Predictive Analytics & Decision Science

Beyond "What Happened?"

Most enterprise data teams excel at descriptive analytics (dashboards showing what happened) and predictive analytics (forecasting what will happen).

We focus on Prescriptive and Causal Analytics: determining what will happen if you intervene.

We help organizations move beyond correlation to understand cause and effect. Whether optimizing marketing spend or managing inventory risk, we build models that isolate the true incremental value of a decision, separating signal from noise.

Case Study

Causal Marketing Attribution

Context

A healthcare services provider was running extensive retention campaigns but struggled to measure true ROI. They knew who they were targeting, but couldn't distinguish between customers who stayed because of the campaign and customers who would have stayed regardless.

The Solution

We implemented an Uplift Modeling framework (estimating Conditional Average Treatment Effects) rather than standard propensity modeling.

1 Experimental Design: Established rigorous, long-horizon control groups to ground-truth model performance.
2 Model Architecture: Built models to identify "Persuadables" (customers who respond to intervention) while filtering out "Sure Things" (customers who don't need it).

The Outcome

The system demonstrated measurable lift in retention over 180-day test periods. By targeting only customers where marketing spend actually changed behavior, the client optimized budget allocation and reduced waste on already-loyal customers.

Technical Depth: Time-Series Forecasting

We've been building forecasting systems since 2013, across domains where getting it wrong has immediate consequences.

Healthcare: Pharmacy Script Forecasting

For pharmacy benefit managers and employer-sponsored health plans, we built forecasting pipelines for prescription usage at scale—predicting demand across thousands of drug/plan combinations. Accurate forecasts directly impact formulary decisions and cost management.

Financial Markets: Long-Horizon Macro Forecasting

In algorithmic trading and investment research, we've built systems for company and sector demand forecasting over 10+ year horizons. This work requires extreme discipline around look-ahead bias, regime changes, and uncertainty quantification—the same rigor we bring to enterprise forecasting problems.

Technical Approach

We treat business decisions with the same discipline applied in financial markets.

1. Causal Inference Over Correlation

Standard machine learning finds patterns (correlation). Acting on correlation in business can be dangerous. We use causal inference techniques (T-Learners, S-Learners) to estimate the actual effect of a business lever. We don't just ask "Who will buy?"—we ask "Who will buy only if we show them this ad?"

2. Prevention of Look-Ahead Bias

A common failure in data science is training a model on data it shouldn't see yet (leakage). We implement strict backtesting protocols—the same discipline used in trading system development—to ensure models perform in production exactly as they did in simulation.

3. Managing Uncertainty

Point estimates ("Sales will be $10M") are rarely useful for decision-making. We build systems that output probability distributions and confidence intervals, allowing leadership to understand the range of outcomes and the risks involved.

How We Engage

1

Data Audit

We evaluate your existing data infrastructure to determine if it can support predictive or causal modeling—checking for history depth, consistency, and proper control groups.

2

Model Development & Validation

We build models, but more importantly, we build the validation harness. Models are tested against out-of-sample data that reflects production conditions.

3

Experiment Design

We help design A/B tests and holdout strategies that feed the models. We turn your business operations into a learning engine that generates data for future optimization.