The total online retail spend for Black Friday in 2016 totalled a huge £1.23 billion, 12.2 percent up on the previous year. But how many of these rushed, one-day-only purchases are actually kept by the customer and how many are inevitably returned?

And is this simply lost profit or is there still value to be gained? In an industry driven by intuition, at what point are retailers introducing the facts in order to learn from the sale and return trends of discount days? Is it to steer strategy for the next day? The next season? Is it at all?

So many questions.

When it comes to predictive analytics in the retail sector, how do businesses know what and how much their customers are purchasing?

With an estimated total cost of returns sitting at around 20 percent of what businesses are selling as a whole, when it comes to the key shopping calendar markers such as Black Friday and Cyber Monday, retailers are struggling to cope with the sheer demand for products and the corresponding returns process that comes with it.

Currently, this all too common scenario comes with an all too common result – piles and piles of returned goods being sold off by the pallet to the highest bidder because they don’t ever make it back to the shop floor once returned to the warehouse.

This results in a huge loss of revenue for those businesses. The cavernous gap between what retailers are selling and what customers are then later returning is enormous and it often takes businesses months to realise that a product that may have sold 100 units may have also had all 100 returned. Retailers must be able to get this data back within a matter of days to allow them to swap out stock or move it instore and online accordingly.

Shopping days such as Black Friday look great on the surface, but retailers are papering over the cracks from back of house. As the time comes for organisations to gain more insight into returns on these peak trading times to create a more joined-up culture, there often incurs heavy reluctance when it comes to technology versus intuition from the retail sector.

By using a solution that can cost-effectively and efficiently track, monitor, and pre-empt stock movements and returns to better optimise business operations in back of house, retailers will gain extensive insight into their stock and on the shelf product trends.

The key to unlocking the returns door is the data element. Although retailers operate between numerous complex operational systems, by consolidating all the data collected from those systems currently in place, retailers can obtain a single clear view of all their products and their corresponding trends.

Analytical systems can create this singular view, so retailers can quickly and easily detect issues within the returns process and adapt accordingly, such as stocking less of those items that have suffered more frequent returns and visa versa.

It’s clear that a problem still lies within the returns process for the retail sector and it can be rectified through cultural change; retailers need to align their returns process to the rest of the business and complement their intuition with the hard facts if they’re to gain value from their returns that can influence future sales.

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About The Author

Chairman of Zizo

With over 40 years’ broad experience in the IT industry, including working with some of the biggest data technologies such as Oracle, IBM and Ingres Peter is the founder of Zizo Software. With a keen interest in Cloud analytic technologies, Peter understood the move to Cloud analytics was underway, and assembled a team to create a new type of technology, suited to deliver Big Data analytics and pattern database services at scale in the cloud. That technology is Zizo.