Client is a Fortune Global 500company, leading in packaged foods and beverages. It has interests in the manufacturing, marketing, and distribution of grain-based snack foods, beverages, and other products.
Revenue Management Data Foundation & Analytics
Revenue Management Data Foundation & Analytics
Challenges
- No Integrated System for Revenue Reporting: The client faced significant issues due to the lack of a centralized system for revenue reporting. This resulted in fragmented and inconsistent reporting across the organization.
- Multiple Reporting Systems with Different Data Sources: Various departments relied on different reporting systems, each sourcing data from distinct systems, leading to discrepancies and inefficiencies.
- No Platform for Self-Service Business Usage: Business users lacked a platform that allowed them to independently access and analyse data, creating bottlenecks and dependency on IT for reports.
- Decentralized Data Sources on Various Systems: Data was spread across multiple systems without a unified strategy, complicating data retrieval and analysis.
Solutions
- Data Extraction and Integration: We extracted data from multiple source systems, primarily SAP, ensuring a comprehensive collection of relevant data points.
- Creating a Data Foundation in HANA Cloud Data Lake: All extracted data was consolidated into the HANA Cloud Data Lake. This provided a scalable and efficient storage solution capable of handling large volumes of data.
- Data and Business Model Creation in SAP Datasphere: Using SAP Datasphere, we built robust data and business models. This involved organizing the data into meaningful structures that facilitated easy access and analysis.
- Consumption in Power BI: The final step was to make this data accessible through Power BI. Power BI served as the visualization and reporting tool, providing intuitive and interactive dashboards for end-users.
Results
- AI/ML Models for Consumer Segmentation:
We developed machine learning models to segment consumers based on their behavior and preferences. This enabled targeted marketing and personalized experiences. - Price and Market Mix Analysis:
- Using predictive analytics, we optimized pricing strategies and market mix decisions. These models helped forecast demand and adjust prices dynamically.
- Predictive Analytics:
- Our predictive analytics models provided insights into future trends and potential revenue streams. This allowed the business to make proactive decisions and stay ahead of the competition.
