In the e-commerce sector, analysing customer data is an important component for the purpose of optimizing sales figures. Alongside age, sex, and interests, the geographic location of the target group, can also be an important criterion. In order to be able to track in what land the most sales are made, or in which region non-payments arise the most, creating a visually appealing map in Tableau is the best option.
There are many good reasons for depicting data in the form of a map. For example, you can identify hotspots at first glance, or can see that there are hardly any data in a particular region. These are all good reasons for creating a map visualization. Don’t forget, however, that maps, like any other type of visualization, serve a specific purpose: they answer spatial questions
But what is a spatial question? Here are some examples:
- Which federal state do you receive the most orders from?
- In which regions is the non-payment rate the highest?
- Where are cancellations made particularly often?
- In what city do you have the highest sales?
- From which warehouse are your products delivered to which regions?
This explains what the Tableau map is supposed to show. But what types of use are there? Here’s a brief outline of our two favourites:
With Tableau maps, regional data can be analysed in a visually appealing way. But the map in Tableau is not always the best way to receive informative data through visualization.
Answering the question as to which federal state is the source of the most orders, the map gives a visually appealing analysis and also provides a first impression of the regions in which orders are frequently mad. But it doesn’t allow you to identify which state is the one with the most orders, as the visual difference between the individual states is too small.
Instead of this, these values could alternatively or additionally be depicted using a bar chart, allowing you to identify precisely the federal state from which most of the orders come.
This example shows that data can be recorded quickly using maps, but that there are some cases in which another type of visualization will provide a better answer to a spatial question.
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