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The usability of Point-of-Sale data

Point-of-Sale ("POS") data is one of the most important pieces of information that large companies analyse when determining which products sell and therefore which products they should produce, or offer, more or less of. Usage of this data is not new and has been common for a very long time, but it is only recently that the accessibility of tools to analyse POS data has become widely available, making it usable without technical knowledge. In this post we want to explore how smaller companies can also benefit from understanding the data that their sales are generating and how to use it to their advantage. But first, we should clarify what POS data is, when it is generated and what it contains. POS data is captured at the time and place where a retail transaction is completed, which – in physical stores – is the check-out counter. In the context of e-commerce, this is the time when you click “buy” and receive an order confirmation. These are some examples of what data is collected:

  • Details of the product sold

  • Stock levels

  • Other items purchased at the same time

  • Browsing history leading up to the purchase

Depending on the type of products being sold, the importance of each of the data points varies. For example, Amazon is very interested in knowing exactly what a person was doing before they purchased highly individualised items such as a particular book or movie (browsing history, search terms, ads displayed etc.) so they can look for particular triggers. Compare that with supermarkets, who are less interested in the triggers for something common like milk purchases, but much more interested in broader trends such as seasonality factors etc. Larger supermarkets, such as Carrefour, also make POS data available to their supply chain in order to make it more collaborative and allow upstream prioritisation.

While this type of data is widely utilised by larger companies such as Amazon or supermarkets, smaller companies can also leverage POS data to understand their sales. For instance, a retailer could try placing a product with a higher profit margin closer to a highly populated area of the store to see if it increases sales. They could measure this precisely by analysing the POS data and benchmarking the sales before and after moving the product, although in practice most small retailers do this intuitively. However using the more detailed POS data can provide even deeper insights to understand your sales, such as which products are usually sold together. Everybody knows that oranges and certain spices are in demand during winter, but there are many more patterns that are not as obvious, such as which chocolate bar is most in-demand on Mondays.

The more complex part of using your POS data is actually analysing it in a meaningful way and whether you need tools, or other services, in order to understand and draw conclusions from your POS data. This will be the topic of a separate blog post in the near future - so please stay tuned! If you have any questions about how you could possibly use your own POS data, or how you have used your POS data already, feel free to drop us a message at info@yoba.com. We would love to hear your story! Read more about POS data here: Bigcommerce introduction to POS data Wikipedia article of Point of Sale Carrefour POS data page

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