Big Data in Retail — Faster than airmail [2 Astonishing Case Studies]
See How Top Retail Companies of the World are benefitted by Big Data
INTRODUCTION
Big Data has already impacted different domains quite significantly and hence gained special attention across the globe. Big Data has its footprints in almost all the top organizations of the world. In the same way, Retail Industry has been also got influenced by this technology which in turn considered now as a paradigm shift.
The results the retail industry has observed through Big Data analytics are no less than a revolution. The retail companies are now more and more diverted towards Big Data. And why not, Big Data is providing them with solutions that were once beyond anyone’s expectations.
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BIG DATA IN RETAIL
The retail industry is hugely based on the customer’s behavior, reviews, decisions, and understanding. Big Data can help retailers to understand their customers by tracking their transactions, browsing behavior, preference for specific products, shopping trends, and social media habits.
With this accumulated data the retailers can provide their customers in a more personalized way via targeted advertising, product recommendations, and pricing, instead of showing the random recommendation which customer is not interested in.
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Companies like Facebook, Google keeps track of all the data of the users, who are connected over the internet. Based on the user’s browsing pattern the ads are placed on the pages they visit subsequently.
CASE STUDIES
Unilever — The undisputed winner
Unilever is one of the world’s largest Consumer Packaged Goods (CPG) companies. With a business scale of 1 million+ outlets spread across 35+ product categories, 400+ brands, and 1100+ products, its products are available across 190 countries around the globe.
Unilever’s existing Decision Support System (DSS) comprised an on-premise analytics platform based on Machine Learning and advanced predictive analytics. The platform churned nearly 500 million recommendations every month, computing around a billion records amounting to 6–8 TB of data. In order to drive competitive advantage, they needed to significantly increase the performance of its DSS.
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The DSS required high lead-time for generating Key Performance Indicators (KPIs) and building statistical models. As a result, Unilever was unable to leverage the latest market information for business insights. The existing platform was also not able to scale up to accommodate new-age data sources for generating deeper insights.
Unilever needed a DSS based on a comprehensive data lake―with data from both traditional and new age sources―to answer ad-hoc business questions, accurately. Not only this, but the DSS would also have to perform significantly better to drive competitive advantage
Unilever migrated the existing on-premise Decision Support System to AWS Cloud. As a result, Unilever was able to reduce the lead-time required for business insight generation from 20 days to less than 10 hours for 4–6 TB of data, thereby significantly increasing business agility.
The cloud-based Big Data Analytics platform (DSS on Cloud) leveraged on-demand scalability and massively parallel processing capabilities. This involved re-platforming and re-designing the entire process from data gathering to analytics and insights generation.
To achieve this, Unilever built an entirely new architecture using Bigdata, AWS, AWS Redshift and Spark technologies with Machine Learning capabilities.
Walmart — Playing it smart
With over 20,000 stores in 28 countries, Walmart is the largest retailer in the world. So, it’s fitting then that the company is in the process of building the world’s largest private cloud, big enough to cope with 2.5 petabytes of data every hour.
Walmart relies on Big Data to get a real-time view of the workflow in the pharmacy, distribution centers and throughout our stores and e-commerce.
Walmart has a broad Big Data ecosystem. The Big Data ecosystem at Walmart processes multiple terabytes of new data and petabytes of historical data every day. The analysis covers millions of products and 100’s of millions of customers from different sources.
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The analytics systems at Walmart analyze close to 100 million keywords on a daily basis to optimize the bidding of each keyword. The main objective of leveraging big data at Walmart is to optimize the shopping experience for customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion. Big Data solutions at Walmart are developed with the intent of redesigning global websites.
The Walmart Data Café allows huge volumes of internal and external data, including 40 petabytes of recent transactional data, to be rapidly modeled, manipulated and visualized.
Quick access to insights is vital. The grocery team could not understand why sales had suddenly are declined in a particular product category. By using Big data analytics, they were quickly able to see those pricing miscalculations had led to the products being listed at a higher price than they should have been.
The system also provides automated alerts, so, when particular metrics fall below a set threshold in any department, the relevant team is alerted so that they can find a fast solution.
In one example of this, during Halloween, sales analysts were able to see in real-time that, although a particular novelty cookie was very popular in most stores, it wasn’t selling at all in two stores. The alert prompted a quick investigation, which showed that, due to a simple stocking oversight, the cookies hadn’t been put on the shelves. The store was then able to rectify the situation immediately.
Walmart has 200 billion rows of transactional data (representing only the past few weeks!), the Café pulls in information from 200 sources, including meteorological data, economic data, Nielsen data, telecom data, social media data, gas prices, and local events databases. Anything within these vast and varied datasets could hold the key to the solution to a particular problem, and Walmart’s algorithms are designed to blaze through them in microseconds to come up with real-time solutions.
Clearly, Walmart has huge amounts of data at its fingertips — and the resources to tackle all that data. But what any company can borrow from Walmart’s example is their ability to react to data quickly. After all, there’s little point investing in data capabilities if your internal setup doesn’t allow you to quickly make decisions and changes based on what the data is telling you.
