Big Data in Retail: Common Benefits and 7 Real-Life Examples

In an industry where brands face the challenge of e-commerce giants like Amazon, dynamic pricing, and the growing thrift shopping trend, retailers need all the help they can get to stay competitive. Big data has become so prevalent and accessible that more retail brands than ever are relying on data-driven insights to optimize pricing, streamline operations, and improve customer experience.

The first step to using big data to stay competitive? Understanding how big data is working in the retail industry and gleaning insights from how other brands have already put it into practice.

Big data in retail: An overview

To stay competitive, retailers make better buying decisions, must offer relevant discounts, convince customers to hop on new trends, and remember their customers’ birthdays—all while making the business run behind the scenes. How do they keep up? Big data in retail is essential to target and retain customers, streamline operations, optimize supply chain, improve business decisions, and ultimately, save money.

Before the cloud was readily available, companies were limited to tracking what a person bought and when. With more sophisticated technology, companies can capture a wealth of data about their customers, like their age, geographical location, gender, favorite restaurants, other stores they shop at, what books or news they read—the list goes on and on. Retailers have now turned to cloud-based big data solutions to aggregate and manage that data.

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So how exactly do these large data sets help retailers make critical business decisions?

4 big data benefits for retail

Big data analysis can predict emerging trends, target the right customer at the right time, decrease marketing costs, and increase the quality of customer service. Common benefits of using big data in retail include:

  1. Maintaining a 360-degree view of each customer — Create the kind of personal engagement that customers have come to expect by knowing each individual, at scale.
  2. Optimize pricing — Get the most value out of upcoming trends and know when, and how much, to decrease off-trend product prices.
  3. Streamline back-office operations — Imaging maintaining perfect stock levels throughout the year and gathering data from registered products in real-time.
  4. Enhanced customer service — Unlock the customer service data hiding in recorded calls, in-store security footage, and social media comment.

 

1. 360-degree view of the customer

The “360-degree view” term gets thrown around a lot, but what does that mean? It all boils down to a comprehensive picture of a customer that is as accurate as possible. Retailers need to know a customer’s likes and dislikes, their likelihood of using coupons, their gender, their location, their social media presence, etc.

Blending just a few of these data points can lead to sophisticated marketing strategies. For example, fashion retailers typically hire expensive celebrity brand ambassadors. But by paying attention to customer gender, likes, and social media presence, fashion brands can find more affordable and effective micro-influencers to represent their brands on Instagram

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2. Price optimization

Big data gives businesses an advantage when pricing products. Consistently monitoring relevant search words can enable companies to forecast trends before they happen. Retailers can prepare new products and can anticipate an effective dynamic pricing strategy.

Pricing can leverage the 360-degree view of the customer as well. This is because pricing is largely based on a customer’s geographical location and purchasing habits. Companies can run beta tests for segments of their customer population to see which pricing fits best. Understanding what a customer expects can inform the retailer of ways they can stand out against their competition.

 

3. Streamlined back office operations

Anyone who has worked in retail has experienced that sinking feeling when their stock is depleted. For the rest of their shift, that manager will be dealing with angry customers. Ideally, companies would eliminate this situation entirely. While that may not always be possible, big data can help companies manage supply chain and product distribution.

Product logs and server data can give retailers clues as to how their operations are running upstream. The products themselves can expose bugs, too. Customers that register their wearables, for example, can show the product performance over time.

 

4. Enhanced quality of service

Think about the last time you called a toll-free number. Usually, there is a warning that your call will be, “recorded for quality purposes.” Big data analysis can bring top issues from those recorded calls to light, and then measure the success of company-led quality changes over time.

Some retail companies scrutinize in-store video footage and motion sensors to improve customer experience. Retailers measure how often customers gravitate towards an area in the store, and strategically place items they want to sell first. This is not a new concept—grocery stores deliberately design their layout, causing you to come out with more food than intended.

There are insights waiting to be uncovered in customer reviews and comments as well. Analyzing these reviews can allow retailers to notify customers that particular garments may run small or large. “Sentiment analysis” can also be used to identify whether customers are talking positively or negatively about certain products and companies at large.

 

7 real-world examples of big data in retail

Now that we know big data is essential to maintain a competitive edge in retail, it’s important to understand how to leverage this information in the real-world. The following big-name retail companies use big data platforms to make decisions that drive revenue and boost customer satisfaction.

1. Aldo uses big data to survive Black Friday

Without a doubt, Black Friday and Cyber Monday are the most stressful days for retail businesses, and the most exciting days for consumers. In fact, the National Retail Federation estimates that sales in November and December are responsible for as much as 30% of retail annual sales.

Aldo is a shoe and accessory company based in Canada that uses big data to address this crazy time of year. The company operates on a service-oriented big data architecture, integrating multiple data sources involved in payment, billing, and fraud detection. This integration project enables Aldo to deliver a seamless ecommerce experience—even on Black Friday.

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2. Office Depot integrated offline and online big data

Office Depot Europe operates two brands (Office Depot and Viking) in 13 countries. As the leading office supply retailer, Office Depot Europe stays ahead by integrating online and offline efforts. That’s a lot of disparate data.

The organization uses a big data platform to link data from their offline catalog, online website, customer call centers, ERP s, and fulfillment systems. Office Depot Europe outcompetes other office supply companies by targeting particular customer segments and allocating internal spending to positively affect the productivity of various departments.

 

3. Groupon analyzes a terabyte of data per day

Groupon is an e-commerce site that connects subscribers to discounts on activities, travel, and other goods and services. To serve that range of customers, Groupon must process over one terabyte of raw data every day.

That dataset is too extensive to store and study without a big data platform. Groupon uses a major IT framework to import, integrate, transform, and analyze data in real time. Key stakeholders are able to run reports and visualize data from millions of customers in bite-sized formats.

 

4. Big data made PriceMinister flexible and agile

PriceMinister (now Rakuten) is a French retail group with a third-party pricing model. They put their customers in touch with sellers, and ensure that all transactions between the parties are successful by collecting massive data sets monitoring buyer and seller activity.

To alleviate strain on their IT department, PriceMinister adopted a big data platform to integrate buyer and seller datasets with an Oracle database containing all 100 million PriceMinister products. This technology enables PriceMinister to amplify their flexibility and reactivity. Updates to their buyer and seller information post almost immediately.

 

5. myWorld Solutions AG uses big data to augment Salesforce

myWorld Solutions AG is a shopping network that allows their customers to collect points and cashback on their purchases at more than 70,000 merchants in 47 countries. Managing all that data is a laborious undertaking.

The brand uses a big data platform with a Salesforce connector to merge, clean, and transform their customer and merchant data before deploying into Salesforce Sales and Marketing clouds. This integration allows myWorld Solutions AG to readily access customer information, track marketing performance, and course correct, if need be.

 

6. Disrupting the fashion industry with big data

Groupe Zannier (now Kidiliz Group) is a French retailer with a brand portfolio covering all segments of children and adult fashion. Some famous Zannier Group brands include: Kenzo, Levi’s, and Marc Jacobs.

To maintain their status as a fashion industry leader, Zannier Group needs to be an expert in the changing desires of children, teenagers, and adults in and outside of France. To do this, Zannier Group consolidates data from two major ERPs. With this integrated dataset, the business can distill retail activity into significant customer purchasing patterns to influence real-time sales and inventory decisions.

 

7. Big data bringing diverse business units together

Naville is a Swiss company that primarily markets and distributes press products. Besides distributing 3,000 publications, the retailer is responsible for other business units: a candy and chocolate chain, a tourist guide, a comic store chain, and a string of outlet malls.

Naville embraces a service-oriented big data architecture to ensure data flow between all four business units. On top of this architecture, Naville develops business applications that enable them to scale and refine communication and connectivity across business units.

 

Getting started with big data in retail

Today, customers expect a certain amount of guided selling. They want to know about products that interest and appeal specifically to them. Retailers need to present their consumers with products and promotions uniquely tailored to their preferences and habits. Catering to that individuality increases revenue, customer satisfaction, and brand loyalty.

At the same time, these retailers have to be mindful of their operations, marketing budget, and pricing optimization. They must develop and market products that are trending. They must be prepared with enough stock on Black Friday. They must price their products appropriately. Aggregating, normalizing, and interpreting big data allows retailers to achieve all of these objectives and more.

How do you get started? The first step is to choose a suite of apps that can link data from nearly any cloud or on-premises system while maintaining data integrity. Talend Data Fabric is a comprehensive suite of apps with 900+ connectors to facilitate seamless cross-communication with any number of CRMs, ERPs, and other customer data. Demonstrate the power of big data retail today — try Talend Data Fabric.

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| Last Updated: August 9th, 2019