Refund Analysis Report

The Refund Analysis report provides a detailed view of refund activity across your locations and ordering platforms. It helps you understand refund trends, identify locations and platforms with higher refund impact, and analyze the most common reasons for refunds. This report supports data-driven decisions aimed at improving order accuracy, reducing refund volume, and minimizing revenue loss.

How to Access the Refund Analysis Report

To view “Refund Analysis” Reports. Log in to the Checkmate Portal using your credentials. Once logged in, click "Refunds Management" from the navigation bar.

Under the "Refunds Management" section, click "Refund Analysis" to view the dashboard for an in-depth review of your refunds.

You can refine the report data by applying filters to the entire dashboard or to specific visual components. These filters allow you to focus on specific brands, locations, platforms, and time periods.

The following filters are available:

• Brand: Filters results by the selected brand name.
• Operating Platform: Filters data by the ordering platform used to place orders.
• Order Date: Filters data based on the date the order was placed.
• Location Name: Filters results for a specific store location.
• Store Type: Filters locations by business type, such as Brick and Mortar Establishment, Corporate, Franchise, or Independent.
• Primary Owner: Filters data based on the store’s primary owner.

Learn how to navigate through Checkmate Reports here.

Navigating the Report

The Refund Analysis report includes the following tabs:

• Refund Analysis Board
• Heat Maps

You can switch between these views by clicking on the corresponding tabs at the top of the report.

Refund Analysis Board Tab

The Refund Analysis Board provides a high-level view of refund trends and key drivers. It includes charts and tables designed to help you monitor refund performance over time, compare platform behavior, and identify locations and items with the greatest refund impact.

The following charts are included in this tab:

• Refund Percentage: This chart shows the trend of refund percentage over the selected time period. It represents the ratio of refunded revenue to total revenue and helps track how refund rates change day by day. Monitoring this trend can help identify spikes in refund activity and potential operational issues.

• Stores with Most Impact: This table highlights the locations that have the greatest overall impact on refunds during the selected time period. It focuses on both refund volume and refund value to help identify locations that may require further review. This table includes:
  • Location Name: This field shows the name of the restaurant location included in the report. It identifies the store to which the refund data is associated.
  • Average Refund Value (in $): This field shows the average dollar amount refunded per refund at the location. It is calculated by dividing the total refund amount by the number of refunds. This helps identify locations where refunds tend to be higher in value.
  • Number of Refunds: This field shows the total number of refunds issued by the location during the selected period. It helps quantify the frequency of refunds at each store.
  • Total Refund (in $): This field shows the total dollar amount refunded by the location. It represents the overall financial impact of refunds for that store.

• Order and Refund Distribution: This chart compares order distribution and refund distribution across ordering platforms. Order distribution represents the percentage of total orders placed through each platform, while refund distribution represents the percentage of total refunds attributed to each platform. Comparing these two values helps identify platforms where refunds may be disproportionately high relative to order volume.

• Revenue Loss Reasons: This chart shows the top reasons customers request refunds. It provides a categorical breakdown of refund causes, such as missing items, wrong orders, incorrect items, or ingredient issues. Understanding these reasons helps identify recurring issues that contribute to revenue loss.

• Revenue Loss Reasons Details: This table provides a detailed breakdown of refund activity by item and refund reason. When reviewed alongside the Revenue Loss Reasons chart, it helps identify which specific items contribute most to revenue loss. This table includes:
  • List of Items: This field shows the name of the item associated with the refund. It helps identify which menu items are most frequently involved in refund requests.
  • # of Refunds: This field shows the total number of refunds issued for the listed item during the selected period. It helps measure how often a specific item contributes to refund activity.
  • Refund Amount (in $): This field shows the total dollar amount refunded for the listed item. It represents the financial impact of refunds associated with that item.
  • Refund Reason: This field shows the reason provided for the refund, such as missing items or incorrect orders. It helps explain why refunds were issued and supports identifying recurring issues tied to specific items.

Heat Maps Tab

The Heat Maps tab provides time-based visualizations that show when refunds are most likely to occur. These views help identify patterns related to time of day and date.

This Tab includes:

• Total Refunds: This heat map shows the total number of refunds issued by hour across the selected date range. Each cell represents the count of refunds for a specific hour and day. This view helps identify time periods with higher refund activity.

• Refund Percentage: This heat map shows the percentage of refunds for each hour compared to the total refunds for the day. It highlights time periods where refunds make up a larger share of daily activity and helps identify operational patterns related to staffing, volume, or fulfillment timing.

• Refund Amount: This heat map shows the total dollar amount lost due to refunds during each hour of the day. It helps identify time periods with the highest financial impact from refunds, allowing teams to focus on reducing revenue loss during critical hours.