Predict Demand. Optimize Inventory. Maximize Profit.

Stop stocking out or overstocking. Use advanced statistical models to forecast diverse SKU demand with precision.

Holt-Winters Forecasting

Our platform applies triple exponential smoothing to handle trend and seasonality, giving you accurate sales predictions for next week, month, or quarter.

Inventory Optimization

Calculate optimal reorder points and safety stock levels based on demand volatility, reducing carrying costs while preventing lost sales.

Market Basket Analysis

Discover product associations (Apriori algorithm) to design better bundles and store layouts. Know exactly what customers buy together.

Store Performance

Compare performance across locations normalizing for footfall and catchment area size. Identify your true star performers.

Retail Intelligence for the Modern Age

Retail is low margin and high volume. Every percentage point of efficiency counts.Deep Statistics brings the power of enterprise supply chain algorithms to growing retail brands and D2C startups.

Seasonality is King

Don't let festive spikes or seasonal slumps catch you off guard. Our models automatically detect and adjust for seasonal patterns, ensuring your forecasts align with reality.

Connect Your Data

Export from Shopify, WooCommerce, or your POS system and drop it into Deep Statistics. We handle the cleaning and processing to get you straight to the insights.

Master your inventory

Take control of your supply chain with AI-powered precision.



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

Report

Live Stream
LIVE STREAM
205 rows
4 features
42 ms
Throughput
256.7MB/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