Scale Your SaaS with AI-Driven Insights

Stop guessing your growth. Use advanced statistical inputs to predict Churn, Forecast Revenue, and Optimize Pricing tiers instantly.

Accurate Revenue Forecasting

Move beyond simple spreadsheets. Our Holt-Winters and SARIMA models automatically detect seasonality in your MRR data to provide reliable future projections.

Churn Prediction that Works

Identify at-risk customers *before* they leave. Feed your usage logs into our wrapper-free engine to uncover the hidden signals of dissatisfaction.

Instant Cohort Analysis

Visualize retention curves automatically. Understand which user acquisition channels bring the highest LTV customers over time.

Pricing Tier Optimization

Use A/B testing statistical significance calculators to determine the optimal price points for maximum conversion without guessing.

Why SaaS Founders Choose Deep Statistics

Building a SaaS in a competitive market requires more than just gut feeling. You need data. However, traditional BI tools are expensive and complex, requiring dedicated data teams. Deep Statistics bridges this gap by offering enterprise-grade statistical analysis in a no-code interface.

No-Code needed, Just Insights

Whether you are analyzing Stripe exports or custom usage logs, our platform engages automatically. Simply upload your CSV, and our AI agent suggests the best statistical tests to run for your specific growth questions.

Secure & Private

Your financial data never trains public models. We process data largely in-browser or via secure, ephemeral cloud instances, ensuring your proprietary metrics remain yours.

Ready to optimize your MRR?

Join hundreds of data-driven founders. No credit card required for the free tier.



No Code • Automated Deep Pattern Recognition • Conversational AI Voice Assistant • Plug & Play
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Report

Live Stream
LIVE STREAM
205 rows
4 features
43 ms
Throughput
263.1MB/s
Latency
12ms
Quality
99.8%
Data Quality
Before → After Cleaning
BeforeAfter45%98%
Outlier Detection
8 Anomalies Found
⚠ 3 outliers flagged
Pattern Discovery
Hidden Clusters Found
3 distinct patterns identified
Model Accuracy
Training Progress
100%75%50%96%Epochs →

Data Cleaning

Ready
customer_id
transaction_date
amount
currency
region
referral_code
device_type
session_duration
Drop (1)

Univariate

Ready
Mean
32.45
Median
33.53
Std Dev
4.12
Transaction_ValueNumeric
Peak: 33.5
0.00500.001000.00

Distribution Analysis

Ready

Gaussian Fit Test

Column: Age_Demographic

Passed (p > 0.05)
μ = 45
184580+

Outlier Detection

Ready
Mean
425.20
Std Dev
12.5
Anomaly
3
IDValueZ-Score
Row 8429,203.4+4.2σ
Row 10512.1-3.8σ

Bivariate Analysis

Ready
X-AXISMarketing_Spend
vs
Y-AXISRevenue_Q4
Pearson Correlation
+0.87

Customer Segmentation

Ready
3
Groups
High Value
Freq > 5/mo
45%
Loyalists
Tenure > 2yr
30%
At Risk
No Activity 30d
25%

Time Series Analysis

Ready
Decomposition: Multiplicative
Original
Trend
Seasonality

K-Means Clustering

Ready
Inertia: 452.1

Classification

Ready
Model
Accuracy
98.2%
F1 Score
0.97

Confusion Matrix

850True Positive
24False Positive
12False Negative
114True Negative

Predictive Forecasting

Ready
ARIMA
PROPHET
Forecast (30d)
+24.5%
High Confidence

Deep Chat Assistant

Ready
Agent Online

I've analyzed your forecasting model. The ARIMA parameters suggest a strong seasonal component in Q4. Would you like to adjust the seasonality prior?

You

Yes, set seasonality to 12 months and run the simulation again.

Running Simulation...
Type a message...
Highly abstracted and simplified demonstration of the actual application