Stop Employee Attrition Before It Happens

Transform your HR data into retention strategies. Predict who is likely to leave and understand the 'Why' behind every departure.

AI Attrition Prediction

Our models analyze variables like commute time, years since last promotion, and overtime to flag high-risk employees with 85%+ accuracy.

Bias-Free Analysis

Detect systemic issues in pay gaps or promotion velocities across demographics. Ensure your DE&I initiatives are backed by hard data.

Sentiment Correlation

Correlate engagement survey scores with actual performance and retention data to separate noise from true cultural signals.

Headcount Planning

Forecast hiring needs based on historical turnover trends and business growth projections to avoid gaps in your workforce.

Data-Driven HR is the Future

Human Resources is no longer just about compliance and administration. It's a strategic function that drives business success.Deep Statistics empowers HR leaders to sit at the executive table with insights, not just intuition.

Understand the "Why"

Most tools tell you what your turnover rate is. We tell you why. Is it the manager? The commute? The competitive market rate? Our multivariate analysis isolates the key drivers of attrition in your specific organization.

Secure Employee Data

We understand the sensitivity of people data. Deep Statistics is compliant by design, processing data securely without retaining PII in our learning models.

Build a better workplace

Start your journey to a happier, more retained workforce today.



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

Report

Live Stream
LIVE STREAM
205 rows
4 features
41 ms
Throughput
265.1MB/s
Latency
14ms
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