How retailers are using unstructured data to predict summer demands

Retail is a continuously shifting space, where not only the products we buy, but the way we choose to shop is rapidly changing. The ‘future of retail’ is finally upon us – and whether it’s through careful omni-channel strategies or by drone-delivered parcels, agility is more important than ever before.

The shop floor is a battleground, where innovators are thriving. Retailers need to be responsive to customer demand, and where possible, stay ahead of the game to predict shopping patterns.

With the warmer weather, and Wimbledon well underway, purchasing departments across the country are preparing for an avalanche of strawberries and sun cream. However, what happens when demand changes suddenly? If the weather turns and the tennis is a washout, how do retailers ensure that supply doesn’t disappoint customer demand?

> See also: Why omnichannel retail is more than just a buzzword

Predicting the demand for consumer goods is a challenge as it requires vast amounts of information in order to be successful. Usually it is based on measurable information including sales records, trends, holidays and of course the Great British weather. All of these factors play a huge role in keeping customers happy, whilst also impacting the bottom line. As a result, the race is on for retailers to obtain and analyse as much data as possible.

As this crucial data is gathered from a number of sources it is largely unstructured. Unstructured data doesn't have a pre-defined data model and in turn it allows businesses to expand their knowledge of consumers.

Instead of only asking simple 'yes' or 'no' questions, unstructured data gives businesses access to information that asks 'why?' and even 'where?' However, due to their rigid schemas, relational databases cannot easily accommodate unstructured data.

Therefore, savvy retailers choose an unstructured database to store a wide variety of data. For example, major EU retailers are using non-relational databases to power their product catalogues. As a result, their databases can handle a diverse range of fast-changing data including product specs, photos, reviews and ordering/availability information.

The huge mass of data used by retailers to buy and sell stock requires agile database architecture, capable of processing terabytes of information to make informed decisions. Retailers are increasingly relying on non-relational technology, capable to manage content, process real time purchasing potato magic and increasingly support mobile applications.

> See also: What the retail sector can learn from London Fashion Week's tech innovation

Mobile apps already have a central role in retail. Not only do retailers need to successfully develop applications that are easy and secure to use, but these apps need to accurately sync to the existing infrastructure to ensure orders are fulfilled seamlessly.

As shoppers become increasingly expectant of a fast and reliable service, speed becomes a key factor in the buying process – from app performance through to the delivery of goods.

To stay ahead of the game retailers need to be responsive to customer demand. Predictive analysis is the key to this success, and an unstructured database is its foundation.

Sourced from David Maitland, general manager EMEA, Couchbase

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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Unstructured Data