It’s time to rethink traditional sales and price elasticity modeling methods.

Aggregated data at the retail chain level and modern machine learning models are vital in shaping an efficient, practical, and robust approach for predicting and explaining retail sales.

Why this shift? The traditional reliance on granular, disaggregated (i.e., store-product-day level) data and old-school models are costly, complex, and often misaligned with practical management strategies.

Turning to aggregated data and modern ML approaches reduces costs, enhances model accuracy and efficiency, and makes these valuable insights more accessible to smaller firms and faster for larger ones.

Moreover, in-sourcing these capabilities builds critical, sustainable expertise for Revenue Growth Management, freeing companies from reliance on 3rd parties. This shift signifies an effective and sustainable future for Revenue Analytics and redefines competitive positioning, enabling superior service and product offerings.

Most of us are familiar with the term customer price sensitivity as an important concept especially for sales, marketing and revenue management teams. It helps us understand how price changes affect demand, profitability or market share of our products or services. This week, I will describe the most popular analytical methods that help you measure your product or service price elasticities, including a few simple and proven machine learning based approaches that your analytics or data science teams can easily do.