Sample SaaS Analytics Report

🔴 Finding 1: Your Most Popular Feature Is Killing Retention

70 users use "reporting" as their primary feature — but they churn at 34% (24 of 70 churned) vs just 8% for "automation" users. This is counterintuitive: reporting is your most-used feature, which likely means it's where users see the most friction or unmet expectations. Users who come for dashboards leave when the dashboards don't deliver.

Finding 2: Organic Users Are 4x More Valuable Than Paid

80 organic users have a 6% churn rate and avg MRR of $67. 60 paid search users have a 28% churn rate and avg MRR of $31. Your CAC-to-LTV for paid channels is likely negative once you account for churn. You are paying to acquire users who leave.

Finding 3: 85% of Pro Plan Capacity Is Untapped

200 users, only 30 on Pro (15%). Pro users average $199 MRR, NPS 72, and 0% churn in the dataset. These users are your best product-market-fit signal. The question is not "how do we get more users" — it's "why aren't 150 of our starter/free users converting to Pro?"

Finding 4: India Is Volume, US Is Revenue

80 Indian users but avg MRR $18. 60 US users, avg MRR $89. India is 40% of your user base but ~18% of revenue. This is a go-to-market misalignment — your product pricing and positioning likely fits US/EU buyers better than Indian SMBs.

Finding 5: Pre-Churn Signal Is Detectable 30 Days Out

Churned users average 1.4 sessions in their last 30 days vs 28 sessions for retained users. This 20x gap means churn is predictable. You have a 30-day window to intervene — and no automated intervention is in place.

⚠ 12 users with 0 sessions in last 30 days are still marked "active" — these are silent churners who haven't formally cancelled. Estimated silent churn revenue risk: ~$380 MRR.

⚠ 3 pro users have not logged in for 60+ days — at $199/each, that's $597 MRR at immediate cancellation risk.

⚠ Germany (20 users) has 0% churn — investigate what's different about this segment. Could be a market to double down on.

-- Which feature has the highest churn rate?
SELECT primary_feature, COUNT(*) as users,
  SUM(CASE WHEN is_churned = 'true' THEN 1 ELSE 0 END) as churned,
  ROUND(100.0 * SUM(CASE WHEN is_churned = 'true' THEN 1 ELSE 0 END) / COUNT(*), 1) as churn_rate_pct
FROM users
GROUP BY primary_feature
ORDER BY churn_rate_pct DESC;

-- Average MRR and churn by acquisition channel
SELECT acquisition_channel, COUNT(*) as users,
  ROUND(AVG(mrr_usd::numeric), 2) as avg_mrr,
  ROUND(100.0 * SUM(CASE WHEN is_churned = 'true' THEN 1 ELSE 0 END) / COUNT(*), 1) as churn_pct
FROM users
GROUP BY acquisition_channel
ORDER BY avg_mrr DESC;

-- Identify at-risk users (low sessions, active, high MRR)
SELECT user_id, plan, mrr_usd, sessions_last_30d, last_active_date
FROM users
WHERE is_churned = 'false'
  AND sessions_last_30d < 5
  AND mrr_usd > 0
ORDER BY mrr_usd DESC
LIMIT 20;

Based on this data: