Applied Machine Learning

What is applied machine learning?

Applied machine learning trains models on a company's own history so they forecast, price, score, or flag something that drives a real decision. The measure of success is a rate, a shift, or a campaign that changes because of the model, not a model that works in a demo.

What can machine learning predict for a business?

Machine learning can forecast demand, recommend prices, segment customers, score risk, predict equipment failures, and flag anomalies. These are the kinds of models we have put into production:

• Demand forecasting: hotel pricing, promotion planning, and staffing at a health system
• Dynamic pricing and rate optimization: billions of rate recommendations a year for a hotel operator
• Customer segmentation and lifetime value: nearly 1,000 high-value VIP profiles identified at a resort
• Risk stratification: patient risk and readmission models at a health system, and risk scoring in financial services
• Predictive maintenance and anomaly detection: equipment failures predicted and quality defects caught earlier on a factory floor
• Language analysis: clinical notes, guest feedback, and insurance claims

How do you know a model works before it drives a decision?

We test every model against reality on your data before anyone bets a rate, a shift, or a campaign on it, and we keep monitoring it after launch, so you see results rather than a promise. For a hotel portfolio that grew more than 400% in a year, a centralized pricing platform with demand forecasts and automated rate recommendations earned more than 2 points of RevPAR Index over comparable hotels.

What data does a machine learning project need?

A machine learning project needs the history of the decisions and outcomes it is meant to improve, which usually sits in separate systems such as the PMS, POS, ERP, EHR, and CRM. When those sources disagree, we build the governed data platform first. For a financial services organization we built the warehouse from the ground up, then added forecasting, risk scoring, and claims analysis on top of it.

How do model predictions reach the people who act on them?

Predictions reach people through the tools they already use: a pricing engine that issues rate recommendations, dashboards and alerts for managers, and work queues that assign follow-up. At a health system, risk models gave clinical teams real-time visibility into patient risk, and proactive interventions measurably reduced avoidable readmissions.