Revenue Generation In Consumer Products:
A Case Study On AI-Driven Revenue Growth Management

The Story

In the face of unprecedented global inflation, which has reached levels unseen in decades, consumer product companies find themselves navigating challenging terrain. Retailers, in particular, are bracing for what could be their most demanding negotiations to date. In light of these circumstances, a prominent Consumer Packaged Goods (CPG) and retail group proactively engaged us to develop an AI-driven Revenue Growth Management platform.

 

Our client is a leading provider of AI and analytics services for over two decades now. With a strong presence across 15 global locations, including the UK and India, the company stands as a prominent figure in various industries: retail, technology, CPG, financial services, healthcare, and life sciences. Their extensive portfolio encompasses 5 AI startups and 8 software solution platforms. Algoscale played a pivotal role in collaborating with them to construct an AI-driven Revenue Growth Management (RGM) solution, a strategic move aimed at fortifying both growth and profit margins.

The Impact?

0 %
Increase in Revenue Growth
0 %
Time Reduced in Data Tracking
1 %
Increase in ROI
0 %
Increase in Price Decision Accuracy
0 %
Increase in Promotion effectiveness

Ready to leverage AI for strategic decision-making and maximize your revenue growth like never before?

Breaking down the building process-
The Challenge

Each consumer micro-market had its unique requirements. The challenge was to constantly grasp the factors driving customer demand and connect these factors to revenue-boosting opportunities.

All of this while helping CPG companies reduce the time spent on data analysis and focus more on acquiring practical insights and suggestions from the AI-driven RGM tool.

Our Path to the Solution

Our approach to this problem was structured and clear:

 

  • Python modules for data ingestion and orchestration ‚Äď 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 parameterised using a YAML file, such that the same data pipeline can be used for different datasets.
  • Competition analysis ‚Äď 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 ‚Äď 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.
Paving the way to Success
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