Superstore saves $1 million in 18 months with retail performance analysis
About The Store
The challenge: Long processing and no data optimization
Businesses often rely on instincts when it comes to business growth and undervalue the power of data. The issue was scattered data or no data structures.
Lack of complete view across data hierarchies.
Managed 30+ file types with 60+ daily tables.
Required month-to-date data updates and storage.
Changes in input file structures complicate analysis.
Large data volume with itemized sales lines.
Retail data analytics was delayed due to long processing.
Here's how we created a single data centralization platform.
With the implementation of a standard data structure and a dynamic process, we were able to improve internal culture, customer service, and staff scheduling leading to more efficient and productive marketing.
Retail data platform file management automated.
Built adaptable data structures for changing files.
Developed snapshot views with dynamic charts for KPIs.
In the web portal, MySQL database was used.
Built a low-code computation engine for custom workflows.
The measurable impact
Achieved over $1 million in cost savings within 1.5 years.
Enabled cross-team access to critical insights.
Reduced fraud detection time from 4 weeks to 72 hours.
Made insights accessible with filtered dashboards.
Reduced manual adjustments despite monthly file changes.
This case study highlights That’s where retail sales analytics comes in. So, what are retail store analytics, and how can they make such a big difference? Let’s examine how sales reporting and metrics work and introduce you to building a better business.
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