How to safely monetize POS data in a few practical steps

In the previous article, we addressed the opportunities and challenges faced by POS providers in turning the data into something valuable. Now we will look at some practical steps to extract the value and how SharpGrid can help. And the first one has to be around the question:

“Can I actually do something with the data?”

What is in the POS data and what makes it sensitive?

The data from the POS system usually contains the following categories (non-exhaustive):

  • Merchant information - identification of the entity and/or place that the transactions come from
  • Product information - name, number and value of products customers paid for
  • Timestamp - when did the transaction happen
  • Other information - VAT, currency, various IDs (e.g. product, product category, merchant)

The most sensitive part here is the connection between the Merchant information and their business transactions. This allows one to see their detailed revenue performance. We strongly believe that such information should remain in the full ownership of a Merchant. And it is only their decision if and how they want to share the data.

The good news is that the POS data can be easily anonymized e. g. by removing the merchant information and replacing it with an anonymous ID and categorization based on a geo area or merchant segment. This leaves us with a dataset with no way to connect data to any specific merchant.

So what can a POS software provider do with POS data?

Not a simple question.

The most straightforward solution is to have a clause in the T&Cs between POS providers and their customers - merchants. This clause would state that the anonymized data can be used for analysis or reporting & even passed to 3rd parties to incorporate in market research, industry studies etc. 

Many modern POS systems have such clauses and use the data to the benefit of their customers (benchmarks or insights to help their customers make decisions) or sell the data to 3rd parties to build industry reports or market measurement tools.

It’s always possible to add such aclause to T&Cs. It usually works only if the POS provider has a good level of understanding of the potential value of the data and the will to go through the process of changing T&Cs. While it might feel somewhat sensitive, it is becoming a norm and if the use & anonymization is clearly and transparently explained, there should be little resistance to it.

Even in the absence of such a clause - and in fact, this is the case in most T&Cs as of now - one can work with the concept of derived data.

What is derived data?

The definition of “derived data” from the OECD glossary of statistical terms is the following:

“A derived data element is a data element derived from other data elements using a mathematical, logical, or other type of transformation, e.g. arithmetic formula, composition, aggregation.”

POS data is a great candidate for such a derived dataset, which completely removes the sensitive part of the raw POS data by delinking the data and the vendor (anonymisation) and also by aggregating the data in time, region or vendor category.

Such a dataset still allows us to understand trends, consumption/revenue structure, demand for specific products and pricing. It can be used internally or shared with 3rd parties for an agreed price or in exchange for a revenue share of further derived work done and sold by a 3rd party such as SharpGrid.

What next?

Before the POS data becomes useful it needs some proper cleanup. It is usually pretty messy - the same product items are written in several different ways, products are not categorised, duplicated items, missing values etc. We will address this challenge in the next article in our sequence.

If you are interested to learn more about this topic, please get in touch. We can help with thinking through some of the use cases, we can help monetize the data and we can help create the derived dataset and make it useful.



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