Big Data + Swiss Efficiency = Great Shopping Experience!
The Federation of Migros Cooperative, the largest retailer and employer in Switzerland, was honored with a prestigious Swiss Logistics Award for determining the best transport routes for its goods. Guess how it did that? By studying the behavior of ants!
In my previous post, I talked about how the Grand Bazaar in Istanbul, one of the world’s first shopping malls, was a surprising case study in the use of Big Data. Over the decades, the rug merchants have learned how to ask questions that provide unexpected insights into their customers.
This type of pattern recognition is at the heart of Big Data, and modern retailers are using the same approach to hone inventories and market their wares.
Take Globus, the premier department store subsidiary (pictured) of the Migros Groups. It operates 37 retail stores that sell clothing, cosmetics, jewelry, household supplies and other upscale goods. (Which, ironically, is similar to the fine antiques sold in high domed hall of the Cevahir Bedesten at the Grand Bazaar.)
Globus depends on timely, accurate reporting and analysis of sales performance data to determine consumer trends so it can change its pricing and marketing strategies as the market changes. The retailer offers some 800,000 products through a network of some 3,500 suppliers.
To get quicker insights from its large and fast-changing data, Globus uses SAP HANA platform. This allowed operational data to be captured in-memory as transactions occur, so analytics could be performed against rich data sources and uncover insights in real time.
As I explained in my last post, this is what the rug merchants at the Grand Bazaar were doing when they asked questions like, “What hotel you are staying at!?” or “How do you like that camera?”
The rug merchant could apply the information, which suggests the taste of the customer, in real time. And now, so can Globus because of technology advances in Big Data.
“Previously, it took 22 minutes to generate a basic slow-seller report that did not include the whole product line,” says Alexander Weiss, team leader, processes and business warehouse at the company. “Access to these reports had to be planned in advance because of the processing it.”
After implementing the in-memory database system, Globus can generate a slow-seller report for the entire product line in just 17 seconds. Sales promotion reports, which once took seven minutes to complete, can be produced in just 60 seconds. As a result, Globus can improve its point of sale analytics and the management of slow-selling items.
At its core, this simply means being more knowledgeable about customers to serve them better. And a new report from Accenture shows that’s precisely what retail customers want today:
- More than four in 10 respondents would like retailers to notify them if an item that’s been scanned at the point of purchase is something they should not buy, based on intimate knowledge of them, such as their dietary or religious restrictions.
- More than half of respondents (55%) desire unique pricing, automatic discounts, free returns, or pre-sales based on their loyalty/purchase history.
- Roughly 63 percent want retailers to suggest complementary products based on items they’ve already scanned into their shopping cart.
Source : “Accenture Seamless Retail Study,” April 2013
In order to accomplish this, retailers need to be able to quickly understand more information about customers at every step – from the moment they walk in the door until after they’ve made a purchase.
In a recent report by Businessweek Research Services, Paula Rosenblum, managing partner of the consultancy Retail Systems Research, notes “Discovery, education, research, payment and delivery can happen in all channels. You need real-time analytics to mash up the data and discern the patterns in them.”
The rug merchants at the Grand Bazaar have been doing this for centuries. And modern merchants can use Big Data in the exact same way.
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