The PARIS Method: A Structured Framework for Merchant Root Cause Analysis in Modern Retail
Retail organizations confront extraordinary operational complexity as digital commerce, physical stores, supply chains, and customer touchpoints generate massive information flows that exceed human processing capacity. The PARIS framework emerged to help merchants navigate this data-rich environment through a five-stage process designed specifically for retail challenges. Problem identification establishes whether performance deviations require intervention or represent normal market fluctuations. Assessment employs iterative investigation techniques to uncover genuine root causes hidden beneath surface symptoms. Recommendation development translates findings into targeted strategies aligned with organizational capabilities. Implementation coordinates cross-functional teams to execute solutions across traditionally siloed departments. The final Sustainability phase establishes continuous tracking systems that monitor solution effectiveness over extended periods, catching early signs of deterioration. Where other business frameworks force retail into manufacturing molds, PARIS recognizes that December differs from July, promotions create chaos, rivals strike without warning, and shoppers abandon loyalty instantly. Technology amplifies human judgment throughout—machines flag irregularities buried in data mountains, graphics reveal connections between unrelated metrics, and forecasting engines predict which fixes will succeed. Real-world deployment encounters friction: Veterans trust experience over equations, finance departments balk at infrastructure spending, and merchandisers struggle with statistical software. Fresh techniques emerge daily—cloud services eliminate server rooms, conversational analytics replace code writing, and industry groups share proven blueprints. Retail's future likely holds PARIS as routine practice rather than competitive advantage, once costs drop and complexity vanishes behind user-friendly interfaces.
Institutions
- Film Independent (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-07-19
- DOI
- https://doi.org/10.5281/zenodo.16110765
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00