# doany.ai — AI Agent Analytics Launch Content Plan

**Prepared for:** Executive Review
**Date:** April 13, 2026
**Launch:** Today
**Status:** Ready for review

---

## Table of Contents

1. [SEO Blog Brief](#1-seo-blog-brief)
2. [LinkedIn + X Post Plan](#2-linkedin--x-post-plan)
3. [Launch Email Sequence](#3-launch-email-sequence)
4. [Distribution Checklist](#4-distribution-checklist)
5. [KPI Framework](#5-kpi-framework)

---

## 1. SEO Blog Brief

### Metadata

| Field | Value |
|-------|-------|
| **Target word count** | 1,800–2,100 words |
| **Primary keyword** | ai agent analytics |
| **Secondary keywords** | ai agent monitoring, llm observability, ai agent performance tracking, ai agent cost optimization, ai agent quality scoring |
| **Gap keywords to weave in** | llm monitoring dashboard, ai agent drift detection, ai ops platform |
| **Meta title** | AI Agent Analytics: Monitor Performance, Cost, and Quality in Real Time — doany.ai |
| **Meta description** (148 chars) | Track AI agent performance, cost, and conversation quality in one dashboard. AI agent analytics with drift detection. Free 14-day trial. |
| **Schema markup** | Article + Product (SoftwareApplication subtype) |
| **Internal links** | docs.doany.ai/analytics, doany.ai/pricing, doany.ai/sdk, blog.doany.ai/how-to-build-ai-agent-python |
| **Publish URL** | blog.doany.ai/ai-agent-analytics-launch |

### H1 / Title

**"Stop Guessing How Your AI Agents Perform — Start Measuring"**

### Article Structure

#### H2: The AI Agent Visibility Problem (150–200 words)
- **Hook:** Teams are shipping AI agents to production faster than ever, but most have zero insight into what happens after deploy. Success rate? Unknown. Cost per interaction? A mystery. Quality over time? Hope for the best.
- **Frame the pain:** Cite the gap — existing LLM observability tools (LangSmith, Helicone, Arize) are built for generic model tracing, not agent-specific workflows. They give you token counts, not business answers.
- **Keyword placement:** Use "ai agent analytics" and "ai agent monitoring" naturally in this section.

#### H2: Introducing AI Agent Analytics (200–300 words)
- Position as the purpose-built answer: "The observability layer your AI agents have been missing."
- Announce availability: Pro and Enterprise plans, 14-day free trial, no credit card.
- Emphasize simplicity: 2-line SDK integration (`doany.configure(analytics=True)`), dashboard live within 5 minutes.
- Internal link to docs.doany.ai/analytics.
- **Keyword placement:** "ai agent performance tracking" in this section.

#### H2: Five Capabilities That Close the Feedback Loop (400–600 words)

Structure as five H3 sub-sections:

**H3: Real-Time Performance Dashboard**
- Success rates, latency, token usage, error rates per agent. Filter by time range, agent type, workflow.
- **Benefit angle (Dana):** "The same production visibility you expect from Datadog — but purpose-built for AI agents."
- Keyword: "llm monitoring dashboard"

**H3: Conversation Quality Scoring**
- Automated 1–5 scoring using proprietary eval model. Flag low-quality conversations for human review.
- **Benefit angle (Pat):** "Answer the 'are our AI features actually good?' question with data, not anecdotes."
- Keyword: "ai agent quality scoring"

**H3: Cost Attribution**
- Per-agent, per-workflow, per-team cost breakdown. Identify optimization opportunities.
- **Benefit angle (Dana):** "Know exactly which agent is burning through your API budget — and why."
- Keyword: "ai agent cost optimization"

**H3: Drift Detection**
- Alerts when behavior shifts from baseline. Catches prompt regression, model updates, data quality issues.
- **Benefit angle (Dana):** "Detect problems before your users file tickets."
- Keyword: "ai agent drift detection"

**H3: Custom Metrics and Alerts**
- Define business KPIs (resolution rate, escalation rate). Threshold alerts via Slack, email, webhook.
- **Benefit angle (Pat):** "Track the metrics your exec team actually asks about."

#### H2: Real-World Use Cases (300–400 words)

**H3: Use Case 1 — Support Triage Agent Optimization**
- Scenario: Engineering team running a customer support triage agent. Quality scoring reveals 22% of escalations are unnecessary. Drift detection catches a regression after a prompt update. Cost attribution shows the agent costs 3x more on weekends due to retry logic.
- Outcome: Team reduces unnecessary escalations by 40%, cuts weekend costs by 60%.

**H3: Use Case 2 — Multi-Agent Workflow Monitoring**
- Scenario: DevOps team managing 12 agents across document processing, code review, and internal Q&A. Performance dashboard shows one agent's latency spiking. Custom alerts notify via Slack before SLA breach.
- Outcome: Mean time to detect agent issues drops from hours to minutes.

**H3: Use Case 3 — Executive Reporting on AI ROI**
- Scenario: PM needs to present AI feature ROI to leadership. Cost attribution + quality scoring data feeds directly into quarterly business review.
- Outcome: First data-backed AI ROI report in the company's history.

#### H2: Get Started in Five Minutes (200–300 words)
- Step 1: Enable analytics in your doany.ai SDK — `doany.configure(analytics=True)`
- Step 2: Deploy your agent (or redeploy existing agents)
- Step 3: Open app.doany.ai/analytics — data appears within minutes
- Step 4: Set up your first custom alert
- Code snippet block (Python and TypeScript examples)
- Link to full SDK docs
- Keyword: "ai ops platform"

#### H2: Try AI Agent Analytics Today (50–100 words)
- CTA: "Start your free 14-day trial — full access, no credit card required."
- Link to app.doany.ai/signup?utm_source=blog&utm_campaign=analytics-launch
- Secondary CTA: "Already on doany.ai? Enable analytics now" → docs.doany.ai/analytics

### SEO Checklist

- [x] Primary keyword ("ai agent analytics") in H1, first paragraph, one H2, meta title
- [x] 2–3 secondary keywords distributed across H2/H3 sections
- [x] Gap keywords ("llm monitoring dashboard", "ai agent drift detection", "ai ops platform") placed naturally
- [x] Internal links to pricing, docs, and existing high-ranking blog post
- [x] Schema markup: Article + Product (SoftwareApplication)
- [x] Meta description under 155 characters with primary keyword
- [x] Code snippet for featured snippet potential on "how to set up ai agent analytics"

### Repurposing Notes
- Condense "Five Capabilities" section into a standalone LinkedIn article
- Extract each use case as a standalone X thread
- Pull the "Get Started" section into a Dev.to tutorial post
- Use the H2/H3 structure as the skeleton for a YouTube walkthrough script

### Success Criteria
- Rank on page 1 for "ai agent analytics" within 30 days
- 2,500+ pageviews in the first 7 days
- Blog-to-signup conversion rate > 2%
- Average time on page > 3:00

---

## 2. LinkedIn + X Post Plan

### LinkedIn Posts

---

#### LinkedIn Post 1 — Launch Day (April 13, 9:00 AM ET)
**Target persona:** DevOps Dana
**Theme:** Problem/solution announcement

> Most teams deploy AI agents and then... hope for the best.
>
> No visibility into success rates. No insight into cost. No way to know if quality is drifting until a user complains.
>
> We've been building AI agent infrastructure at doany.ai for two years, and the number one request from engineering teams has been consistent: "I need to see what's happening in production."
>
> Today we're launching AI Agent Analytics — purpose-built observability for AI agents.
>
> What it gives you:
> - Real-time performance dashboard (success rates, latency, errors, token usage)
> - Automated conversation quality scoring (1–5, flagging low-quality interactions)
> - Cost attribution per agent, per workflow, per team
> - Drift detection that alerts you before users notice
> - Custom business KPIs with Slack/email/webhook alerts
>
> Two lines of code to enable. Dashboard live in five minutes. No complex setup, no generic LLM tracing tools — this is built specifically for AI agents.
>
> Available on Pro and Enterprise plans. Free 14-day trial, no credit card.
>
> Full details on the blog (link in comments).
>
> #AIAgents #DevTools #Observability #LLMOps #AIOps

---

#### LinkedIn Post 2 — Day 2 (April 15, 8:30 AM ET)
**Target persona:** PM Pat
**Theme:** Business metrics / ROI angle

> "How do we know our AI features are actually working?"
>
> If you're a product manager, you've heard this from your exec team. And until now, the honest answer was often: "We don't, exactly."
>
> That's the gap we built AI Agent Analytics to close.
>
> The feature I'm most excited about for PMs: Conversation Quality Scoring. Every agent interaction gets an automated quality score (1–5). You can filter by time range, agent, or workflow — and flag low-scoring conversations for human review.
>
> Combine that with cost attribution (know exactly what each agent costs) and custom business KPIs (track resolution rate, escalation rate, whatever matters to your team), and you can finally walk into a quarterly review with hard data on AI ROI.
>
> No engineering setup required beyond two lines of SDK config.
>
> 14-day free trial, full access: doany.ai/analytics
>
> #ProductManagement #AIAgents #Analytics #DevTools

---

#### LinkedIn Post 3 — Day 4 (April 17, 12:00 PM ET)
**Target persona:** DevOps Dana
**Theme:** Technical deep-dive on drift detection

> Prompt regression is the silent killer of AI agent quality.
>
> You update a prompt. Tests pass. You deploy. Two weeks later, support tickets spike and nobody connects it to the prompt change.
>
> We built drift detection into doany.ai's new analytics feature specifically for this problem. Here's how it works:
>
> 1. The system establishes a behavioral baseline for each agent over a rolling window
> 2. It monitors key signals: response distribution, quality scores, latency patterns, error rates
> 3. When behavior deviates beyond configurable thresholds, you get an alert via Slack, email, or webhook
> 4. The alert includes the specific metrics that shifted and the time window, so you can correlate with recent deploys
>
> It catches prompt regression, upstream model changes, and data quality shifts — often hours before users notice.
>
> This is the kind of production observability AI agents have been missing. Two lines of code to enable: `doany.configure(analytics=True)`
>
> Details in our launch post (link in comments).
>
> #AIAgents #LLMOps #Observability #DevOps #Monitoring

---

#### LinkedIn Post 4 — Day 6 (April 19, 9:00 AM ET)
**Target persona:** Both
**Theme:** Early results / social proof (update with real data if available)

> One week since we launched AI Agent Analytics. Here's what early adopters are finding.
>
> [Placeholder — update with real early adopter data. Suggested framing:]
>
> - Teams identifying cost optimization opportunities within the first hour
> - Quality scoring revealing patterns invisible to manual review
> - Drift detection catching issues that would have taken days to surface
>
> If you're running AI agents in production without observability, you're making decisions with incomplete data.
>
> Try it free for 14 days: doany.ai/analytics
>
> #AIAgents #Analytics #DevTools #AIOps

---

### X / Twitter Posts

---

#### X Post 1 — Launch Tweet (April 13, 9:00 AM ET)
**Target persona:** DevOps Dana
**Format:** Single tweet

> We just shipped AI Agent Analytics at doany.ai — real-time performance, quality scoring, cost attribution, and drift detection for your AI agents. Purpose-built, not another generic LLM tracer. 14-day free trial. #AIAgents #LLMOps
>
> blog.doany.ai/ai-agent-analytics-launch

---

#### X Post 2 — Launch Day Thread (April 13, 1:00 PM ET)
**Target persona:** DevOps Dana
**Format:** Thread (6 tweets)

> **1/6** Most teams deploy AI agents with zero production visibility. No quality metrics, no cost breakdown, no drift detection. We built the fix. Thread on what AI Agent Analytics does and why we built it.
>
> **2/6** Performance Dashboard: real-time success rates, latency, token usage, and error rates per agent. Filter by time range, agent type, or workflow. The same production visibility you'd expect from any serious infrastructure — now for AI agents.
>
> **3/6** Conversation Quality Scoring: every interaction scored 1–5 automatically. Flag low-quality conversations for human review. Finally answer "are our agents actually good?" with data.
>
> **4/6** Cost Attribution: per-agent, per-workflow, per-team. Know exactly where your API spend goes. We've seen teams find 30–50% cost optimization opportunities in the first week.
>
> **5/6** Drift Detection: alerts when agent behavior shifts from baseline. Catches prompt regression, model updates, and data quality issues — often hours before users notice.
>
> **6/6** Two lines of code. Dashboard in five minutes. Free 14-day trial. No credit card.
>
> doany.ai/analytics
>
> #AIAgents

---

#### X Post 3 — Day 2 (April 15, 10:00 AM ET)
**Target persona:** DevOps Dana
**Format:** Single tweet

> "How much does each AI agent cost per interaction?"
>
> If you can't answer that, you need cost attribution. It's one of five analytics tools we just shipped at doany.ai.
>
> doany.ai/analytics #AIAgents

---

#### X Post 4 — Day 4 (April 17, 2:00 PM ET)
**Target persona:** PM Pat
**Format:** Single tweet

> Product managers: next time an exec asks "what's the ROI on our AI features," open your doany.ai analytics dashboard instead of building another spreadsheet.
>
> Quality scores + cost data + custom KPIs = the AI ROI report that writes itself.
>
> doany.ai/analytics

---

#### X Post 5 — Day 6 (April 19, 10:00 AM ET)
**Target persona:** Both
**Format:** Single tweet

> If you're running AI agents in production without observability, you're flying blind.
>
> We built AI Agent Analytics to fix that. Performance, quality, cost, drift — one dashboard, two lines of code.
>
> Free 14-day trial: doany.ai/analytics #LLMOps

---

### Social Media Repurposing Notes
- Each LinkedIn post can be condensed into an X thread (and vice versa)
- The drift detection deep-dive (LinkedIn Post 3) works as a Dev.to article with expanded code examples
- Social proof post (LinkedIn Post 4 / X Post 5) should be updated with real data as it comes in
- All posts can be adapted for Reddit (remove hashtags, lead with value, no self-promo tone)

### Social Success Criteria
- LinkedIn: 50,000+ combined impressions across 4 posts, > 3% engagement rate
- X: 30,000+ combined impressions, > 2% engagement rate
- At least 200 link clicks to blog/signup from social in the first 7 days

---

## 3. Launch Email Sequence

### Email 1 — Launch Announcement (Day 0: April 13)

**Send time:** 8:15 AM ET (active users), 2:00 PM ET (prospects)
**Segments:**
- Send A (8:15 AM): Active users (3,800) + Pro plan users (1,200) + Trial users (640) = 5,640
- Send B (2:00 PM): Prospects / newsletter-only (6,760)

**Subject line:** `{{first_name}}, your AI agents now have analytics`
**Preview text:** `Real-time performance, quality scoring, and cost attribution — live today.`

**Body outline:**

> **Header:** Stop guessing. Start measuring.
>
> **Opening (2–3 sentences):**
> You're deploying AI agents. But once they're in production, how do you know they're working? Today we're launching AI Agent Analytics — real-time observability purpose-built for AI agents.
>
> **What's new (bullet list):**
> - Performance Dashboard — success rates, latency, errors, token usage per agent
> - Conversation Quality Scoring — automated 1–5 scoring for every interaction
> - Cost Attribution — per-agent, per-workflow, per-team cost breakdown
> - Drift Detection — alerts when agent behavior shifts from baseline
> - Custom Metrics & Alerts — define business KPIs, get notified via Slack/email/webhook
>
> **Setup proof point:**
> Two lines of code. Dashboard live in five minutes.
> `doany.configure(analytics=True)`
>
> **CTA button:** `Start Your Free 14-Day Trial`
> → app.doany.ai/signup?utm_source=email&utm_campaign=analytics-launch&utm_content=day0
>
> *(For Send A / existing users, CTA instead: `Enable Analytics Now` → docs.doany.ai/analytics)*
>
> **Footer:** Available on Pro ($79/mo) and Enterprise plans. Full access during trial, no credit card required.

---

### Email 2 — Feature Deep-Dive (Day 2: April 15)

**Send time:** 9:00 AM ET
**Segments:** All subscribers who opened Email 1 OR are active users (combine, deduplicate)
**Subject line:** `How AI agent drift detection actually works`
**Preview text:** `Plus: quality scoring, cost attribution, and a 5-minute setup walkthrough.`

**Body outline:**

> **Header:** Under the hood of AI Agent Analytics
>
> **Opening (2–3 sentences):**
> Two days ago we launched AI Agent Analytics. Today, a closer look at how the features work and what teams are using them for.
>
> **Section 1: Drift Detection (3–4 sentences)**
> Explain baseline establishment, rolling window monitoring, configurable thresholds, and multi-channel alerts. Position as the feature that catches problems before users do.
>
> **Section 2: Quality Scoring (3–4 sentences)**
> Explain the proprietary eval model, 1–5 scoring, and human review flagging. Position as the answer to "are our agents actually good?"
>
> **Section 3: Cost Attribution (2–3 sentences)**
> Per-agent, per-workflow, per-team. Identify the 20% of agents driving 80% of cost.
>
> **Code block: Quick start**
> ```python
> import doany
> doany.configure(analytics=True)
> # That's it. Dashboard at app.doany.ai/analytics
> ```
>
> **CTA button:** `Open the Dashboard`
> → app.doany.ai/analytics?utm_source=email&utm_campaign=analytics-launch&utm_content=day2
>
> **Secondary link:** Read the full blog post → blog.doany.ai/ai-agent-analytics-launch

---

### Email 3 — Social Proof / Follow-Up (Day 5: April 18)

**Send time:** 10:00 AM ET
**Segments:** All subscribers who opened Email 1 or 2 but have NOT started a trial or enabled analytics
**Subject line:** `What teams are finding with AI agent analytics`
**Preview text:** `Early results from the first week — and your trial is still waiting.`

**Body outline:**

> **Header:** The data is already telling stories
>
> **Opening (2–3 sentences):**
> It's been five days since we launched AI Agent Analytics. Here's what early adopters are seeing.
>
> **Social proof section (3–4 data points):**
> [Update with real data before send. Placeholder framing:]
> - "One team found 40% of their support agent escalations were unnecessary — quality scoring surfaced it in the first hour."
> - "A DevOps team running 12 agents cut their mean-time-to-detect from hours to minutes using drift alerts."
> - "Cost attribution revealed one workflow was 3x more expensive on weekends due to retry logic — a 10-minute fix saved $X/month."
>
> *(If real data isn't available by Day 5, use internal testing data or reframe as "what we've seen in testing with design partners.")*
>
> **Objection handling:**
> - "Setup takes five minutes, not five sprints."
> - "Free 14-day trial, full access, no credit card."
> - "Works with your existing doany.ai agents — just enable the flag."
>
> **CTA button:** `Start Your Free Trial`
> → app.doany.ai/signup?utm_source=email&utm_campaign=analytics-launch&utm_content=day5
>
> **P.S. line:** Questions? Reply to this email — a human will answer.

---

### Email Repurposing Notes
- Email 2's drift detection explainer → standalone Dev.to article or blog post
- Email 3's social proof bullets → social media posts once data is confirmed
- Subject line variants should be A/B tested (swap benefit-led vs. curiosity-led)

### Email Success Criteria
- Email 1: > 32% open rate (matches list baseline), > 5% CTR
- Email 2: > 28% open rate, > 6% CTR (smaller, warmer segment)
- Email 3: > 25% open rate, > 4% CTR
- Sequence total: > 200 trial signups or analytics activations attributed to email

---

## 4. Distribution Checklist

### Launch Day — April 13, 2026

#### Owned Channels

| Time (ET) | Action | Owner | Status |
|-----------|--------|-------|--------|
| 8:00 AM | Publish blog post at blog.doany.ai/ai-agent-analytics-launch | Content | [ ] |
| 8:00 AM | Update product changelog at doany.ai/changelog | Product | [ ] |
| 8:00 AM | Publish SDK docs at docs.doany.ai/analytics | Docs / Eng | [ ] |
| 8:15 AM | Send launch email — Segment A (active users, Pro, trial) | Email / Growth | [ ] |
| 10:00 AM | Enable in-app announcement banner for all logged-in users | Product / Eng | [ ] |
| 2:00 PM | Send launch email — Segment B (prospects) | Email / Growth | [ ] |

#### Social Channels

| Time (ET) | Action | Owner | Status |
|-----------|--------|-------|--------|
| 9:00 AM | LinkedIn Post 1 (company page) — problem/solution announcement | Social / Growth | [ ] |
| 9:00 AM | X Post 1 — launch tweet (company account) | Social / Growth | [ ] |
| 9:30 AM | Team personal posts — founders + eng leads share on LinkedIn and X | All Hands | [ ] |
| 1:00 PM | X Post 2 — launch day thread (company account) | Social / Growth | [ ] |

#### Earned / Community Channels

| Time (ET) | Action | Owner | Status |
|-----------|--------|-------|--------|
| 4:00 PM | Post to Reddit r/MachineLearning and r/devops (value-first, not promo) | Content / Growth | [ ] |
| 4:00 PM | Cross-post or adapted article on Dev.to | Content | [ ] |
| 4:00 PM | Consider Hacker News "Show HN" post (only if demo video is ready) | Eng / Growth | [ ] |

#### Paid Amplification (if budget approved)

| Time (ET) | Action | Owner | Status |
|-----------|--------|-------|--------|
| 10:00 AM | Launch LinkedIn Sponsored Content — boost Post 1 targeting eng leads at 50–500 person companies | Growth / Paid | [ ] |
| 10:00 AM | Launch X Promoted tweet — boost launch tweet | Growth / Paid | [ ] |
| 12:00 PM | Activate Google Ads on "ai agent analytics", "ai agent monitoring", competitor keywords | Growth / Paid | [ ] |
| 12:00 PM | Activate retargeting for blog.doany.ai visitors (7-day window) | Growth / Paid | [ ] |

#### End of Day

| Time (ET) | Action | Owner | Status |
|-----------|--------|-------|--------|
| 5:00 PM | Day 1 performance check — blog views, email stats, social engagement, signups | Analytics / Growth | [ ] |
| 5:00 PM | Flag any content or messaging issues for Day 2 adjustments | Growth | [ ] |

---

### Week 1 — April 14–19, 2026

| Date | Action | Owner | Status |
|------|--------|-------|--------|
| Apr 14 (Mon) | Monitor and respond to comments on social, Reddit, HN | Social / Growth | [ ] |
| Apr 14 (Mon) | Internal Slack update on Day 1 metrics | Analytics | [ ] |
| Apr 15 (Tue) | LinkedIn Post 2 — PM Pat / business metrics angle | Social / Growth | [ ] |
| Apr 15 (Tue) | X Post 3 — cost attribution single tweet | Social / Growth | [ ] |
| Apr 15 (Tue) | Send Email 2 — feature deep-dive | Email / Growth | [ ] |
| Apr 16 (Wed) | Publish YouTube video walkthrough (if demo video is ready by now) | Content / Design | [ ] |
| Apr 17 (Thu) | LinkedIn Post 3 — drift detection deep-dive | Social / Growth | [ ] |
| Apr 17 (Thu) | X Post 4 — PM-focused single tweet | Social / Growth | [ ] |
| Apr 17 (Thu) | Pitch to 2–3 industry newsletters (The Pragmatic Engineer, TLDR, etc.) | Growth | [ ] |
| Apr 18 (Fri) | Send Email 3 — social proof / follow-up | Email / Growth | [ ] |
| Apr 19 (Sat) | LinkedIn Post 4 — early results / social proof | Social / Growth | [ ] |
| Apr 19 (Sat) | X Post 5 — week recap single tweet | Social / Growth | [ ] |
| Apr 19 (Sat) | Week 1 performance report — full metrics review | Analytics / Growth | [ ] |

### Dependency Notes
- Demo video (ETA noon April 13): if delayed, defer YouTube and HN Show HN until video is ready
- Product screenshots: needed for blog and social — confirm with design team by 8 AM
- Social proof data for Email 3 and social Post 4: pull from product analytics on April 17

---

## 5. KPI Framework

### Awareness Metrics

| Metric | 7-Day Target | 30-Day Target | Tool | Owner |
|--------|-------------|---------------|------|-------|
| Blog post pageviews | 2,500 | 8,000 | GA4 | Analytics |
| Social media impressions (LinkedIn + X) | 80,000 | 200,000 | Native analytics | Social |
| Email reach (total sends) | 12,400 | 18,000 (incl. re-sends) | Mailchimp | Email |
| Brand mention volume | 50+ mentions | 150+ mentions | Brand monitoring tool | Growth |

### Engagement Metrics

| Metric | 7-Day Target | 30-Day Target | Tool | Owner |
|--------|-------------|---------------|------|-------|
| Blog avg. time on page | > 3:00 | > 3:00 | GA4 | Analytics |
| Blog scroll depth (to CTA) | > 60% | > 60% | GA4 | Analytics |
| LinkedIn engagement rate | > 3.2% | > 3.0% | LinkedIn Analytics | Social |
| X engagement rate | > 2.1% | > 2.0% | X Analytics | Social |
| Email open rate (sequence avg.) | > 30% | — | Mailchimp | Email |
| Email CTR (sequence avg.) | > 4.5% | — | Mailchimp | Email |

### Conversion Metrics

| Metric | 7-Day Target | 30-Day Target | Tool | Owner |
|--------|-------------|---------------|------|-------|
| Blog → trial signup | > 2% conversion | > 2% conversion | GA4 + Product Analytics | Analytics |
| Email → trial signup / analytics activation | 200 total | 400 total | Mailchimp + Product Analytics | Email |
| Social → blog click-through | 200 clicks | 500 clicks | UTM tracking via GA4 | Social |
| Free trial starts (all channels) | 300 | 800 | Product Analytics | Product |
| Analytics feature activation (existing users) | 15% of active users (570) | 30% of active users | Product Analytics | Product |

### Revenue Metrics

| Metric | 7-Day Target | 30-Day Target | Tool | Owner |
|--------|-------------|---------------|------|-------|
| Trial → paid conversion | Track (no target yet) | > 20% of trials | Product Analytics + CRM | Revenue |
| Pipeline influenced by launch content | Track | $50K+ influenced pipeline | CRM (attribution) | Revenue |
| Expansion revenue (existing Pro → Enterprise) | Track | 5+ Enterprise conversations | CRM | Sales |

### Reporting Cadence

| Report | Frequency | Owner | Audience |
|--------|-----------|-------|----------|
| Daily dashboard check | Daily (launch week) | Growth | Growth team |
| Week 1 performance report | April 19 | Analytics / Growth | Exec team |
| 30-day launch retrospective | May 13 | Growth | Exec team + marketing |

### Success Criteria Summary

**The launch is a success if, at 30 days:**
1. Blog post ranks on page 1 for "ai agent analytics"
2. 800+ free trials started with launch content attribution
3. 30% of active users have enabled analytics
4. 20%+ trial-to-paid conversion rate
5. $50K+ in influenced pipeline

**The launch needs adjustment if, at 7 days:**
- Blog pageviews < 1,500
- Email open rate < 25%
- Trial signups < 150
- Feature activation < 10% of active users

---

## Appendix: Open Items and Risks

| Item | Status | Impact | Mitigation |
|------|--------|--------|------------|
| Product screenshots from design | Not yet delivered | Blog and social posts need visuals | Use SDK code snippets and dashboard mockups; follow up with #design-requests |
| Demo video | In progress, ETA noon today | YouTube post and HN submission depend on it | Defer YouTube/HN to mid-week if delayed; launch blog and email without video |
| Social proof data for Email 3 | Depends on real usage data by Day 5 | Email 3 credibility | Fallback: use internal testing data or reframe as "design partner" results |
| Jamie (content strategist) return | Expected Thursday April 16 | Week 1 execution | Growth team covers Mon–Wed; Jamie picks up Thu–Sat posts and newsletter pitches |

---

*Prepared by the Growth team, April 13, 2026. Ready for executive review.*