Analytical Excellence Since 1996

Driving Business SUccess through
Marketing ANalytics

Suman API Gateway

High-performance API access to proprietary algorithmic engines.

Regularized Quantile Regression

Advanced quantile-based regularized regressions performing multi-quantile optimization. Outperforms Quantile Regression and Quantile Boosting.

View Public API Specs → View Performance Benchmarks →
Fully Deployed. Provides robust quantile error minimization, beating best-in-class predictive models by a wide margin.

Extremely Robust Regressions

Maximally-Robust Linear Model for accurate ROI and elasticity estimates. Outperforms Quantile and HuberT.

View Public API Specs → View Performance Benchmarks →
Fully Deployed. Provides superior robustness against outliers for sensitive business metrics.

Doubly Robust Regularized Regressions

Engineered to provide robust predictive modeling with resilience to extreme data and missing values.

View Public API Specs → View Performance Benchmarks →
Fully Deployed. Mathematical framework remains stable to outliers, extreme data, and missing values.

Advanced Hybrid Factor Analysis

Continuous and discrete factor decomposition for hybrid datasets.

View Public API Specs →
Fully Deployed. High-precision decomposition of mixed-scale factors.

Stochastic K-mcube Honest Clustering

Integrates localized random sampling mechanics with Honest Clustering.

View Public API Specs →
Fully Deployed. Optimized for big-data environments.

K-mcube Honest Clustering

Clusters variables on different measurement scales honestly.

View Public API Specs →
Fully Deployed for immediate use. Accessible via cloud-native API.

The Foundation

Decades of expertise in Marketing Science and Econometrics.

Grounding corporate strategy in mathematical systems.

EZPredict™

High precision predictive modeling for categorical and continuous outcomes.

Used for customer prospecting and risk decisions where high accuracy is the primary ROI driver.

EZMap™

Identifies unique brand axes and positioning opportunities.

Deploy when re-assessing brand strategy in response to new competitive market entrants.

EZSegment™

Goal-based approach leveraging weighted hybrid clustering.

Identify targetable market segments for product design, placement, and pricing.

EZCompete™

Identifies SKU-level threats and quantifies susceptibility.

Essential for establishing defenses against market share erosion.

EZPromo™

Analyzes lift and profitability to clarify sales volume trade-offs.

Optimizes promotional spend by identifying which strategies drive true incremental profit.

Extremely Robust Regressions

Minimizing Median Absolute Deviations (MAD) to Eliminate Financial Distortion

While Ordinary Least Squares (OLS) squares errors—allowing a single extreme outlier to hijack the entire solution—our engine strictly minimizes the median of the absolute deviations (MAD), consistently outperforming HuberT and QuantileReg.

1. Stackloss Data

Chemical plant operational sensor spikes.

Coeff OLS Huber QuantReg Ext.Rob
const -39.900 -41.000 -39.700 -37.100
AIRFLOW 0.720 0.830 0.830 0.850
WATERTEMP 1.300 0.930 0.570 0.120
ACIDCONC -0.150 -0.130 -0.060 0.000
RobustR2 0.782 0.814 0.831 0.894

2. House Prices

Ames housing valuation skews.

Coeff OLS Huber QuantReg Ext.Rob
const -85.00k -78.00k -72.00k -65.00k
LotArea 0.650 0.710 0.750 0.820
OverallQual 27.40k 25.80k 24.90k 23.50k
GrLivArea 56.890 59.200 61.500 64.300
RobustR2 0.521 0.542 0.548 0.583

3. Wine Quality

Baseline error ratios and robust R2.

Metric OLS Huber QuantReg Ext.Rob
Error Ratio 0.160 0.160 0.150 0.112
RobustR2 0.841 0.844 0.852 0.891
Industrial Operations

Preventing False Alarms & Wasted Capital

A single faulty SCADA sensor or extreme outlier shouldn't distort your baseline cost-efficiency model. MAD minimization ensures your operating budget targets structural inefficiencies—not transient noise.

Bottom-Line Impact:

Stops costly false alarms and unnecessary emergency maintenance dispatches.

Real Estate & Lending

Surviving Distressed Comps

Foreclosures, fire sales, and luxury tear-downs routinely corrupt standard valuation models. Lock in true baseline market values to price portfolios accurately without being blindsided by localized anomalies.

Bottom-Line Impact:

Protects mortgage underwriting and collateral pricing from severe tail-risk distortions.

Viticulture & Winemaking

Isolating True Vintage Drivers

Strip away subjective sommelier scoring bias and extreme weather anomalies to identify the exact chemical and process factors that dictate true market value and product quality.

Bottom-Line Impact:

Directs R&D and QA spending toward variables that actually drive margin and consumer preference.

Integrate the Extremely Robust Regressions API directly into your analytics pipeline within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open Public /redoc Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous robust scoring:

curl -X 'POST' \ 'https://suman-mdad-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80] }'

Doubly Robust Regularized Regressions

Robustness to Extreme Data and Missing Values for Unbiased Predictive Modeling

While standard Lasso regressions return undefined metrics under extreme data corruption and missing values, our Doubly Robust Regularized engine maintains structural integrity, delivering reliable coefficients and superior fit metrics (rounded to 3 decimal places).

1. Advertising Dataset

Normalized scale coefficients and fit measures.

Feature Lasso BCR (Doubly Robust)
TV 0.740 0.710
radio 0.530 0.500
newspaper 0.000 0.000
Fit Measure Lasso BCR
MAE 0.242 0.238
R-square --- 0.902
Deviation Accounted For --- 0.714

2. Vehicle Valuation Dataset

Normalized scale, no missing data.

Feature Lasso Suman Inc.
Mileage -0.190 -0.220
Weight 0.560 0.540
Disp. -0.540 -0.540
HP 0.540 0.570
Fit Measure Lasso Suman Inc.
MAE 0.452 0.446
R-square --- 0.612
Deviation Accounted For --- 0.421
Car Price Predictions

Surviving Odometer Rollbacks and Incomplete Listings

Real-world pre-owned vehicle datasets are riddled with extreme valuation anomalies and massive gaps in optional feature descriptions. Standard models either throw out valuable rows or let outliers warp the entire pricing curve.

Bottom-Line Impact:

Automatically neutralizes extreme data distortions and circumvents missing data.

Advertising Impact on Sales

Shielding Media Attribution from Tracking Gaps

Modern omnichannel marketing datasets frequently suffer from broken tracking tags and erratic seasonal spend spikes. Standard regression algorithms collapse when faced with these gaps.

Bottom-Line Impact:

Delivers crystal-clear media mix attribution by immunizing ROI models against missing touchpoints and spend outliers.

Integrate the Doubly Robust Regularized Regressions API directly into your analytics pipeline within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open Public /redoc Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous robust scoring:

curl -X 'POST' \ 'https://drr-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80] }'

Regularized Quantile Regression

Enterprise-grade multi-quantile optimization engine for robust distributional forecasting.

Demonstrating superior error minimization by our Algorithm across both lower and extreme upper tails compared to current best-in-class methods (QuantileReg, LightGBM, and XGBoost). (Click any chart to enlarge)

Housing Dataset

Housing Dataset Benchmark

Superior lower-tail baseline control and halved absolute error in the upper tail.

Retail Sales Dataset

Retail Sales Dataset Benchmark

Maintains tight error bounds at low quantiles while suppressing tail expansion.

Synthetic Simulation

Synthetic Dataset Benchmark

Flawless error minimization across the entire spectrum from lower to upper quantiles.

Leverage quantile profiling to transition from average-case approximations to targeted, risk-adjusted decision-making.

Customer Lifetime Value (CLV)

Predict upper-quantile customer cohorts (Q ≥ 0.85) to isolate high-value VIP segments, optimizing luxury acquisition budgets and retention incentives without wasting spend on median averages.

Financial Risk & Exposure

Quantify extreme downside risks and tail-end credit defaults by modeling lower and upper error bounds accurately under severe economic volatility or market shocks.

Inventory & Demand Forecasting

Prevent stockouts during peak surges by targeting upper quantiles for safety stock, while minimizing overstock holding costs at lower quantiles during sluggish demand periods.

Integrate the Regularized Quantile Regression API directly into your corporate analytics infrastructure within minutes.

Step 1: Explore Interactive API Documentation

Examine request schemas, JSON payloads, and test parameters live via ReDoc.

Open Public /redoc Specs →

Step 2: Make programmatic HTTP requests

Send JSON feature matrices to the production endpoint for instantaneous multi-quantile scoring:

curl -X 'POST' \ 'https://suman-regularized-quantile-regression.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "features": [1.20, 0.50, -0.80], "quantiles": [0.10, 0.50, 0.90] }'
Expanded Benchmark View