Strategic Revenue Management – Another Advanced Analytics Conquest?
In spite of the prominence of commercial performance tools lately, many consumer packaged goods (CPG) manufacturers still make their revenue growth management decisions heuristically. While these tools combine the best of data science and data engineering, they lack the business context and overlook the allied complexity that each organization brings.
What brands need is the integration of market basket and shopper panel data from retailers, a unique art of problem-solving using analytics, and the brand’s specific business context woven into a customized Strategic Revenue Growth Management capability.
One of the largest global consumer packaged goods (CPG) manufacturers in the food sector was struggling to decode the real drivers of sales and leverage them for better returns on revenue management.
They were unable to root basic trade promotion questions in reliable data:
• How much should be spent on trade?
• Which levers to use?
• Which promotions to invest in?
The Mu Sigma Approach
With a deeper dive using our proprietary tools – muUniverse for complexity mapping, and muPDNA for hypothesis-driven problem definition – we uncovered some systemic problem triggers:
• Fragmented data: Their data was being lodged into many silos in different formats, thus under-contributing to trade spend insights simply for the lack of unified data architecture.
• Unilateral outcomes: Trade spend managers with different retail partners followed diverse business outcomes and KPIs.
We combined insights from the problem exploration and our experience of working across the board for retailers and consumer packaged goods (CPG) brands to build a strategic revenue growth management platform from scratch.
This SRM framework would help a trade planning manager take data-driven, pragmatic decisions about the four pillars of trade promotion optimization (TPO):
• Promotions: When to promote a product?
• Strategic Pricing: At what price point/discount should it be promoted?
• Trade Architecture: Should the promotion be supported by features like ads and on which channel?
• Portfolio mix: If two products in the portfolio are co-promoted, will one cannibalize the sales of the other?
Since silos were the major perpetrator behind this product brand’s ineffective revenue growth management strategy, we formed a multi-disciplinary team to institute a flexible Strategic Revenue Management (SRM) framework.
The Strategic Revenue Management Platform
Data from multiple data sources were integrated into a promotion-specific cloud-based data lake. This is supported by a reusable and sustainable process to acquire, integrate, analyze, and store over 2 million rows of data.
The analytical layer deploys machine learning-based models for:
• Sales Driver Analysis: This involved building models to understand the exact contribution of each driver to the final sales of a product. These drivers include structural demand, price, display, and feature support, the impact of internal and external competitors, stock up, etc.
• Optimization and Scenario Builder: The sales driver models coupled with an optimization engine that combines all pillars of trade promo and suggests the most optimal promotional calendar for each retailer and product combination. Scenario builder is layered on top of this, to enable the end user to modify the recommended state and look at the new KPIs.
This analytical layer codified a methodology for competitor mapping and baseline generation as well.
The consumption layer hosts a suite of flexible planning and analysis tools with visualization, simulation, and optimization capabilities which enable:
• Insights Generation
• Insights Automation
• Third-Party Tools
• Third-Party Analysis
This Strategic Revenue Management framework has been instrumental in delivering value across the revenue management value chain. The cumulative insights helped identify opportunities for trade promotion optimization of about $15M yearly profits in the USA.