Revenue Growth Management

AI/ML is not the ultimate solution for every data-related problem. We must first set up foundational descriptive and diagnostic analytics capabilities and more straightforward ML approaches before applying more advanced techniques. It’s essential to understand the business problems and work closely with functional partners to solve them in a way that aligns well with the company’s analytical readiness and operating rhythm.

The examples of Revenue Growth Analytics use cases mentioned, such as Promotional Analytics, Everyday Price Optimization, Dynamic, Automated Clearance Pricing, Bulk Purchase Optimization, Customer Segmentation & Predictive Insights, and Customer Churn & Cross-Sell Modeling, are practical and impactful capabilities that can drive measurable sales and gross profit improvements. They can be implemented using simple math and essential ML and with popular tech stacks with which pricing, supply chain, and sales partners are familiar.

Overall, the focus should be on pragmatic and co-created approaches with business stakeholders that are most likely to get adoption and impact rather than on celebrating complexity for its own sake.

Revenue Growth Analytics (RGA) is a foundational enabler for organizations looking to transform their Revenue Growth Management strategies. RGA goes beyond traditional pricing techniques and provides insights into areas such as customer mix management, customer retention and cross-sell opportunities, and customer lifetime value. One of the key techniques used in RGA is RFM (Recency-Frequency-Monetary) Analysis.

RFM Analysis is a simple yet effective method of analyzing customer transactional data to drive better customer insights and improve customer retention, profits, and customer satisfaction.

The article discusses the common issue faced by Analytics/Data Science COEs where they invest heavily in new initiatives but struggle to see adoption and measurable outcomes. I advise new leaders in the field, data practitioners, and CXOs to focus on fewer initiatives that directly impact the revenue and gross profit drivers of the business, prioritize a subset with the right balance of impact, effort, and support, and involve an internal team of experts from relevant functions in the development process. Additionally, I advocate focusing on specific areas, such as price optimization, customer churn reduction, cross-sell optimization, promotion and discount optimization, and procurement optimization, to generate substantial value and internal adoption in the first couple of years before tackling larger-scale digital transformation type efforts.

Commercial Analytics in B2B environments is a team sport: you need to win the trust and credibility of the Sales Team to accelerate the impact of analytics and drive sustainable Pricing and Data ROI for your company.

Read this brief opinion piece on why listening to and collaborating with your Sales teams is not optional – it’s essential.