Monthly Login Retention Analysis

Basic cohort retention analysis for a SaaS product tracking login activity, with confidence intervals, B2B SaaS benchmark comparisons, and prioritized recommendations for addressing retention cliffs.

Input

What you provide to the skill

Analyze our monthly cohort retention for logins. Here’s our data: user_id, signup_date, event_type, event_timestamp

Output

What the skill generates for you

Retention Cohort Analysis

Data Quality Summary

Total users analyzed: 80
Date range: 2024-07-05 to 2024-12-22
Cohorts: 6 (July 2024 - December 2024)
Retention definition: login events
Cohort period: Monthly

Cohort Sizes

July 2024: 13 users
August 2024: 15 users
September 2024: 20 users
October 2024: 15 users
November 2024: 12 users
December 2024: 5 users

Retention Table

| Cohort | Month 0 | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 |
|––––|———|———|———|———|———|———||
| July 2024 | 100.0% | 61.5% | 38.5% | 7.7% | - | - |
| August 2024 | 100.0% | 53.3% | 26.7% | 6.7% | - | - |
| September 2024 | 100.0% | 50.0% | 20.0% | - | - | - |
| October 2024 | 100.0% | 40.0% | - | - | - | - |
| November 2024 | 100.0% | 33.3% | - | - | - | - |
| December 2024 | 100.0% | - | - | - | - | - |

Average Retention by Period

Month 0: 100.0% (signup month - all users active by definition)
Month 1: 47.6% (95% CI: 38.2%-57.2%)
Month 2: 28.4% (95% CI: 17.8%-41.5%)
Month 3: 7.1% (95% CI: 2.6%-17.8%)

Retention Cliff Analysis

Largest Drop Identified: Month 0 → Month 1

Drop magnitude: 52.4 percentage points
Critical finding: Losing more than half of users after first month

Benchmarked Findings

Comparison to B2B SaaS Standards

Metric	Your Product	B2B SaaS Benchmark	Assessment
M1 Retention	47.6%	60-80%	Below benchmark
M2 Retention	28.4%	50-70%	Significantly below
M3 Retention	7.1%	40-55%	Critical underperformance

Actionable Recommendations

Priority 1: Fix the M0→M1 Cliff (Immediate Impact)

Investigate:

What happens in the first 30 days that causes 50%+ churn?
When exactly do users drop off? (Days 1-7, 8-15, 16-30?)
What do retained users do differently in M0 vs. churned users?

Quick Wins:

Implement welcome email sequence with clear next steps
Add in-product onboarding checklist or tour
Trigger human outreach at day 7 for inactive users
Simplify initial setup flow

Priority 2: Identify the Retained User Profile (Strategic)

Analysis needed:

Profile the 7.1% who stay for 3+ months
What do they do in M0 that others don’t?
What features do they use consistently?

Reproducible Code

uv run scripts/analyze_cohorts.py your_data.csv --cohort-period monthly --retention-event login

SQL Query Template

SELECT 
    user_id,
    created_at AS signup_date,
    'login' AS event_type,
    event_timestamp
FROM users u
JOIN user_events e ON u.id = e.user_id
WHERE event_type = 'login'
    AND signup_date >= '2024-01-01'
ORDER BY user_id, event_timestamp;

Report Generated: 2026-01-19
Analysis Period: July 2024 - December 2024
Retention Definition: Login events
Cohort Grouping: Monthly