End-to-end analytics solution for retail labor optimization
An analytics platform improves the accuracy and efficiency of retailer’s merchandizing execution
A large U.S. retailer lacked the data and analytics capabilities it needed to support its merchandising execution workforce. Its team focused all its time on keep-the-lights-on operations instead of developing labor management insights.
The Data and Analytics team from NTT DATA drew up a roadmap to create an analytics insight team, rebuilt key reports and used data science/machine learning to improve forecasting accuracy, delivering an annual benefit of $12-$13 million.
Besoin de l’entreprise
The retailer was managing the operations of its 30,000+ merchandizing execution workforce on a legacy data platform that struggled to support operational needs and provided no analytics capabilities.
The company maintained multiple data feeds, hundreds of standard reports, 1,200 tables, 800 views and countless ad hoc requests, all while answering countless calls from the field for support. Its team spent 100% of their time on keep-the-lights-on operations versus developing insights and analytics to drive actions.
Résultats
- Retired 600+ rarely used reports
- Rebuilt key reports to focus on true KPIs
The company created a roadmap to develop an analytical insights team, rebuild key reports and use data science/machine learning to improve forecasting accuracy.
Solution
The retailer turned to NTT DATA’s Data and Analytics team for help creating a roadmap to improve merchandizing accuracy and efficiency. The teams created a vision, roadmap and organizational design to stand up an analytical insights team focused on providing value to the business as opposed to supporting the legacy environment.
The Data and Analytics team migrated the retailer’s legacy system to the Google Cloud Platform and BigQuery. Doing so reduced tables and views from more than 2,000 to approximately 275. It also removed duplicative data and provided an architecture foundation for the future.
The team rationalized reporting, retiring more than 600 rarely used reports. It rebuilt key reports to help the retailer focus on true key performance indicators (KPIs). The project also helped automate data ingestion, transformation and extraction of data.
By using data science and machine learning, NTT DATA helped the retailer develop granular labor standards that improved forecasting accuracy by 15% and delivered $12–$13 million in annual benefits.
About the case study
A large retailer defined an analytics strategy, developed a platform and advanced analytics to improve merchandizing accuracy and efficiency.