Fragmented sales data
Sales, customer and product data was distributed across Salesforce, Oracle R12, Oracle E-commerce Cloud, Google Analytics and other systems.
A national retailer needed a consistent view of sales and customer performance across physical stores, regions and e-commerce. Data was scattered across multiple enterprise and digital systems, with inconsistent KPIs and slow manual consolidation. NoèmeX integrated the sources, cleaned seven years of historical data and built a role-based BI application refreshed daily before business operations began.
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Sales, customer and product data was distributed across Salesforce, Oracle R12, Oracle E-commerce Cloud, Google Analytics and other systems.
Data consolidation delayed insight and consumed time that could otherwise be spent on commercial decisions.
Stores, regions and the e-commerce team were not working from a consistent performance framework.
Without a unified historical dataset, the business struggled to compare trends, seasonality and performance over time.
This summary explains the major workstreams. The downloadable case study can carry the deeper implementation narrative, diagrams, frameworks and technical detail.
Defined the sales, customer, product and channel metrics needed by store managers, regional leaders and e-commerce teams.
Mapped and integrated 10 systems into a centralized data environment supporting store and digital-channel analytics.
Collected and cleansed seven years of historical data to support trend, seasonality and comparative analysis.
Orchestrated daily refreshes and built dashboards tailored to different personas and decision contexts.
Salesforce · Oracle R12 · Oracle E-commerce Cloud · Google Analytics · Finance systems · Internal data sources
Only outcomes supported by the documented case material are shown here. Where the source describes future AI opportunities, they are not presented as delivered results.
Physical-store and e-commerce data was consolidated from ten disparate source systems.
The new environment made seven years of historical data available for comparative analysis.
Automated pipelines delivered updated metrics by the start of each business day.
The organization gained a consistent basis for understanding product popularity, customer behavior, regional performance and sales trends.
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