Optimising Product Sales by Discovering Dead Shelf Space with Power BI Heatmaps
A retail chain discovered 15–20% of its shelf space underperformed, impacting sales and profitability. Instead of costly IoT or planogram software, they leveraged existing tools, Power BI, Azure Synapse and Databricks to turn manual shelf and sales logs into dynamic heatmaps.
These visual insights highlighted low performing zones, guiding targeted interventions such as repositioning high margin SKUs and optimising inventory rotation.
The approach boosted shelf ROI, recovered lost revenue and established a scalable framework for continuous store optimisation, all without heavy technology investments.
Author
Pravin
Ghavare
I'm a Data & AI Lead with 9 years of experience delivering scalable Azure based solutions. I lead cross-functional teams to build high-performance, cost optimised data platforms with a strong focus on observability, FinOps, and performance tuning. I collaborate closely with stakeholders to align technical delivery with strategic business goals.
Connect with
Pravin
Ghavare
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Introduction
A retail chain found that 15–20% of their shelf space generated minimal sales. Rather than investing in costly IoT sensors or complex planogram software, they turned to Power BI heatmaps powered by Azure Synapse and Databricks, quickly identifying underperforming zones. Shelf space being one of retail’s most valuable assets, optimising it is critical, poorly performing shelves reduce product visibility, slow stock movement and hit profitability. Leveraging existing tools like Power BI allowed the retailer to maximise ROI efficiently, without heavy external investments.
Architecture Overview
Cloudaeon leveraged Databricks to link SKU sales with planogram data, tagging each product by aisle, shelf and bin location. Synapse aggregated sales volume by shelf area and Power BI translated this into a visual heatmap. Red zones revealed dead space, while green zones highlighted top performing shelves.
Store teams manually logged weekly sales and shelf IDs, feeding the raw data into the system. Databricks transformed this into a 2D shelf grid for Power BI analysis. Synapse enriched the dataset with returns and foot traffic metrics, adding layers of actionable insight. Power BI’s conditional formatting then made underperforming zones immediately visible, enabling fast, informed decisions.
Step by Step Walkthrough
Store management teams initially tracked weekly sales and shelf IDs manually, a time intensive process with limited visibility.
Databricks transformed this raw data into a structured 2D shelf grid, creating a clear map of inventory placement.
Synapse then aggregated the dataset, incorporating returns and foot traffic to provide a complete performance picture.
Power BI brought the insights to life with dynamic heatmaps: red zones highlighted underperforming shelves, while green zones revealed top performing areas.
Impact
Heatmaps revealed premium products were often in low visibility spots bottom shelves or corners.
Minor interventions, like relocating high-margin items or adding signage, drove measurable gains.
A 10% planogram adjustment based on insights boosted shelf ROI significantly.
Strategic repositioning of stagnant stock recovered 6% of lost revenue.
SKU rotation improved by 12% in low performing sections.
Heatmap dashboards became a key tool for monthly store audits, enabling continuous optimisation.
Conclusion
By turning existing sales and shelf data into actionable visual insights, the retailer achieved measurable improvements without heavy technology investments. Power BI heatmaps, powered by Azure Synapse and Databricks, made underperforming zones instantly visible, guiding small yet impactful interventions from repositioning high margin items to optimising SKU rotation. The result: higher shelf ROI, recovered revenue, and a framework for continuous store optimisation, all leveraging tools already at hand.
