Shrinking Big Data Down to Size

Retailers have long been urged to employ Big Data analysis to uncover big insights into how their business is performing, how their brand resonates among consumers, and whether their customer experience meets expectations. But the new trend in Big Data is applying much more refined analysis at the granular level to uncover small insights that can combine to reveal even larger truths.

Several major Big Data analytics vendors have recently promoted their ability to perform analysis of granular segments of Big Data in near or actual real time, making statistical data cubes smaller and timelier, or even irrelevant altogether. Without delving into the particular merits or substance of the capabilities any Big Data vendor claims to provide, I’d like to offer three specific insights that granular, real-time Big Data analysis holds the potential to reveal.

Giving Retailers the Time of Day

Granular, real-time analysis of Big Data provides retailers with previously unavailable insight into their daypart performance. For most retailers, different customer demographics with different shopping goals and needs tend to visit at different times of day. While retailers may have a general sense of who is coming into the store when, Big Data can reveal precisely when specific customer groups are most likely to shop and exactly what they are looking for in terms of product, service, price, etc.

Armed with this highly detailed information, retailers can make highly targeted adjustments to staffing, product placement, marketing displays and even prices throughout the day to maximize customer satisfaction, conversion, market basket and loyalty.

Unskewing SKU Performance

It is one thing for a retailer to know that men’s jeans are generally selling well. It is another thing to know, near or in real time, that several specific SKUs in the men’s jeans category are selling extremely well while other SKUs in the category are languishing on the shelves. Individual SKU performance metrics lie within the volumes of Big Data collected by retailers, and granular analysis can give them access to these metrics in a much higher level of detail much more quickly than ever before.

This means retailers can adjust merchandise assortments and promotions on the fly, improving inventory throughput while avoiding the need for costly end-of-season markdowns on SKUs that did not sell to expectations. In addition to improving profits, timely SKU rationalization also improves customer experience, providing additional soft benefits.

Nobody Likes Being a Number

The Holy Grail of targeted omnichannel retailing is true “one-to-one” personalization, where the customer has a completely tailored experience that seamlessly travels with them across all touch points, in real time. No retailer has fully achieved this personalization goal to date, and it will likely be some time before any retailer does.

However, granular, real-time Big Data analysis can take retailers closer to providing this idealized customer experience. Nobody likes being a number, and the more detailed data a retailer can utilize in real time about an individual shopper, the less like a number they will feel.

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