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Case Study

Calculating retailer’s percent-of-sales rent payment for 1,000+ locations.

Home » Resources » Case Studies » Calculating retailer’s percent-of-sales rent payment for 1,000+ locations.

Results

90%

90% faster processing

8K+hrs

8k+ hours saved annually

Risk

Increased compliance

Company Profile

Specialty multi-brand footwear retailer (NYSE-listed), operates over a thousand retail stores plus e-commerce across the US, Canada, and Western Europe, with brands spanning teen-focused mall chains, premium men’s leather footwear, and licensed wholesale relationships with major apparel labels. Tens of thousands of employees.

Challenge

Time consuming monthly calculations

Retail real estate has unique lease concepts such as “Percentage Rent,” where tenants’ monthly rent includes a percentage of sales payment. The lease management team manually verifies monthly percent rent payments for each of their 1,000+ locations. This process required team members to retrieve sales information and rental agreements from their leasing admin portal, perform calculations, enter the data onto the correct template, then submit the output and system screenshots to auditors. The process was unnecessarily complex and required a dedicated team for one week each month.

Leadership wanted to streamline this process and reduce resource requirements so employees could be redirected to more valuable activities.

Solution

ReportBotz+ prebuilt solution

WonderBotz ReportBotz+ prebuilt solution retrieves all required sales and rental information from the database and employs user-managed business rules to select and complete the necessary work for each location. Instead of screenshots, the solution updates the location workbook with data retrieved directly from the database, using system logs as evidence. It performs calculations, updates the template, provides a link to the completed workbook, and alerts auditors to review any exception cases manually.

The solution completed the percentage rent calculation process 90% faster and with higher accuracy than before. It also saved the company over 8,000 hours per year.

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