Predictive Analytics
Custom predictive models for churn, demand, revenue, and lead scoring. Trained on your historical data. Running in your stack.
What we predict
Identify customers at risk of canceling 30, 60, or 90 days out. Score every account and trigger retention actions automatically.
Predict product demand by SKU, region, and season. Reduce inventory costs and stockouts simultaneously.
Rank leads by conversion probability using behavioral, firmographic, and interaction signals. Sales focuses on what converts.
Model revenue per account, cohort, or product line with confidence intervals. Useful for planning and investor reporting.
Statistical and ML-based detection of transactions, events, or patterns that deviate from learned baselines.
Collaborative and content-based filtering for product recommendations, content personalization, and cross-sell.
Methods and tools
Client projects
Use case 01
A B2B SaaS platform needed to identify which accounts were likely to churn before renewal. The engineers we placed built a gradient-boosted model using product usage, support tickets, and billing history. It flagged at-risk accounts 45 days out with 81% precision.
Use case 02
An e-commerce company needed SKU-level demand forecasts across 8,000 products. The team we placed built a time-series ensemble that reduced forecast error by 34% vs the existing rule-based system, cutting both overstock and stockout costs.
Use case 03
A B2B software company was wasting sales capacity on low-probability leads. The engineers we placed built a conversion probability model using CRM, website behavior, and firmographic data. Sales capacity shifted 40% toward top-scored leads.
Process
step 01
We identify what data exists, what's missing, and build pipelines to produce a clean, labeled training dataset.
step 02
We evaluate candidate model families against your data. We track experiments and select based on your business metric, not just accuracy.
step 03
We validate model outputs against your domain rules. A technically correct model that makes business-nonsensical predictions doesn't ship.
step 04
The model serves predictions via API, batch job, or embedded in your existing product. Monitoring and retraining are included.
FAQ
