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“People who bought this item also purchased this…”

If you have shopped online, you will more than likely recognise the sentence above. Right from the early days of e-commerce, data has been used to make checkout recommendations.

The analysis was simple; sift through the sales data to identify patterns between purchases. If you know that people who bought item A also bought item B, then offer item B at the checkout every time someone purchases item A.

This has now evolved into a very sophisticated system. Recommendations for additional purchases can be derived using increasingly more data points. For example; location, weather, sports results and recent travel, just to name a few, can all be pulled together to make far more personalised point-of-purchase recommendations.

Even more interestingly, with e-wallets and connected devices, these recommendations no longer have to be limited to online checkouts. We can even apply the data to checkouts in physical retail locations.

The power of personalised purchasing recommendations brings the currency of data into sharp perspective.

Personalised insights are valuable, but timing is everything.

At the point just before a purchase, time-specific personal data, which contributes to making personalised recommendations, is highly valuable. The moment the transaction is complete, or when the client exits their online shopping cart, some of that data immediately loses its value.

If we look at physical retail locations, data that monitors where a customer has moved around your store can be incredibly valuable in making an “in the moment” recommendation. Once customers leave the shop, that specific data may have no ongoing value whatsoever.

Understanding the currency of the data you collect helps you understand the data management infrastructure you put in place.

To be capable of pulling such varied data from many different places, analysing it and feeding the results back to the point of purchase, in time to influence an upsell, is a significant challenge.

Companies like Cloudera are helping businesses with this problem.

Cloudera’s enterprise data platform can combine vast amounts of static data, such as that from a data warehouse or data lake, with real time streaming data from connected devices or social feeds, process it centrally, and provide insights back to the edge.

More importantly, Cloudera deploys technologies that enable all of this to happen whilst the currency of the data is still high – before a checkout session is complete.

Building systems that manage the currency of data may just allow you to switch from recommendations like “People who bought this item also purchased this…”  to “Based on how you are feeling today, we would like to offer you…”

Data currency fluctuates, but unlike monetary currency, the fluctuations are more predictable. If you know that in-the-moment data can impact revenue and bottom line, then investing in a data platform that allows you to meet these demands is something your business should seriously consider.

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