๐ฏ What is Sim Loyalty?
Sim Loyalty helps you model customer retention and predict long-term growth using a three-segment retention model. Instead of assuming all customers behave the same, we divide them into groups based on how long they stick around.
๐ The Three-Segment Model
Customers naturally fall into different retention patterns:
- Short-term: High churn rate (~90%/month). These are exploratory users who try your product but don't stick around.
- Medium-term: Moderate churn rate (~10%/month). Regular users who engage but may eventually leave.
- Long-term: Low churn rate (~1%/month). Your most engaged, loyal customers.
Each segment follows exponential decay: remaining(t) = initial ร (1 - churn)^t
๐ค Why Model Instead of Using Raw Data?
Great question! Here's why modeling adds value:
- ๐ฎ Future Prediction: Your data shows 12 months; the model predicts months 13-36+ using learned patterns
- ๐งช What-If Scenarios: Test "What if we reduce churn by 2%?" - impossible with historical data alone
- ๐ Multi-Cohort Growth: Your data = one cohort. Model = total customer base from overlapping monthly cohorts
- ๐ฏ Steady-State Planning: Calculate asymptote (ceiling) - where you'll level off at current acquisition rates
- ๐ก Actionable Insights: "30% short-term, 50% medium-term, 20% loyal" drives strategy better than a list of numbers
- ๐ Noise Filtering: Model reveals underlying trend, smoothing random variation and one-time events
- โ๏ธ Comparison: Compare products/cohorts by segment parameters, not just curves
Bottom line: Actual data = rearview mirror. Model = windshield. You need both to drive forward!
๐ Getting Started
Quick Start: Select a preset from Presets & Saved Sets dropdown (SaaS, E-commerce, Mobile App, or Enterprise B2B) to see example parameters.
Manual Setup:
- Step 1: Set your Monthly Customer Acquisition
- Step 2: Define your Retention Segments (must sum to 100%)
- Step 3: Charts update automatically - no button click needed!
๐ก Tip: Save your parameters with a custom name to quickly switch between scenarios!
โก Interactive Features
The simulator includes several interactive features for fluid exploration:
- Range Sliders: Each segment parameter has a slider for quick, visual adjustments
- Live Auto-Updates: Charts update automatically 300ms after you stop adjusting parameters (no "Run Simulation" button needed)
- Segment Sum Indicator: A visual progress bar shows if your segments sum to 100% (green), under 100% (orange), or over 100% (red)
- Auto-Save via URL: Your parameters are automatically saved in the browser URL as you work - refreshing the page restores your exact state!
- Stacked Area Charts: See exactly how much each segment contributes to retention and growth over time
Pro Tip: The URL autosave means you can bookmark interesting scenarios or share your exact current state just by copying the URL!
๐ฏ Using Real Data (Advanced)
If you have actual retention data from your analytics:
- Click "๐ฏ Fit Model to Data"
- Paste retention percentages (one per month, starting at 100%)
- Click Fit Parameters to calculate optimal segment parameters
- Your actual data appears as light blue dots on the retention chart
- The blue line shows the fitted model - compare visually!
๐ฌ Advanced Multi-Method Optimization: The curve fitting uses 4 different optimization algorithms simultaneously (Grid Search, Differential Evolution, Random Restart, and Simulated Annealing) and automatically selects the best result. This ensures robust fitting across diverse retention patterns, from simple exponential decay to complex multi-segment behaviors.
๐พ Save & Share Your Work
- Save Named Sets: Enter a name and click ๐พ Save to store parameters (including fitted data)
- Load Presets: Use built-in examples (SaaS, E-commerce, Mobile App, Enterprise)
- Export Charts: Download retention or growth charts as PNG images
- Export Data: Download simulation results as CSV for Excel/Sheets
- Share Links: Click ๐ Copy Link to share your exact parameters with colleagues
๐ Understanding the Charts
Single Cohort Retention: Shows how one group of customers decays over time. The stacked areas show the contribution of each segment (short-term, medium-term, long-term). Light blue dots (if present) show your actual data vs. the fitted model (blue line).
Multi-Cohort Growth: Shows your total customer base when adding new cohorts monthly. The stacked areas reveal how much each segment contributes to total growth. The dashed line shows the theoretical ceiling (asymptote).
๐ฏ Understanding Your Growth Ceiling (Asymptote)
Your growth ceiling is the customer level you naturally approach when new customers per month and churn stay steady. It's the point where new in = customers out, so the total stops rising.
The "Leaky Bucket" Analogy
Think of your business as a bucket:
- Acquisition (the tap): A steady flow of new customers each month (e.g., 1,000/month).
- Customers (the water level): Your total active customer base.
- Churn (the holes): Each month, a fixed percent applies at the segment level; in total, the leak behaves like a percent that settles to a steady effective churn as cohorts accumulate.
When you're small, the leak is tiny. As you grow, the same percent of a bigger base becomes a bigger absolute leak. Growth naturally slows until the leak equals the tap.
Rule of thumb: Ceiling โ monthly acquisition รท effective monthly churn.
Example: 1,000 per month and 5% churn โ about 20,000 customers.
In this tool, your effective churn comes from the three segments you set (short, medium, long). Early on, the overall percent leaking each month can change as the mix of cohort ages stabilizes; the rule of thumb uses the steady-state effective churn that the model derives from your segments. We calculate and show the ceiling on the Growth chart and in the โGrowth Ceilingโ KPI.
How to raise the ceiling:
- Increase acquisition (open the tap) โ ceiling rises linearly.
- Reduce effective churn (smaller holes) โ ceiling rises faster for every point of churn you cut.
๐ Key Metrics Explained
- Cohort Half-Life (Median): Time when 50% of customers have churned
- Mean Lifetime: Average customer lifespan (weighted by segment size)
- Avg Error (MAPE): Average error between model and data (<5% = excellent, 5-10% = good, >10% = review fit visually)
- Rยฒ: Variance explained (can be misleading for visual fit - use Avg Error instead)
โ ๏ธ Model Limitations
The three-segment exponential model works best for:
- โ
Steady churn patterns (SaaS, subscriptions, consumer apps)
- โ
Multiple distinct customer behavior groups
- โ
Exponential decay within segments (including 0% churn = plateau)
- โ
Gradual reactivation (captured as lower effective churn rates)
It may struggle with:
- โ S-curves (slow start, then rapid churn)
- โ U-curves/Smiley patterns (retention improving over time)
- โ Sudden step changes in retention
- โ Massive reactivation campaigns (churned users returning en masse)
- โ Network effects (retention improving as product matures)
- โ Seasonal or time-varying churn rates
Check the Avg Error % when fitting. If >10%, visually inspect the light blue dots - look for systematic patterns that the model misses.
๏ฟฝ Note on Acquisition Growth
This model assumes fixed monthly acquisition to isolate retention dynamics and calculate steady-state (asymptote). This assumption is realistic for:
- Mature businesses in stable markets
- Companies with consistent marketing spend
- Market-share limited acquisition (e.g., B2B, niches)
Fixed acquisition lets you answer key strategic questions:
- "At 1,000 customers/month, what's our ceiling?"
- "How much does improving retention lift our steady-state?"
For high-growth scenarios (e.g., 1,000 โ 1,500 โ 2,000/month), run multiple scenarios at different acquisition levels and interpolate externally. Mixing acquisition growth with retention modeling makes it harder to isolate what drives results.
๐ก Tips
- Try the built-in presets to learn the model quickly
- Save multiple scenarios to compare different strategies
- Use real data fitting for accuracy - the light blue dots show fit quality
- Watch the Avg Error % - if it's high, inspect the dots visually
- Export charts for presentations and reports
- Share links with teammates to discuss assumptions
- Interactive sliders: Use the range sliders below each parameter for fluid adjustments
- Auto-update: Charts update automatically as you adjust parameters (300ms delay)
- Segment sum indicator: Watch the progress bar to ensure segments sum to 100%
- Auto-save via URL: Your parameters are automatically saved in the URL - refresh won't lose your work!
๐งช Stress Test Examples
Try fitting these patterns to see model performance:
S-Curve (Poor Fit Expected):
100, 95, 90, 85, 75, 60, 40, 25, 18, 15, 13, 12
Slow initial churn, then rapid - model struggles with this pattern
Smiley/U-Curve (Poor Fit Expected):
100, 70, 50, 40, 35, 38, 42, 47, 52, 56, 60, 63
Retention improves over time (reactivation/network effects) - exponential decay can't capture this
Typical SaaS (Excellent Fit):
100, 82, 73, 65, 58, 52, 47, 43, 39, 36, 33, 31
Classic exponential decay - model excels here
Plateau/Sticky Users (Excellent Fit):
100, 80, 70, 65, 63, 62, 61, 60, 60, 60, 60, 60
Model handles this well with long-term segment at 0% churn