Intelligence Layer
Sentiment Intelligence Engine
Real-time customer sentiment analysis across support, reviews, and social channels
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Pluriza x Velo Commerce
Predictive Analytics Layer
ML-powered forecasting for demand, churn, and revenue across product lines — turning historical data into forward-looking business intelligence.
Velo Commerce
Industry
Intelligence Layer
Services
Data ScienceML EngineeringDashboard Design
Year
2024
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Overview
Velo Commerce had years of transactional data but no way to turn it into actionable forecasts. We built a predictive analytics layer that provides demand, churn, and revenue predictions — giving leadership the confidence to plan inventory, staffing, and budgets months in advance.
The Challenge
1
Data Quality Issues
Five years of transactional data spread across three systems with inconsistent schemas, missing fields, and duplicate records.
2
No ML Infrastructure
The engineering team had no experience with model training, feature stores, or serving predictions at scale.
3
Trust Gap
Leadership was skeptical of black box predictions and needed transparent, explainable outputs they could act on.
Our Approach
We followed a build-trust-first methodology: start with a simple, explainable model, prove its value, then layer on complexity.
1
Data Unification
Consolidated five years of data from Shopify, NetSuite, and a legacy SQL Server into a clean, versioned data warehouse.
2
Feature Engineering
Built 120+ features capturing seasonality, customer lifecycle stage, product affinity, and macroeconomic indicators.
3
Model Development
Trained gradient-boosted tree models for demand and churn, and a time-series ensemble for revenue forecasting.
4
Explainability Layer
Added SHAP-based feature importance and natural-language explanations for every prediction surfaced to stakeholders.
5
Serving & Monitoring
Deployed models behind a low-latency API with drift detection, automatic retraining triggers, and alerting.
Key Screens
Twelve-month rolling revenue forecast with confidence bands
Customer-level churn scores with explainability breakdown
Product demand heatmap by region and time period
Real-time model performance and drift monitoring
Results
94%
Demand forecast accuracy
28%
Reduction in excess inventory
$2.1M
Revenue protected from churn
< 50ms
Prediction API latency
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