---
description: >-
  This application calculates customer scores based on historical sales data,
  taking into account various customer actions like completed purchases,
  returns, refunds, and resends.
---

# How Customer Score Calculation Works

## Step-by-Step Explanation

### 1. Order Retrieval

When calculating the customer's score, the system gathers all historical orders linked to the customer’s postal code. It skips customers who have only unprocessed (open) orders, as they don't yet provide meaningful insights.

### 2. Order Events Considered

Each order can have multiple events or statuses, including:

* Processed (completed) – Positively affects the score.
* Returned – Negatively affects the score.
* Refunded – Negatively affects the score.
* Resent – Negatively affects the score.
* Open (pending) – Not included in the final calculation.

### 3. Reducing the Influence of Older Orders (Decay)

Recent customer activity is more valuable in assessing their current trustworthiness. The app reduces the impact of older orders gradually. The older an order gets, the less influence it has on the customer's current score.

For example:

* Orders placed recently (e.g., in the past few weeks) significantly impact the score.
* Orders from several months or years ago have a smaller influence.

This ensures the customer’s current behavior is most influential, providing a fairer and more relevant score.

### 4. Calculating Scores Based on Order Events

The system calculates scores separately for each event type:

* Processed Score – Total value of completed orders (positive impact).
* Returns Score – Total value of items returned (negative impact).
* Refunds Score – Total value refunded to customers (negative impact).
* Resends Score – Total value of orders resent (negative impact).

Each score considers the age of orders, meaning newer events contribute more to the final customer score than older events.

### 5. Adjusting Impact With User-Defined Weights

You can control how negatively different customer behaviors affect their score. In your app settings, you set the "weight" or significance of each negative action (returns, refunds, resends).

* Setting a low weight (e.g., 10%) means the behavior has only a slight negative impact.
* Setting a high weight (e.g., 90%) greatly increases the negative impact on the customer’s score.

### 6. Final Customer Score Calculation

To calculate a customer's final score, the system takes the total positive value of recent processed orders and reduces it by the adjusted negative impacts from returns, refunds, and resends.

This means:

* Customers with mostly positive recent actions will score high.
* Customers who frequently request refunds or return items will score lower.

### 7. Customer Score as a Percentage

The final score is presented as a percentage (0%–100%), which is easy to understand:

* 100% indicates consistently excellent customer behavior.
* 0% indicates problematic behavior (frequent refunds, returns, etc.).

### 8. Customer Score Rating Categories

The percentage score is translated into clear ratings for easy interpretation:

| Score Range | Rating  | Meaning                                        |
| ----------- | ------- | ---------------------------------------------- |
| 0–25%       | Bad     | High frequency of negative customer behavior.  |
| 26–50%      | Mid     | Moderate concerns with some negative behavior. |
| 51–75%      | Good    | Generally positive customer behavior.          |
| 76–100%     | Great   | Excellent and trustworthy behavior.            |
| No data     | Unknown | Insufficient data to assess behavior.          |

If there's not enough historical data, the app marks the customer as "Unknown."
