How we helped

Riveron initially conducted a discovery phase to assess the retailer’s existing financial planning processes and systems in detail. With a proliferation of growth initiatives and the amount of financial data needed to track them, the company’s existing tools could not efficiently support the standardized reporting required. As a result, the finance team was spending a considerable amount of time preparing data manually and resolving metric discrepancies. In order to support the evolving business requirements, the retailer needed to transform its financial planning and analysis processes and systems.

Following the discovery phase, Riveron began working with the company to stand up a business intelligence and reporting infrastructure that would improve the accuracy of the forecasts and automate processes. To increase the cadence of forecasting adjustments, we integrated the retailer’s promotional calendar with its real-time transactional data and models based on historical financials, enabling the retailer to reforecast weekly instead of monthly and biannually. We also improved forecast and KPI data integration, dashboards, and visualizations, making the reporting and analysis easier to produce and consume. Further, we resolved specific financial challenges in the process, including reconciling cash account variances that ensued after the implementation of a new point of sale and system conversion and identifying causes and resolving balancing issues related to cash between the banks and the retailer’s systems.

Results

As a result of these efforts, the retailer has an evolved corporate planning organization with a strong foundation for data-driven decision making and self-service analytics. This foundation includes reduced time spent by employees to manually manipulate and report data; new management reporting capabilities for KPI data and strategic initiatives; and the enablement of business partner access to internal data in both raw formats from the data warehouse and in dashboard format.

The company was able to improve its forecasting accuracy by approximately 3% and now reforecasts on a weekly basis using available transaction data. Ultimately, as a result of the improved forecast accuracy and the speed gained through automation, stakeholders have access to more timely data, which has led to more timely and effective decision making across the company.