Perfect Store Execution - Store KPI Prioritization:8-Wk Implementation

CPG organizations are focussed on strong retail execution, to stand out on the shelf. Brands struggle to execute at retail and as a result experience lost opportunities and less sales. Real time insights on sales, customer satisfaction and promotion performance enable sales team to delivered tailored recommendation to retailers. It is essential to prioritize Perfect Store KPIs for efficient resource use, strategic alignment, continuous improvement and maintain consistency of performance. Enterprises do not have an understanding of which stores respond in common behavior to Perfect Store KPIs and which stores can be clustered together for KPI strategy development. Some examples of Store KPIs that need monitoring:

1. Share of Shelf (SOS)
2. Secondary Display
3. Promotional Sales Lift
4. Priority Portfolio
5. Out of Stock Rate

Sigmoid's solution enables customers to sort & rank store KPIs within a store-cluster for Sales team to execute as a “next best action”. Following a consultative approach, interviews are conducted with the business team to understand Perfect Store Execution process and priorities. Clustering variables are identified using data driven sensitivity techniques to group stores together and then finalize the KPIs to optimize sell-in and sell-out. A consumption layer is created atop to ensure insights are available in ready shape to be disbursed to required end users. It ensures improved collaboration between Central Key accounts strategy team and on-ground sales force.

The benefits realized:

1. Recommendations for actions and strategies to optimize store layout, product placement, etc.
2. User friendly interactive visualizations
3. Actionable insights leading to effective business decisions

The following Azure workloads have been used in developing the above mentioned solution:

1. Azure Data Factory (ADF) for orchestrating data integration pipelines to integrate data from diverse sources
2. Azure Data Lake Storage, serving as the underlying storage layer
3. Microsoft Purview for a Unified Data Governance
4. Azure Machine Learning
5. Azure Synapse Analytics for integrating and analysing large data sets
6. Power BI for Business Intelligence