Payments Forecasting with Retail Data

Join us at Code Talent for our Local Expert speaker series featuring the brilliant Bruce Hicks for his talk "Payments Forecasting in the CPG and Retail sector".

A practitioner-focused talk on building insight centric, drift-resistant revenue forecasting models for retail businesses. Covers the full modeling lifecycle — from addressing pitfalls (sampling bias, simultaneity) through constructing business-weighted macroeconomic indices, fitting regression models with proper diagnostics, and establishing ongoing monitoring frameworks.

Aimed at data scientists, ML engineers, and analytics leaders who need precise (<1% annual error) forecasts that stakeholders can trust and explain. We will explore revenue forecasting for payments/retail using macroeconomic indices. This talk will cover statistical foundations, index construction, regression diagnostics, advanced time series, and monitoring.

This session is hands on so don't forget to bring your laptop!

Objectives

• A framework for decomposing retail/payments revenue into structural components (pricing, volume, mix)
• Practical techniques for building business-weighted macroeconomic indices from FRED data
• An understanding of regression diagnostics that matter in production — HAC standard errors, leveraged outliers, Cook's distance, collinearity (VIF), joint hypothesis testing, and specification testing
• Strategies for monitoring forecast drift and communicating forecast risk to stakeholders
• A versioned Python regression diagnostics module you can adapt for your own work

Topics Covered

• Statistical issues in observational data (sampling bias, simultaneity, instrumental variables)
• Dynamic systems modeling — pro-cyclical, counter-cyclical, and a-cyclical decomposition
• Business-weighted index construction from macroeconomic indicators (GDP, retail indices)
• Log-linear regression modeling with transformations and indicator functions
• Regression diagnostics — heteroskedasticity, autocorrelation, omitted variable bias, leveraged outliers
• Advanced time series concepts — stationarity (I(0) vs I(1)), cointegration, error-correction, AR(p) modeling
• Forecast uncertainty, scenario planning, and decision-oriented evaluation
• Model monitoring and drift detection in production