Revenue Growth Management (RGM) is often mistakenly equated solely with pricing strategies. However, true RGM encompasses a broader scope, leveraging comprehensive commercial analytics to drive business growth across multiple dimensions.

Beyond pricing optimization, RGM integrates advanced metrics and analytics efforts such as Customer Acquisition Cost, Lifetime Value, Churn Rate, Marketing ROI, and Net Revenue Retention to provide a holistic view of revenue and profit opportunities.

By analyzing these diverse data points, companies can unlock insights that lead to improved customer segmentation, targeted marketing campaigns, optimized product and customer mix, and enhanced operational efficiencies.

Companies that expand their RGM focus beyond pricing are better positioned to identify untapped revenue streams, boost profitability, and achieve sustainable growth in an increasingly competitive and insights-driven marketplace.

Analytical fire drills and knee-jerk pricing actions are becoming increasingly common in the consumer goods industry. With intense competition and ever-changing customer needs, companies must be agile enough to rapidly adjust their strategies to remain competitive and deliver relative value to their consumers.

By creating a dynamic integrated performance deep dive, CPGs can identify specific areas of improvement to maximize efficiency while delivering high-quality products at competitive prices — all of which will help them stay ahead of the competition in today's market.

In the below quick guide, we'll explore the steps needed to build a reproducible and highly actionable (and impactful) performance deep dive that can help CPGs address Revenue performance shortfalls.

If you haven't tried #bayesian analysis to attack common business problems, I highly encourage you to explore it.

Companies' data science curve often starts with linear regression before a giant leap to ML within weeks or months (ensemble models, even some deep learning).

In pragmatic terms, the power of Bayesian modeling comes from being able to assign probability intervals to predictions.