Are analytics in big data a necessity for retailers?


Asking the right questions from your customer unlocks significant business value and insight. Using analytics, that information can help a company to delve further into shopping trends.

Writing about his experiences at Istanbul’s Grand Bazaar which receives up to 400,000 visitors each day, Reza Soudagar from SAP writes that questions like: “Where are you visiting from?” and “How long are you staying in our city?” may appear like casual conversation starters but are in fact capitalising on ‘data’ allowing merchants to decipher what type of people buy which type of product.

What use is there for analytics?

The ability to pick up on body language and other visual and verbal cues like age, clothing, and choice of dress allows merchants to gain insight about a total stranger in a matter of minutes. This is real-time analytics.

While big retailers have used big data for many years now, the present climate presents a unique set of challenges and is altogether different from the past given how the sheet volume of data in recent years has sky-rocketed.

Retailers are now increasingly turning to real time reporting and analysis of sales performance data to determine consumer trends, so they can better target pricing and marketing strategies to reflect consumer behaviour. Aggregating and mining online data as well transactional and spatial data from thousands of data points – and then turning all this into actionable insights is a complex process.

Successfully rolling out big data analytics technology depends heavily on executive involvement, and on implementers knowing what insights they need from their data in order to put in place the right solution. Only then can retailers truly begin to decipher how their consumers make purchase decisions.

A recent example of big data analytics in action is the announcement by retail giant Tesco to install face-scanning technology across 450 of its petrol stations to target advertisements to individual customers at a till. The face recognition technology uses a camera to identify a customer’s gender and age, before it tailors an advert to that individual’s demographic.

Aside from video analytics, other retailers are deploying sensors in retail and fast food outlets, where they use smartphone Wi-Fi signals to detect where consumers are. Such technology, its users say, is designed to identify trends in consumer behaviour, to be able to answer such questions as: “What percentage of my customers shop for high-end specialty items?” and “What aisle and product gets looked at the most?”

Targeting certain data can be coupled with useful analytics

The negative consequences of merging physical retail experience with big data is obvious – a lack of privacy. Campaigners say retailers should notify customers of the tracking technologies they use, while others say such measures are paled when you consider how predictive analytics have made it all too easy to piece together information about individuals that would otherwise not be possible.

Writing in the Guardian, John Naughton observes that routine big data analytical techniques “can now effectively manufacture personal data that is not protected by any of the measures we’ve used up to now.”

A prime example of this is the way the American retail chain ‘Target’ is able to collate pieces of data about individuals’ shopping habits to predict the delivery data of pregnant shoppers. An analyst at Target observes that:

“…sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.”

Why is this important? The NYT neatly surmises:

“If companies can identify pregnant shoppers, they can earn millions.”