Revenue Growth Management (RGM) is a cornerstone for managing profitability in the consumer packaged goods (CPG) sector, but the methods used for data analysis and insight generation have struggled to keep pace with growing complexities. According to a McKinsey report, RGM teams spend up to 60% of their time on manual data preparation and analysis, leaving less capacity for strategic initiatives.
The demand for more efficient, precise, and scalable solutions is amplified by broader market shifts, such as this week's acquisition of Interpublic Group by Omnicom, creating a global advertising powerhouse. This consolidation signals a shift towards more data-driven and integrated approaches in marketing and sales - a trend that directly impacts how RGM teams need to operate in CPG companies.
The advent of Generative AI for Numbers, pioneered by MetaMarketing, offers a transformative solution. Combining advanced statistical methods, algorithms, and LLMs, this technology delivers actionable insights with unprecedented speed and accuracy. Below, we explore its impact on RGM teams, with examples from a drinks manufacturer and a detergent company, and highlight how methodologies like Accuris’ Source of Business® further enhance promotional outcomes. Please note the disclaimer: Accuris is a sister company of MetaMarketing.
Reducing Analysis Time by Up to 90%
Speed is critical in RGM, where timely decisions can yield significant competitive advantages. Traditional methods relying on spreadsheets or standalone BI tools cannot keep pace with the complexity of modern data. Generative AI automates descriptive, diagnostic, and predictive analyses, reducing the time required for reporting and insights.
For example, a leading beverages manufacturer partnered with MetaMarketing to optimise its quarterly price and promotions strategy. Previously, analysing promotional data across markets took over two weeks. With generative AI, this task was completed in two days, enabling the revenue team to finalise recommendations ahead of competitors’ campaigns. This agility translated into measurable revenue gains during a key sales period.
Enhancing the Quality and Consistency of Insights
High-quality insights are essential for effective RGM. Errors in analysis can lead to suboptimal pricing, ineffective promotions, or missed opportunities. Generative AI eliminates these risks by structuring the analysis process using algorithm-driven "datastories" that identify and prioritise key data patterns.
A detergent manufacturer illustrates this benefit. MetaMarketing’s generative AI flagged an unexpected spike in sales during non-promotional weeks in specific regions. The AI identified an opportunity for micro-promotions in high-potential areas that manual analysis had previously missed. These targeted efforts increased regional market share by 5% in just three months, demonstrating the impact of precise, data-driven decisions.
Scaling Best Practices Across Teams and Regions
Ensuring consistency in applying insights across geographies and teams is a perennial challenge in RGM. Generative AI incorporates global benchmarks, industry standards, and company-specific guidelines into its automated reports. This ensures that recommendations are aligned with corporate strategies while addressing regional nuances.
For instance, an FMCG manufacturer used MetaMarketing to standardise its promotional effectiveness analysis across 10 European markets. Regional teams previously employed inconsistent methodologies, complicating performance tracking. AI-generated reports provided a unified approach, allowing management to compare results and replicate successful strategies. The Harvard Business Review notes that such alignment can improve organisational productivity by up to 15%.
Driving Promotional Effectiveness
Companies like Accuris enhance the value of promotional activities by taking a more granular approach. Using their patented Source of Business® methodology, Accuris helps FMCG companies increase promotional effectiveness by over 5%, and in some cases, up to 25%. This approach identifies the origins of incremental revenue, such as competitive switching or category expansion, while also reducing negative revenue sources like the cost of cannibalisation or retail switching.
For example, by addressing cannibalisation in its product portfolio, a detergent company achieved a 10% improvement in promotional ROI. This dual focus on maximising positive revenue sources while mitigating negative ones creates a more sustainable promotional strategy.
Quantifiable Benefits for FMCG Companies
The potential financial impact of adopting Generative AI for RGM is substantial. Gartner estimates that organisations using AI-driven analytics tools can reduce operational costs by 25% while increasing decision-making accuracy by up to 50%.
For the detergent manufacturer, automated insights uncovered inefficiencies in promotion planning, saving €2 million annually by reallocating funds to higher-performing initiatives. Similarly, MetaMarketing projects that its solutions could yield $30 million in cost savings for a large CPG client through faster, more accurate analysis.
Conclusion
Generative AI for Numbers is transforming RGM processes in FMCG companies. By automating time-consuming tasks, enhancing the precision of insights, and ensuring the consistent application of best practices, it allows organisations to achieve better outcomes with fewer resources.
Coupled with methodologies like Accuris’ Source of Business®, which focuses on both maximising incremental revenue and reducing inefficiencies, the future of RGM is one of greater agility and effectiveness. Companies that adopt these innovations will be well-positioned to optimise pricing, promotions, and product mix, maintaining a competitive edge in an increasingly data-driven marketplace.
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