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Use Case Lazada Reconciliation Report

Goals of the project

  • Enhanced Root Cause Analysis: Implement a proactive approach to identify and address underlying causes of missing Lazada transactions and loyalty points discrepancies
  • Timely Issue Resolution: Aim to promptly resolve any identified discrepancies and patch data within a timeframe of 1 week
  • Minimize Customer Complaints: To minimize customer complaints related to incorrect loyalty point credits

The missions

  • Build a reconciliation report and dashboard for data comparison with data from cross-domain systems: Lazada x Order Management System (OMS) x CRM
  • Conduct a pilot study to one brand, one market (TH KIEHL’S), then expand to other brands from other markets in SAPMENA (11 brands in 4 markets: TH, ID, MY, VN)
  • Identify data quality and integration issues in the Zone based on reconciliation report
  • Work with cross-domain teams (Lazada x OMS x CPRV x HIP x Project team) to implement robust data quality control measures


  • Successfully built reconciliation report and Power BI dashboard and set up daily send out of report to stakeholders including L'Oréal CIO and Operations Lead
  • Reduced data quality issues within L'Oréal from 33% of transactions down to 0.4%
  • Expanded report to 11 brands in 4 markets, TH, ID, MY, VN within 4 months
  • Automated the daily matching file using SQL scripts in internal Federated Data Layer (FDL) system and saved processing time by 40%