Originally Published on: QuantzigHow we Helped FMCG Client To Measure Cannibalization at an SKU Level
Client Overview: In our partnership with a prominent consumer goods manufacturing company headquartered in the USA, operating across the USA, Europe, and Asia, we collaborated with a dedicated workforce of 600 employees.
Challenges: The client grappled with understanding the cannibalization effects on SKUs, hindering decision-making and resource allocation. The exclusive focus on brand-level ROI led to the oversight of shifts in demand between brands. Additionally, the in-house analytics team lacked tools for precise SKU-level cannibalization analysis.
Solutions: Our model meticulously analyzed sales, identifying factors such as competitive activities and promotions. Using a decomposed model, we examined demand transference among SKUs, simultaneously measuring both marketing and cannibalization effects. The implementation of a dedicated measurement tool provided real-time insights into cannibalization impact, seamlessly integrating a PowerBI dashboard with a Python model on an Azure container.
Impact Achieved:
- Unveiled insights into SKU-level cannibalization effects.
- Facilitated a 15% reallocation of the budget.
- Reduced promotion planning time to a streamlined 3-day process.
Client's Profile: A leading consumer goods manufacturer specializing in diverse products, including food, beverages, personal care items, and household goods, catering to the varied needs of a diverse customer base.
Addressed Challenges:
- Lack of SKU-level cannibalization insights hindering strategic marketing decisions.
- Inefficient resource allocation and fragmented marketing efforts.
- Limitations in SKU-level cannibalization analysis by the in-house analytics team.
Implemented Solutions:
- Comprehensive dissection of sales, considering drivers like competitive actions and promotions.
- Utilization of a decomposed model for simultaneous measurement of marketing and cannibalization effects.
- Development of a dedicated tool for real-time measurement of cannibalization impact.
Technology Utilized: Leveraged a PowerBI dashboard and a Python model on an Azure container.
