Data Analysis & Forecasting

It depends on the use case. Sales forecasting typically needs 2+ years of data for seasonality detection. Churn prediction can work with 6-12 months. We assess your data during the initial phase and advise honestly on what is feasible.

Accuracy varies by domain and data quality. We provide clear confidence intervals and benchmark against naive baselines. Our goal is models that are meaningfully better than your current forecasting approach, with transparent performance metrics.

Yes. We deliver predictions via APIs, automated reports, and dashboard integrations that connect to your BI tools (Tableau, Power BI, Looker), CRM, ERP, or custom platforms.

Yes. We build automated retraining pipelines that incorporate new data on a schedule you define. Drift detection alerts us if model accuracy degrades, triggering a review and retraining cycle.

That is normal. Data cleaning and preparation is a core part of our process. We identify gaps, handle missing values, and engineer features that maximize model performance even with imperfect data.

Forecasting predicts future values of a specific metric - like next quarter's sales. Predictive analytics is broader, using data patterns to predict outcomes like customer churn, fraud likelihood, or equipment failure. Both use machine learning but serve different business decisions.

Yes. We build churn prediction models that analyze customer behavior patterns - usage frequency, support interactions, payment history, engagement metrics - to flag at-risk customers weeks or months before they leave, giving your retention team time to intervene.

Anomaly detection identifies unusual patterns in your data that may indicate fraud, system failures, quality issues, or security breaches. Common applications include financial transaction monitoring, manufacturing quality control, network security, and operational performance tracking.