AI-driven Revenue Growth Management for Consumer Products Companies

AI-driven Revenue Growth Management for Consumer Products Companies

Industry Challenges

With the changing marketplace and the impact of the pandemic, traditional approaches for achieving consumer revenue growth are now more effective for leading Consumer Product companies. The solution lies in adopting Revenue Growth Management (RGM) strategies; however, the challenge is that consumer micro-markets have unique strategic requirements that must be addressed to balance revenue growth with sustainable profit margins.

  • Management
    Leading Consumer Packaged Goods (CPG) and retail groups are now utilizing Revenue Growth Management (RGM).

  • Margins
    RGM is becoming increasingly important as CP companies aim to balance revenue growth and sustainable profit margins.

  • Micro-Market
    RGM is still a challenge due to the unique strategic requirements of each consumer micro-market.

Problem Statement

To understand the levers of consumer demand continuously and create linkages to revenue-growth opportunities while enabling CPG companies to spend less time analyzing data and more time getting actionable insights and recommendations through an AI-driven RGM tool.

Our Solution

  • Python modules for data ingestion and orchestration – Our Data Engineering experts began by creating Python modules for the client to perform ingestion for different modules and orchestrate them into a data pipeline that fetches data from Azure/SQL, performs ETL operations (specific to a module), and dumps the data into a PostgreSQL database for the app. The configuration of the dataset was parameterized using a YAML file, such that the same data pipeline can be used for different datasets.

  • Competition analysis – Algoscale performed competition analysis to find out competitors of the product in the same market based on sales and other KPIs. We created a Spark script to perform an end-to-end job from processing the raw file, transforming the dataset, performing competition analysis, and ingesting the data into the PostgreSQL database for the app.

  • Exploratory data analysis – Our Data Science experts conducted exploratory data analysis using Python libraries and running pipelines in the trained AI model of the client for predictive analysis.


Business Impact

  • Scalability: Continuously track performance and impact of marketplace changes with automated updates

  • Usability: Analyze a market status by marketplace facts, verified principles, category, and brand strategy

  • Adaptability: Recommend best pricing and promotion actions through simulations and optimization

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