Any industry expert will state that the retail industry is too complex and volatile. But no one will deny the rate at which the industry is rapidly growing. The financial projections have crossed billions to reach the trillion-dollar mark. In fact, predictions state that global retail sales will reach $31.3 trillion by 2025.
Several factors have contributed to the rise of the retail industry, one of which is Big Data Analytics. What exactly is “Big Data”?
In simple words, the term refers to massive volumes of structured and unstructured data that are collected, processed, and interpreted for analytical insights. Using computer algorithms, big data collated from the retail industry can reveal patterns and trends in customer persona, buying behavior, and customer experiences. This allows companies to make more accurate predictions about consumer behavior, optimize marketing strategies and make more informed decisions about product development.
Apart from the massive volume of data, Big Data has two more dimensions: Variety and Velocity.
Currently, Big Data is the revolutionary buzzword in every industry. And with retail growth bursting at the seams, why and how can this industry be any exception?
So, yes, big data analytics is the future of retail because it delivers a fuller picture of the marketplace. By leveraging Big Data, retailers can identify which strategies are working and which aren't, allowing them to make more informed decisions. Furthermore, the rapid adoption of data-centric technology will enable retailers to optimize investments and drive better business outcomes.
The relationship between retailers and Big Data runs deep. From big names to startups in the retail industry, everyone relies on Big Data to:
The value of Big Data is better explained with the following five behavioral analytics use cases.
1. Delivering tailored customer experience
Retailers can make customer-centric decisions based on Big Data Analytics. This will enable them to anticipate customers' needs and personalize their marketing campaigns. Consumers' data collected from websites, mobile applications, and social media platforms generate massive data that helps retailers focus on customizing customer experience.
The Case Study:
Omnichannel retailers can harness data engineering platforms to:
2. Detecting and preventing fraudulence
Fraud detection and prevention are mandatory to prevent financial loss and maintain customers’ trust in the retail business. To protect their customers and their own financial interests, businesses must identify and prevent fraud before it occurs. This includes recognizing patterns of fraudulent behavior and using advanced technologies to detect and prevent it. Credit card frauds and fraudulent returns of purchased goods are two of the most commonly observed frauds.
Also Read: From Complex to Simple: Tips to Automate Retail Data Transformation
The Case study:
Retail giant Amazon’s fraud detection and prevention measures keep its payment gateways well-guarded and impregnable. Amazon has successfully reduced about 50% of frauds thanks to its completely watertight strategies.
Amazon’s fraud detection software which is based on predictive analysis, has all the information related to:
Access to such detailed data allows Amazon to track and detect patterns that might indicate fraudulent activities. This helps them to implement counteractive measures before they can do any damage.
3. Predicting customer demands
One of the easiest ways to utilize Big Data is to generate insights into customer buying habits and preferences. Retailers can identify which products and services are most in demand and which should be discontinued. A retail forecasting system will also enable them to manufacture new products in accordance with the current market trends.
The Case study
One of the most innovative and cleverest uses of Big Data to predict customer demands is the collaboration between The Weather Channel, Pantene, and Walgreens.
Pantene and Walgreens banked on The Weather Channel’s prediction about high humidity levels to advertise their hair products that help women with increased humidity in the air. As a result, Pantene sales at Walgreens increased by 10% during July and August, and overall hair category sales increased by 4%!
4. In the management of the supply chain
There are a number of strategies retailers can use to improve the efficiency of their supply chains. Their adaptable strategies enable them to better anticipate customer demand and leverage technologies that enable real-time data sharing across the entire supply chain.
The Case Study
In Metro Group stores, retail analytics helps detect and communicate with customers and store personnel about the movement of goods inside the store. The retail giant’s algorithm recommends similar-sized clothes when customers try on apparel inside the trial room. This intuitive data analytics system goes one step further to track when a product is taken off the shelf and replaced. The data is later used and processed to gather customer analytics. Metro Group’s analytics system also alerts store managers when a particular product is repeatedly pulled off the shelf but has never been billed.
5. For operational efficiency
For retailers operating with omnichannel, operational efficiency streamlines the coordination between production and inventory units. It gives the company the ability to respond to changes in customer demand. To this end, Big Data has provided retailers with the tools and insights needed for:
The Case Study
In the retail world, data is gathered from almost every conceivable device. Everything is a data source, from servers, product logs, and customer-owned devices to agricultural machinery and the power grid.
The valuable data gathered from these sources is used to gain insights into the performance of the assets and detect inefficiencies, malfunctions, and potential security threats. It can also be used to predict maintenance needs and optimize operations.
As the amount of data increases, collecting, processing, and analyzing it becomes increasingly difficult. Hence, businesses are turning to automation and advanced technology like AI to detect and respond to issues. Automation helps reduce the time and cost associated with manual data collection and analysis, and AI can see patterns and uncover insights that would have been impossible to find manually.
To Conclude
Big Data is omnipresent, and the retail world thrives on it to survive and succeed. As retail grows, so does Big Data and vice versa. It won’t be long before we witness more astonishing use cases demonstrating Big Data’s role in retail. Let’s wait and watch!
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