New User Dish/Product Repurchase Rate

I. Calculator Introduction

The New User Dish (Product) Repurchase Rate calculator is used to analyze the repurchase performance of dishes or products that new users purchased on their registration day, within 30 days, 60 days, and 180 days after registration. This tool groups by "first-day dish/product", calculates total users, repurchase users, and repurchase rate for each group, and displays the results for the three time windows in one merged table with multi-level headers, making it easy to compare the repurchase potential of different dishes horizontally.

Core Features

• 30-day / 60-day / 180-day repurchase: Only users who have been registered for at least N days are included; their orders within N days after registration (including the registration day) are counted; repurchase rate is calculated by grouping by first-day dishes/products purchased.
• Merged report: Merged into one table by dish/product name, with multi-level headers distinguishing the three groups of metrics for 30 days, 60 days, and 180 days (repurchase users, total users, repurchase rate).
• Sorting: The report is sorted by 30-day total users in descending order, so that dishes with larger volume are viewed first.

Application Scenarios

• Identify which first-day dishes/products better drive new user repurchase, for menu or assortment optimization.
• Compare repurchase rate changes for the same dish across 30-day, 60-day, and 180-day windows, to observe long-term repurchase performance.
• Combine total user scale to prioritize operation or supply chain investment in dishes that have "sufficient volume and outstanding repurchase rate".

Applicable Customers

This calculator is suitable for businesses that have "user + order detail (including dish/product name) + registration date" data, such as catering, retail, e-commerce, etc., and wish to analyze new user repurchase performance from the dimension of first-day purchase category.

Prerequisites: The data can provide user ID, order date, registration date, and dish/product name; and new users have at least one order on the registration day (this tool only counts new users with at least one transaction on the registration day).

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II. Algorithm Introduction

2.1 Core Concepts

First-day dish/product

For each user, the set of dish or product names (deduplicated) purchased on the registration day is called that user's "first-day dish/product". Each row in the report corresponds to one first-day dish/product, and the statistics are the repurchase performance of "new users who purchased that dish/product on the registration day".

Order days and repurchase

Within a given time window (e.g. 30 days), for each user the number of distinct order dates is counted; this is called that user's "order days" in that window.

• Repurchase users: Users with order days ≥ 2 (i.e. at least 2 distinct order dates within that window).
• Repurchase rate: Repurchase users / total users for that dish in that window.

Time window and "registered for at least N days"

• 30 days: Only users with "registration date + 30 days ≤ maximum date in the data", i.e. who have been registered for at least 30 days; only their orders within 30 days after registration (including the registration day) are counted.
• 60 days / 180 days: Similarly, registration for at least 60 days and 180 days respectively is required, and only orders within 60 days and 180 days after registration (including the registration day) are counted.

2.2 Calculation Logic

1. Reference date: The maximum order date in the full dataset is taken as the analysis cutoff date.
2. User filter: For each window (30/60/180 days), keep only users with "registration date + N days ≤ reference date" and "at least one order on the registration day".
3. Orders within window: For each user, keep only order lines whose order date falls within [registration day, registration day + N − 1 days].
4. First-day dishes: For each user, mark the dish/product names purchased on the registration day.
5. Aggregation by dish: For each (first-day dish, user), compute that user's order days within the window; then aggregate by first-day dish to get total users and repurchase users, and thus repurchase rate.
6. Merge and sort: Merge the 30-day, 60-day, and 180-day results by dish/product name into one table, sorted by 30-day total users in descending order.

2.3 Result Display

The report is one merged table with a two-level header:

• First row: 30 days, 60 days, 180 days (each group occupies 3 columns).
• Second row: Dish/product; under each group: "Repurchase users", "Total users", "Repurchase rate".

Each row corresponds to one first-day dish/product; the columns are its repurchase users, total users, and repurchase rate (as percentage) in the 30-day, 60-day, and 180-day windows.

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III. Usage and Notes

3.1 Data Preparation

Required fields

Ensure your data file contains the following four columns:

1. User ID column: A column that uniquely identifies users (e.g. member ID, phone number).
2. Order date column: The date the order occurred, in formats such as 2025-01-01 or 2025/1/1.
3. Registration date column: The date that user registered, used to determine whether they are a new customer and to compute the repurchase window.
4. Dish/product name column: The column that specifies the dish or product name (e.g. Kung Pao Chicken, a certain SKU name).

Data granularity: Each row should ideally be one order line (one record of a user purchasing a certain dish/product on a certain date).

3.2 Field mapping

After uploading the file, map your data columns to: User ID column, Order date column, Registration date column, Dish/product name column.

3.3 Interpreting results

• Total users: In the corresponding time window, the total number of new users who purchased that dish/product on their first day (users who had an order on the registration day and are included in the window statistics).
• Repurchase users: Among the above users, the number of users with order days ≥ 2 within that window.
• Repurchase rate: Repurchase users / total users; reflects the proportion of new users brought by that first-day dish/product who repurchased within the window.

3.4 Notes

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IV. Summary

This calculator analyzes new user repurchase from the dimension of "first-day purchase dish/product", outputs a merged table of 30/60/180-day metrics, and helps identify which dishes better drive repurchase. When interpreting, please pay attention to total user volume and only compare repurchase rates and make decisions for dishes with sufficient volume.