we broke that trust. Here's what happened, why it happened, and how we're ensuring it never happens again.
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>
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> ## What Happened
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>
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> **Timeline:**
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> - **2:14am:** Automated monitoring alerted on-call engineer (Sarah) to 80% error rate
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> - **2:20am:** Sarah paged database team, identified corrupted database indexes causing all queries to fail
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> - **2:45am:** CTO (me) joined incident call after Sarah escalated
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> - **5:30am:** Rolled back Friday's schema migration, partially restored service (50% → 80% success rate)
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> - **6:47am:** Fully recovered after manually rebuilding indexes
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>
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> **Customer Impact:**
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> - 847 customers (80% of active base) affected
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> - 12,400 failed requests (orders, login attempts, data syncs)
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> - 23 support tickets filed, 8 customers escalated to executives
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> - Estimated revenue impact: $15K in SLA credits
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>
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> **Our Response:**
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> - 6 engineers worked through the night
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> - We proactively emailed all affected customers by 8am Saturday with status and apology
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> - We held customer office hours Sunday 2-6pm (47 customers attended)
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> - We're issuing automatic SLA credits (no request needed)
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>
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> ## Why It Happened (Root Cause)
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>
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> **Immediate cause:** Friday evening we deployed a database schema migration (adding index to support new feature). Under normal load, this worked fine in staging. But Saturday at 2am, a scheduled batch job ran that queries the same table. The combination of migration + batch job created a race condition that corrupted the index.
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>
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> **Underlying causes (honest reflection):**
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> 1. **Insufficient testing:** We tested the migration, but not under realistic load patterns that include batch jobs
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> 2. **Risky timing:** Deploying database changes Friday evening meant skeleton crew if problems emerged
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> 3. **Missing safeguards:** Batch job didn't have circuit breaker to stop if error rate spiked
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>
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> **I take responsibility.** As CTO, I approved the deployment plan that didn't account for batch job interaction. The engineering team followed our process—the process was inadequate.
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>
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> ## What We're Doing About It
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>
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> **Immediate (This Week):**
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> 1. **Deployment freeze:** No schema changes until new process is in place (unfreezes Nov 10)
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> 2. **Load testing:** Adding batch job scenarios to staging environment (reproduces Saturday's load pattern)
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> 3. **Circuit breakers:** Batch jobs now halt if error rate >5% (prevents cascading failures)
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>
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> **Short-term (Next 30 Days):**
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> 1. **Deployment policy:** No risky changes on Fridays or weekends (lessons from Saturday)
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> 2. **Runbook updates:** Document recovery procedures for index corruption (today it was tribal knowledge)
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> 3. **Customer communication template:** Pre-written incident comms for <30min response time
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>
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> **Long-term (Next Quarter):**
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> 1. **Incident response training:** All engineers will complete tabletop exercises (scheduled for Dec)
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> 2. **Database reliability investment:** Hiring database SRE, implementing automated failover
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> 3. **Proactive monitoring:** Detect schema migration risks before they reach production
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>
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> ## Transparency Commitment
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>
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> **Public postmortem:** I'm publishing a blameless postmortem on our engineering blog by Friday (I'll share draft with you first for input). Customers deserve to know we're taking this seriously and learning from it.
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>
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> **Monthly reliability reports:** Starting this month, I'll share incident metrics at exec meetings (MTTR, incident count, severity breakdown) so you have visibility into trends.
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>
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> **Ask anything:** I know you'll have questions. I'm available all day Monday—no meeting too small, no question too uncomfortable.
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>
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||||
> ## What I Need from You
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>
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||||
> 1. **Approval for database SRE hire** (unplanned $180K/year) - this is our #1 reliability gap
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> 2. **Customer outreach support** - If any of the 8 executive escalations need personal attention from CEO/CTO, let me know
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> 3. **Patience** - We'll have fewer feature releases next 2 weeks while we prioritize reliability
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||||
>
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||||
> I'm deeply sorry for this failure. Our customers and our team deserved better. We're fixing it.
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||||
>
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||||
> —CTO Name
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>
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||||
> **Office hours:** Monday 9am-6pm, my door is open
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||||
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||||
**Why This Works:**
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||||
|
||||
✅ **Headline:** Acknowledges failure explicitly ("We Failed") - shows accountability, builds trust
|
||||
✅ **Structure:** What/Why/What We're Doing - clear, logical flow
|
||||
✅ **Specificity:** Exact numbers (847 customers, 4.5 hours, $15K) not vague ("many," "several")
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||||
✅ **Accountability:** "I take responsibility" (named CTO) vs passive "mistakes were made"
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||||
✅ **Show don't tell:** Timeline with timestamps shows urgency, not just "we responded quickly"
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||||
✅ **Humanization:** Named engineer (Sarah), personal language ("deeply sorry"), emotional honesty
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||||
✅ **Transparency:** Admits underlying causes (not just immediate trigger), commits to public postmortem
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||||
✅ **Credibility:** Concrete actions with timelines (not vague "we'll do better")
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||||
✅ **Stakes:** Shows revenue impact ($15K SLA credits) and customer escalations (8 to executives)
|
||||
✅ **Call-to-action:** Specific asks (SRE hire approval, customer outreach, patience on features)
|
||||
✅ **Accessibility:** "Office hours Monday 9am-6pm" - invites conversation, not defensive
|
||||
|
||||
---
|
||||
|
||||
## Self-Assessment Using Rubric
|
||||
|
||||
**Headline Clarity (5/5):** "We Failed 80% of Our Customers" - impossible to misunderstand
|
||||
**Structure (5/5):** What/Why/What We're Doing + Transparency Commitment - clear flow
|
||||
**Evidence Quality (5/5):** Specific data (847 customers, timeline with timestamps, $15K impact)
|
||||
**Audience Fit (5/5):** Mixed technical/non-technical with explanations, addresses exec concerns (customer impact, revenue, capability)
|
||||
**Storytelling (5/5):** Shows (timeline, named people) vs tells, humanizes data (8 escalations to executives = serious)
|
||||
**Accountability (5/5):** CTO takes responsibility explicitly, no passive voice or blame-shifting
|
||||
**Actionability (5/5):** Concrete preventions with timelines, clear asks with budget impact
|
||||
**Tone (5/5):** Serious, accountable, solution-oriented - matches crisis situation
|
||||
**Transparency (5/5):** Admits underlying causes, commits to public postmortem, invites questions
|
||||
**Credibility (5/5):** Vulnerable (admits inadequate process), shows work (root cause analysis), commits with specifics
|
||||
|
||||
**Average: 5.0/5** ✓ Production-ready
|
||||
|
||||
---
|
||||
|
||||
## Key Techniques Demonstrated
|
||||
|
||||
1. **Crisis Communication Pattern:** Acknowledge → Accountability → Action → Transparency
|
||||
2. **Specificity:** 847 customers (not "many"), 4.5 hours (not "extended"), $15K (not "financial impact")
|
||||
3. **Named Accountability:** "As CTO, I approved..." (not "the team" or "we")
|
||||
4. **Timeline Storytelling:** Timestamps create urgency and show response speed
|
||||
5. **Tiered Actions:** Immediate (this week) / Short-term (30 days) / Long-term (quarter) - shows comprehensive thinking
|
||||
6. **Vulnerability:** "I take responsibility", "deeply sorry", "customers deserved better" - builds trust through honesty
|
||||
7. **Stakeholder Addressing:** Customers (SLA credits, office hours), Team (supported through incident), Executives (asks for support)
|
||||
8. **Open Communication:** "Ask anything", "no question too uncomfortable", "my door is open" - invites dialogue
|
||||
|
||||
---
|
||||
|
||||
## Alternative Version: External Customer Communication
|
||||
|
||||
If communicating to customers (not internal execs), use Before-After-Bridge structure:
|
||||
|
||||
**Before:** "On Saturday morning, you may have experienced errors accessing our service. For 4.5 hours, 80% of requests failed."
|
||||
|
||||
**After:** "Service is fully restored. We've issued automatic SLA credits to affected accounts (no action needed), and we've implemented safeguards to prevent this specific failure."
|
||||
|
||||
**Bridge:** "Here's what happened and what we learned: [simplified root cause without technical jargon]. We're publishing a detailed postmortem on our blog Friday, and I'm personally available for questions: [email]."
|
||||
|
||||
**Key differences from internal version:**
|
||||
- Less technical detail (no "corrupted indexes")
|
||||
- More emphasis on customer impact and resolution
|
||||
- Explicit next steps for customers (SLA credits automatic, email for questions)
|
||||
- Still accountable and transparent, but focused on customer needs not internal process
|
||||
Reference in New Issue
Block a user