Analysis of Regional Store and Product Quadrant

  • Last update:June 20, 2024
  • Overview

    This document further analyzes data on regions, stores and products.

    Example

    Regional Gross Margin Analysis

    You can view the gross margin according to the map.

    1. Drag the field City into the field Province.

    2. Click the f2e8d847d71c999699411e7f5710109.png icon next to the field Province and choose Geographic Role > Province/City/Autonomous Region from the drop-down list. Click the f2e8d847d71c999699411e7f5710109.png icon next to the field City and choose Geographic Role > City from the drop-down list.

    3. Set Chart Type to Area Map.

    4. Drag the longitude field into Horizontal Axis and the latitude field into Vertical Axis to generate a map.

    For details, see Area Map.

    2.1.gif

    You can set map component styles as needed.

    1. Drag the field Gross Margin into Color in Graphic Property and click Color to set the gross margin value to different colors for differentiation.

    2. Drag the fields Province and Gross Margin into Label in Graphic Property and click Label to set Label Display to Max/Min.

    图片1.png

    According to the previous document, anomalous data exists in August. Therefore, you can filter the August data in the result filter for analysis.

    1bb5b1ff8d9ab22728afbc151daa30e.png

    Exploration

    The gross margin in Hunan Province in August is the lowest in the country, at only 0.65%.

    If you drill down the gross margin data from the province level to the city level, you will find that Changsha City in Hunan Province shows a negative value.

    图片2.png

    Store Gross Margin Analysis

    Based on the above analysis, you can filter Changsha City data to identify the cause further.

    You can create the Store Gross Margin Analysis component by copying the Regional Gross Margin Analysis component, retaining its original filter conditions, and adding another filter condition for Changsha City.

    2.3.gif

    Product Quadrant Analysis

    Based on the above analysis, you can filter the Changsha Meixihu Store data to identify the cause further.

    1. Product type quadrant analysis

    2.4.gif

    2. Product name quadrant analysis

    Based on the product type quadrant analysis, you will find that anomalous data exists in the Snack type.

    You can directly copy the above component, filter the data for Product Type as Snack, and replace the indicator field Product Type with Product Name.

    Subsequent Operation

    After following the steps in this document, you can go to the next document Abnormal Order Monitoring for further learning.

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