Purchase Frequency Analysis

  • Last update:April 14, 2023
  • Overview

    Background

    It is easy to calculate the purchase frequency of each client with an order detail table.

    However, how to calculate the number of clients who have purchased products for many times? You can calculate the distribution of their purchase frequency to know the regular pattern of clients' purchase frequency within a certain time.

    Preview

    Clients' purchase frequency is normally distributed, with the maximum number of clients purchasing five to seven times.

     1.png

    Procedures

    Adding Data

    Sample data: 

    Supermarket Sales Data.xlsx

    Create a subject and upload the sample data.

     2.png

    Configuring a Component

    To calculate the number of clients in different purchase frequency, you need to set Purchase Frequency as a dimension and Number of Client as an indicator.

    Dimension - Purchase Frequency

    Add a calculation field and calculate the purchase frequency with a DEF function.

    Formula: DEF(COUNTD_AGG(Order ID),[Client Name])

     3.png

    Formula

    Description

    COUNTD_AGG(Order ID)

    Count the number of orders without duplicate values.

    DEF(COUNTD_AGG(Order ID),[Client Name])

    Group the number of orders without duplicate values according to Client Name and calculate the purchase frequency of each client.

    To calculate the number of clients in different purchase frequencies, you need to set Purchase Frequency as a dimension and Number of Client as an indicator.

    Transform Purchase Frequency into a dimension field.

     4.png

    Indicator - Number of Clients

    Create a calculation field Number of Clients and calculate the number of clients with a function.

     5.png

    Configuring a Column Chart

    Select Partitioned Column Chart and drag Purchase Frequency into Dimensions and Number of Clients into Indicators. Select Merge same items as a group as the group way of Purchase Frequency.

     6.png

    Demonstration

    See the section "Preview".


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    主题: Advanced Data Analysis
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