Analytical Excellence Since 1996

Driving Business SUccess through
Marketing ANalytics

Suman API Gateway

High-performance API access to proprietary algorithmic engines.

Regularized Quantile Regressions

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

See Predictions & ROI Proof → View API Specs →
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.

See Predictions & ROI Proof → View API Specs →
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.

See Predictions & ROI Proof → View API Specs →
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.

See Predictions & ROI Proof → View API Specs →
Fully Deployed. High-precision decomposition of mixed-scale factors.

Stochastic K-mcube Honest Clustering

Batch-based stochastic partitioning for Big Data containing millions+ data points with mixed variable types.

See Complexity & Proof → View API Specs →
Fully Deployed. Scales up naturally for large-scale enterprise datasets.

K-mcube Honest Clustering

State-of-the-art O(N*k*P) execution speed for mixed datasets containing Numeric, Rank, and Categorical variables.

See Complexity & Proof → View API Specs →
Fully Deployed for immediate use. Bypasses intractable O(N²) distance calculations.

The Foundation

Decades of expertise in Marketing Science and Econometrics.

Grounding corporate strategy in mathematical systems.

EZConjoint™

Choice-Based Conjoint (CBC) analysis engineered for new product concept testing and optimal feature level selection.

See Predictions & ROI Proof → View API Specs →
The industry gold standard methodology for product optimization. Outperforms Hierarchical Bayes (HB) and MNLogit in holdout test accuracy.

EZPredict™

High-precision predictive classification statistical model that maximizes customer conversions and improves customer acquisition ROI.

See Predictions & ROI Proof → View API Specs (Coming Soon) →
Outperforms Gradient Boosting, XGBoost, Random Forest, and Multinomial Logistic across many commercial datasets. Captures critical minority customer segments directly from natural distributions—even when competing enhanced tree models are bolstered with synthetic oversampling (SMOTE).

EZMap™

Captures a Brand's Distinctiveness and its unique positioning in the consumer perception space, powered by Heterogeneous Hybrid Factor Analysis (HHFA).

See Perceptual Rigor & Proof → View HHFA Engine Specs →
Deploy when re-formulating brand strategy in response to new competitive market entrants or changes in positioning of currently competing brands.

EZSegment™

Comprehensive customer-centric approach to market segmentation incorporating needs, attitudes, behaviors, demographics, and other relevant variables, leveraging hybrid K-mcube clustering to redefine corporate marketing strategies.

See Strategic Framework & Proof → View Base API Specs → View Stochastic API Specs →
Identify target market segments for formulating differentiated and segment-specific marketing strategies.

EZRecommend™

High-performance recommendation engines (HHFA + Supervised Learning) for online streaming and e-commerce platforms, coupled with custom enterprise vector embeddings for Agentic AI and LLM workflows.

See Recommendation Benchmarks → View HHFA API Specs →
Outperforms Factorization Machines and standard Transformers for online entertainment platforms and LLM contextual memory.

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.

EZConjoint™

Choice-Based Conjoint (CBC) Concept Testing & Optimization

Choice-Based Conjoint is the gold standard for New Product Concept Testing in Marketing Data Science. Across standard discrete choice experiments, EZConjoint™ beats the industry-standard Hierarchical Bayes (HB) model by a wide margin across holdout test accuracy, precision, and recall.

1. Out-of-Sample Performance Breakdown

N=200 • 15 Tasks • 5 Attributes
Model Description Accuracy Precision Recall F1-Score
EZConjoint Individual: Carpoolers 0.731 0.581 0.689 0.631
EZConjoint Individual: Non Carpoolers 0.743 0.599 0.695 0.643
EZConjoint Individual - All 0.739 0.593 0.693 0.640
Hierarchical Bayes - Carpool Covariate 0.600 0.616 0.600 0.602
Hierarchical Bayes 0.578 0.605 0.578 0.579

2. Product Feature Level Importance

Relative Choice Share
Feature Level Importance
Engine Gas 16.78%
Cargo 3ft 16.50%
Price 30 16.09%
Seat 6 11.38%
Seat 8 8.54%
Cargo 2ft 8.50%
Engine Hybrid 7.09%
Price 35 6.87%
Seat 7 5.09%
Price 40 2.04%
Engine Electric 1.13%
Automotive & EV Strategy

Trimming Costly Options While Maximizing Adoption

Automakers face massive capital risk when choosing battery range, seating configurations (6 vs. 7 vs. 8 seats), cargo volume, and powertrain options (Gas, Hybrid, EV). EZConjoint models discrete choice tasks to pinpoint which standard packages drive the highest concept adoption without over-engineering unwanted trim.

Bottom-Line Impact:

Saves tens of millions in manufacturing tooling by eliminating low-utility trim levels before factory lines are committed.

SaaS & Subscriptions

Optimizing Feature Gating & Cannibalization

Software companies struggle with deciding which features belong in the "Basic", "Pro", and "Enterprise" tiers versus charging as add-ons. By simulating market response across discrete pricing steps and capability bundles, EZConjoint determines the exact gating threshold that drives user upgrades while preventing plan downgrade cannibalization.

Bottom-Line Impact:

Protects average revenue per user (ARPU) and increases Net Revenue Retention (NRR) during annual pricing overhauls.

Consumer Tech & CPG

Quantifying True Willingness-to-Pay (WTP)

Relying on direct survey questions produces severe hypothetical bias where consumers overstate their intent. EZConjoint forces realistic choice trade-offs between competing feature sets and price points, deriving pure latent part-worth utilities.

Bottom-Line Impact:

Identifies the revenue-maximizing price elasticity envelope and ensures launch pricing captures full market surplus without destroying unit volume.

Integrate the EZConjoint API directly into your product analytics pipeline.

Step 1: Interactive API Documentation

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

Open API Specs →

Step 2: Programmatic Execution Example

Send respondent choice set matrices to the estimation endpoint for part-worth utility extraction:

curl -X 'POST' \ 'https://ezconjoint.onrender.com/conjoint-engine' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -H 'x-suman-auth: 7456' \ -d '{ "dataset_url": "https://r-marketing.r-forge.r-project.org/data/rintro-chapter13conjoint.csv", "conjoint_features": ["Seat", "Cargo", "Engine", "price"], "conjoint_target": "choice", "id_col": "resp.id", "choice_set_col": "ques", "analysis_type": "individual" }'

EZPredict™

High-Precision Predictive Classification & Response Propensity Engine

Standard classifiers often boast high baseline accuracy by predicting the dominant majority, effectively ignoring at-risk customers and churn signals. EZPredict™ consistently achieves superior balanced Macro-Recall and Macro-F1 on raw, unmanipulated customer data—delivering actionable decision boundaries without requiring synthetic SMOTE data patches.

Case Study 1: Retention & Experience

Multi-Class Customer Satisfaction & Churn (Olist Brazil)

Commercial Marketplace Orders • N=31,908 Holdout Records

Multi-Class Holdout Benchmark

Macro Averaged
Architecture Accuracy Recall Macro F1
EZPredict™ 0.602 0.376 0.360
Random Forest 0.755 0.344 0.326
Multinomial Logistic 0.436 0.376 0.320
XGBoost 0.776 0.336 0.298
Gradient Boosting 0.776 0.334 0.294

Resampling Stress-Test: Conventional gradient-boosted trees require synthetic minority oversampling (SMOTE) just to detect minority classes. Even when competing models are rebalanced using SMOTE (Gradient Boosting Macro-F1: 0.355, Random Forest: 0.351), EZPredict trained strictly on raw, untouched customer data retains the overall performance lead (0.360 F1, 0.376 Recall).

Minority Tier Discovery (Raw Data vs. Tree Breakdown)

Holdout Confusion Matrices
EZPredict™
High
Med
Low
High
15.9k
1,361
5,490
Med
1,584
160
677
Low
2,392
257
1,516
True Multi-Tier Discovery
Gradient Boosting
High
Med
Low
High
22.8k
5
20
Med
2,415
0
6
Low
4,147
1
17
Misses 99.6% of Low/Med Tiers
Random Forest
High
Med
Low
High
21.9k
285
578
Med
2,290
44
87
Low
3,888
61
216
Misses 95.5% of Low/Med Tiers
Benchmark Source: Olist Brazilian E-Commerce (Kaggle/OpenML)
Case Study 2: Direct Acquisition

Bank Marketing Campaign Conversion (Resampling Stress-Test)

OpenML ID: 1461 • N=13,564 Holdout Records

Holdout Performance Benchmark

Natural vs. SMOTE Resampled
Architecture F1-Macro G-Mean Class-1 Recall
EZPredict™ (Raw Data) 0.691 0.774 71.5%
Gradient Boosting (SMOTE) 0.754 0.754 61.3%
XGBoost (SMOTE) 0.752 0.725 55.6%
Random Forest (SMOTE) 0.745 0.694 50.5%
Logistic Regression (SMOTE) 0.708 0.679 49.7%

Confusion Matrix Audits (Holdout Conversions)

Actual vs. Predicted

Exposing the Majority Fallacy: Standard models maximize top-line accuracy by defaulting to the non-converting majority, missing ~50% of subscribers even when augmented with SMOTE.

EZPredict™ (Raw Data)
10,040
1,937
452
1,135
Minority Recall: 71.5%
XGBoost (SMOTE)
11,288
689
704
883
Minority Recall: 55.6%

Operational Verdict: EZPredict™ surfaces 1,135 genuine subscribers (71.5% conversion recall) without manufacturing synthetic records. It delivers higher balanced discriminatory power (0.774 G-Mean) than SMOTE-augmented tree ensembles.

Optimization: Natural Distribution Strata Zero Synthetic Artifacts
Case Study 3: Paid Acquisition & Media Efficiency

Pay-Per-Click (PPC) Campaign Performance & Conversion Modeling

Commercial PPC Campaign Data • N=700 Records • 14 Candidate Features

Holdout Performance Benchmark

Macro Averaged (4 Decimals)
Architecture Accuracy Recall Macro F1
EZPredict™ 0.7595 0.7617 0.7594
Random Forest 0.7600 0.7600 0.7592
Multinomial Logistic 0.7467 0.7467 0.7464
XGBoost 0.7467 0.7467 0.7459
Gradient Boosting 0.7367 0.7367 0.7335

Parsimony & Attribution Integrity

Decisive Outperformance: EZPredict™ secures the top rank in both Macro Recall (0.7617) and Macro F1 (0.7594), surpassing Random Forest (0.7592) and substantially outperforming XGBoost (0.7459) and Gradient Boosting (0.7335).

Parsimony Over Complexity: While standard ensemble tree models rely on the entire 14-variable parameter space, EZPredict™ delivers superior class balance and precision using a lean, regularized core subset of only 6 primary features.

Collinearity Shielding: In paid media datasets, overlapping spend, impressions, and click rates frequently corrupt model splits. EZPredict™ eliminates collinear noise, isolating the true commercial drivers of customer conversions.

Engine: EZPredict™ Core Classifier Optimized 6-Feature Subset
Case Study 1 • E-Commerce & Retention

Proactive CSAT & Churn Prevention

Commercial satisfaction data is heavily skewed, causing standard classifiers to collapse—missing up to 99.6% of dissatisfied accounts. EZPredict™ captures 1,516 at-risk customers (36.4% recall) directly from natural data distributions where Gradient Boosting found only 17.

Bottom-Line Impact:

Protects recurring Customer Lifetime Value (LTV) by triggering targeted retention workflows before bad public reviews or churn occur.

Case Study 2 • Direct Acquisition & Outbound

Term Deposit & Campaign Conversion

Outbound sales teams waste enormous bandwidth dialing low-propensity prospects. By elevating minority class recall to 71.5%, EZPredict™ isolates genuine subscribers that standard Gradient Boosting (61.3% recall) and Random Forest (50.5% recall) systematically overlook—even after synthetic oversampling (SMOTE).

Bottom-Line Impact:

Secures significantly higher deposit volume per outreach wave while cutting fruitless call hours and agent burnout.

Case Study 3 • Paid Media & Attribution

PPC Ad Spend & Conversion Efficiency

Severe multicollinearity among spend, impressions, and bids misleads conventional bidding trees. With a lean, regularized feature space, EZPredict™ delivers top-tier 0.7617 Recall and 0.7594 F1, outperforming 14-parameter Random Forest and Gradient Boost models.

Bottom-Line Impact:

Stops budget bleed on deceptive ad clicks and reallocates media budget strictly toward highest-converting campaign assets.

Integrate the EZPredict API directly into your customer scoring and acquisition pipelines.

Step 1: Interactive API Documentation

Examine request schemas, probability outputs, and test parameters live via ReDoc.

API Deployment Pending →

Step 2: Programmatic Execution Example

Send customer feature matrices to the prediction service for instant probability estimation:

curl -X 'POST' \ 'https://sumaninc.com/api/predict-engine' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -H 'x-suman-auth: 7456' \ -d '{ "dataset_url": "https://example.com/bank-marketing.csv", "target_col": "y", "feature_cols": ["age", "job", "balance", "duration", "campaign", "poutcome"], "holdout_percent": 30.0, "seed": 3117171 }'

EZSegment™

Strategic Market Partitioning Grounded in Mathematical & Measurement Integrity

Market segmentation studies frequently cost millions of dollars and are delivered to C-suite executives by partners and principals at top-tier strategy firms. Yet they frequently fail during operational execution because conventional algorithms distort mixed enterprise data. EZSegment™ integrates customer needs, attitudes, behaviors, and demographics natively through scale-honest clustering—ensuring segments strictly reflect corporate business strategy.

Enterprise Segmentation Framework Comparison

Measurement & Rigor
Dimension Legacy Consulting Tools EZSegment™ Engine
Data Scale Integrity Forces arbitrary numeric scales onto discrete ranks & categories Honest treatment across Numeric, Rank, & Categorical scales
Strategy Alignment Generic statistical variance splits disconnected from P&L goals Goal-directed weighting reflects Cost vs. Product Differentiation
Segment Stability Re-clustering scrambles boundaries; unstable wave-over-wave Mathematically stable partitions protect long-term marketing plans
Execution Speed O(N²) distance matrices fail on enterprise Big Data State-of-the-art O(N·k·P) theoretical execution lower bound

The Imperative of Measurement Alignment

Boardroom Execution Gap: A segmentation study is only as defensible as its underlying measurement model. If customer dimensions are compromised during distance computation, strategic alignment is lost.

Strategy-Driven Clustering: Honest Clustering and Stochastic Honest Clustering are the only mathematical engines that allow C-suite executives to extract customer groupings that are:

  • Strategy-Consistent: Tailored to Cost Leadership vs. Premium Differentiation.
  • Operationally Differentiated: Sufficiently distinct to drive dedicated P&L campaigns.
  • Mathematically Stable: Persistent over time so product roadmaps don't change quarterly.
Foundation: K-mcube Architecture Boardroom-Defensible Segments
Efficiency Strategy

Cost Leadership & Basket Elasticity

When competing on operational efficiency and everyday low cost, executive leadership requires segmentation grounded in basket elasticity, replenishment cycles, and cost-to-serve thresholds. EZSegment™ isolates high-velocity customer groups to streamline supply chain alignment and promotional efficiency without margin collapse.

Bottom-Line Impact:

Minimizes promotion cannibalization and eliminates wasted spend on non-responsive, low-margin accounts.

Premium Strategy

Product Differentiation & Superiority

For brands differentiating on product excellence, design, and premium pricing, generic spend clusters are useless. EZSegment™ isolates latent part-worth willingness-to-pay, feature tier valuation, and brand equity affinity—identifying high-margin cohorts willing to pay for premium upgrades.

Bottom-Line Impact:

Enables high-margin product tiering, informs R&D feature roadmaps, and protects premium pricing power.

Go-To-Market Execution

Actionable Segment-Specific Plans

A segmentation study that cannot be operationalized by brand and product managers is a multi-million-dollar loss. EZSegment™ derives stable, non-overlapping segments with explicit scoring profiles, allowing marketing leaders to execute differentiated channel, creative, and pricing strategies for each group.

Bottom-Line Impact:

Transforms boardroom strategic intent into persistent, revenue-generating customer acquisition and retention playbooks.

Deploy strategic enterprise segmentation directly across customer databases and data lakehouses.

Step 1: Explore Underlying Clustering Engines

EZSegment™ operates on the K-mcube Honest Clustering and Stochastic Honest Clustering APIs:

Step 2: Programmatic Enterprise Execution

Pass mixed customer matrices (needs, demographics, transaction volume, and survey ranks) directly to the partitioning engine:

curl -X 'POST' \ 'https://suman-honest-clustering.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -H 'x-suman-auth: 7456' \ -d '{ "data": [ {"customer_id": "CUST_1042", "spend_annual": 4820.50, "loyalty_tier": 2, "primary_channel": "Digital", "price_sensitivity": "Low"}, {"customer_id": "CUST_1043", "spend_annual": 820.10, "loyalty_tier": 4, "primary_channel": "Retail", "price_sensitivity": "High"} ], "strategic_focus": "Product_Differentiation", "num_segments": 4 }'

EZMap™

High-Precision Perceptual Brand Mapping Grounded in Heterogeneous Hybrid Factor Analysis

A brand's market distinctiveness depends on occupying a unique position in the target segment's mind. Legacy tools warp coordinates by forcing discrete imagery onto continuous Euclidean planes. EZMap™ resolves this through proprietary Heterogeneous Hybrid Factor Analysis (HHFA), cleanly decomposing continuous performance perceptions and discrete brand traits without spatial distortion.

Segment Perceptual Map: Professionals

HHFA Projection
Image & Service (- to +) Bank vs. Mono-Line Brand 1 Brand 5 Brand 7 Brand 9 Brand 4 Brand 3 Brand 8 Brand 2 Brand 6
Note: Brand identities strictly suppressed under confidentiality covenants.

HHFA Hybrid Factor Decomposition Matrix

Continuous vs. Discrete Factors

HHFA explicitly separates Continuous Perceptions (Columns 1–2) from Discrete Perceptions (Columns 3–4), preventing data distortion across segments:

Segment Bank-Non (Cont.) Image & Srvc (Cont.) Product Terms (Disc.) Credit Value (Disc.)
Professional 5.35 7.22 -0.10 1.28
Student -10.16 13.69 7.22 6.41
Anonymized Brand Bank-Non Image & Srvc Product Terms Credit Value
Brand 10.520.300.000.58
Brand 2-0.45-0.240.710.00
Brand 3-0.09-0.460.000.00
Brand 40.17-0.120.000.00
Brand 50.300.270.000.00
Brand 6-0.44-0.130.000.00
Brand 70.080.550.000.00
Brand 8-0.37-0.270.000.58
Brand 90.26-0.410.710.58

EZMap™ is engineered around three foundational capabilities that empower brand managers and advertising agencies to define, defend, and grow brand distinctiveness:

Brand Strategy & Advertising

Locating Unique Brand Perceptions

Every consumer marketing company in the world needs to locate, identify, and quantify their brand's unique perceptions in target consumer minds. EZMap™ provides the ability to find desirable locations in the consumer perception space, achieved solely through consistent brand promises communicated via advertising.

Bottom-Line Impact:

Serves as the core competence for modern media and advertising agencies, ensuring creative work is tied directly to equity growth.

Competitive Defense

Uncovering Authentic Market White Space

Legacy mapping tools frequently warp dimensions and generate illusory white spaces. By isolating continuous performance vectors from discrete brand imagery, EZMap™ exposes whether a perceived market opening is genuine territory or an analytical distortion.

Bottom-Line Impact:

Prevents multi-million-dollar capital investments into crowded, dead-end market positioning.

Media Accountability

Tracking Brand Trajectory Over Time

Quantify the exact coordinate migration of your brand versus competitors across successive tracking waves. Verify whether a major rebrand or sustained advertising flight successfully shifted consumer perception toward your target quadrant.

Bottom-Line Impact:

Provides CMOs with empirical validation of brand equity lift and advertising ROI accountability.

Deploy high-dimensional perceptual mapping directly through the deployed HHFA API engine.

Step 1: Explore Interactive API Documentation

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

Open HHFA API Specs →

Step 2: Programmatic Execution Example

Send mixed-scale perceptual matrices to the prediction endpoint for coordinate extraction:

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

EZRecommend™

High-Precision Streaming Recommendations (HHFA + SL) & Custom Agentic AI Embeddings

When combined with supervised learning, HHFA significantly outperforms black-box Factorization Machines (FastFM, myFM), standard Random Forests, and Transformers across recommendation holdout accuracy.

Recommendation Engine Benchmark Results

Model Architecture Test RMSE Test R²
EZRecommend (HHFA + OLS Regression) 0.989 0.226
EZRecommend (HHFA + Gradient Boosting) 1.011 0.191
myFM Regressor (no HFA) 1.003 0.204
Transformer (no HFA) 1.117 0.204
FastFM SGD Regressor (no HFA) 1.130 -0.012
Streaming & E-Commerce

Superior Recommendations (Netflix, Hulu, Amazon)

When used in conjunction with supervised learning, HHFA provides significantly better product recommendations for online product sellers (such as entertainment platforms like Netflix, Hulu, or Amazon Prime). Outperforms Factorization Machines and standard recommendation engines.

Bottom-Line Impact:

Boosts catalog engagement and viewer retention by eliminating recommendation prediction error.

AI & Agentic Workflows

Customized Embeddings for Agentic AI & LLMs

HHFA fundamentally generates numeric factors or embeddings utilized by NLP, ML, AI, and Agentic AI systems. Rather than relying solely on generic global embeddings like GloVe or Word2Vec, HHFA provides customized embeddings tailored precisely to each enterprise use case.

Bottom-Line Impact:

Provides AI agents with structured domain-specific memory vectors for hyper-targeted customer context retrieval.

Integrate the EZRecommend engine directly into your streaming recommendation or LLM vector pipelines.

Step 1: Explore Interactive API Documentation

Examine request schemas and embedding generation endpoints live via ReDoc.

Open HHFA API Specs →

Step 2: Programmatic Execution Example

Send user-item matrices to generate customized latent embeddings:

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

Extremely Robust Regressions

Minimizing Median Absolute Deviations (MAD) to Eliminate Financial Distortion

While Ordinary Least Squares (OLS) squares regression allows a single extreme outlier to hijack the entire solution—our engine is robust to up to 50% outliers in the data, 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 product sensor or extreme outlier shouldn't distort your baseline cost-efficiency model. Extremely Robust minimizers ensure 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 API 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.

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
Mean Absolute Error 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
Engine Size -0.540 -0.540
HP 0.540 0.570
Fit Measure Lasso Suman Inc.
Mean Absolute Error 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 API 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 Regressions

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 API 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] }'

Advanced Hybrid Factor Analysis

Rigorous multiway data decomposition and psychometric modeling frameworks

Recommendation Engine Benchmark Results

Model Architecture Test RMSE Test R²
Hybrid Factor Analysis: OLS Regression 0.989 0.226
Hybrid Factor Analysis: Random Forest Regressor 1.045 0.135
Hybrid Factor Analysis: Gradient Boosting Regressor 1.011 0.191
Hybrid Factor Analysis: AdaBoost Regressor 1.098 0.045
FastFM SGD Regressor (no HFA) 1.130 -0.012
FastFM ALS Regressor (no HFA) 1.040 0.143
FastFM MCMC Regressor (no HFA) 1.007 0.198
myFM Regressor (no HFA) 1.003 0.204
Transformer (no HFA) 1.117 0.204
Recommendation Engines

Superior Product & Content Personalization

By feeding HFA-derived embeddings into clean regression layers, platforms can predict user product ratings and preferences with significantly higher test accuracy than black-box deep learning or standard Factorization Machines.

Bottom-Line Impact:

Boosts click-through and conversion rates by eliminating noisy recommendation errors.

GenAI & Agentic Workflows

High-Precision Structured Embeddings

Modern GenAI agents require rich structural encodings that merge user demographics, geographic context, and product attributes without losing mathematical interpretability or suffering from data sparsity.

Bottom-Line Impact:

Provides AI agents with robust memory and customer context priors for hyper-targeted interactions.

Cross-Sectional Promotion Analysis

Multi-Attribute Promotional Lift & Response Modeling

Seamlessly synthesize heterogeneous survey data, user location metrics, and behavioral profiles into cohesive latent vectors that isolate true promotional sensitivity and price elasticity.

Bottom-Line Impact:

Identifies which promotional channels and offers drive incremental volume rather than cannibalizing baseline sales.

Integrate the Advanced Hybrid Factor Analysis 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 API Specs →

Step 2: Make programmatic HTTP requests

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

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

K-mcube Honest Clustering

State-of-the-Art Speed & Math Rigor for Mixed-Scale Data

Clustering mixed data honestly without distorting variable scales while maintaining mathematical efficiency.

Theoretical Complexity Lower Bound

State-of-the-Art O(N · k · P) Speed

Where N is data points, k is clusters, and P is variables. Because exact clustering is NP-Hard, execution faster than O(N · k · P) does not exist.

Distance-Based Failure:

Distance matrix clustering is a non-starter at O(N²). DBSCAN is typically O(N log N) and O(N²) worst-case.

Scale Integrity

Honest Variable Treatment

Rigorously preserves individual measurement scales by honestly treating Numeric, Rank, and Categorical variables as they should be—without forcing arbitrary numeric conversions.

Granular Evaluation:

Explicit fit measures are independently calculated and reported across each distinct variable type.

Batch-based stochastic clustering naturally scales up to massive datasets containing millions+ data points. Here is how honest mixed-data clustering drives enterprise value:

Market Segmentation

Customer Profiling at Scale

Analyze millions of data points across numeric (spend), categorical (region), and rank (loyalty tier) variables simultaneously. Stochastic batching ensures natural scale-up without memory overflow.

Fraud Detection

Identifying Anomalous Clusters

Detect irregular transaction patterns in real-time by clustering massive mixed-type datasets, relying on the state-of-the-art O(N·k·P) execution speed to isolate fraudulent groupings instantly.

Product Assortment

Optimizing SKU Placement

Group diverse product catalogs using rank (popularity) and categorical (department) data combined with numeric metrics (margin), allowing honest distance calculations to dictate natural assortment clusters.

Deploy Honest Clustering or Stochastic Honest Clustering directly into your production pipelines.

Step 1: Explore Interactive API Documentation

Access Swagger and ReDoc interactive specifications for both clustering engines:

Step 2: Make programmatic HTTP requests

Send JSON payloads containing mixed numeric, categorical, and rank features:

curl -X 'POST' \ 'https://suman-honest-clustering.onrender.com/predict' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "data": [ {"numeric_var": 42.5, "rank_var": 2, "category_var": "Segment_A"} ] }'
Expanded Benchmark View