Big Data in Retail Industry 101 Overview & Implementations

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BigDataScalability.com – Retail is among the lucrative industries that benefit from big data these days. Data analytics in retail help customers enjoy a seamless experience when making a purchase, allowing retailers to increase customer satisfaction and sales. But how does big data in retail industry work?

In this ever-increasing digital age, the retail competition gets more intense. Retailers have to find solutions for some problems, such as complicated purchase processes and inefficient search. This is when big data comes into play, helping retail merchants solve these issues.

Big Data in Retail Overview

Big-Data-in-Retail-Overview

Today’s customers need a convenient shopping solution that allows them to purchase items easily, get discounts, receive buying recommendations, and even remember their transaction history. The question is, how to keep up with them while ensuring the business runs as usual?

Big data helps retail merchants create personalized shopping experiences to improve customer satisfaction. It is also essential to identify the target audience, garner customers, optimize the supply chain, ensure accurate business decisions, and more.

To collect data, retail merchants have several options. A loyalty program is the most common way to acquire data from customers but other methods like an online community, credit card transactions, log-ins, and IP addresses are worth trying.

The collected information can be used to give better insights into the market through retail data analytics. This is a process of analyzing big data in retail to reveal information and help them make decisions to improve customer loyalty, optimize price, gain better satisfaction, and so forth.

Furthermore, businesses can utilize retail data analytics trends to be more competitive. The trends include creating a personalized customer experience, predicting spending and demand, and also developing dynamic pricing models.

When adopting retail analytics, companies will have tools and technologies that allow them to provide a seamless experience to the customers to set them apart from competitors. As a result, they can expect improvement in sales and satisfaction.

Benefits of Big Data in Retail

Benefits-of-Big-Data-in-Retail

Analyzing big data allows retail merchants to predict emerging trends and target the right customers. There are many other benefits of big data in retail, making it one of the essential elements in growing businesses. The benefits include:

1. Build Better Engagement with Each Customer

Big data in retail promises a 360-degree view of the customer. This means you can get an accurate picture of a customer, including their favorite stuff, gender, location, purchase habits, and many others. This comprehensive picture lets retailers build better engagement with each of them.

Once retailers acquire the data, they can arrange accurate marketing strategies to catch the audience’s attention. For instance, retailers can decide on an effective yet affordable strategy according to their likes, gender, and even social media presence.

2. Enhance Service Quality

Big data in retail promises enhanced quality of service. The most common example is a toll-free number that allows customers to dial it in case of queries, suggestions, or complaints. Any call will be recorded so that the companies can analyze, measure, and take decisions based on it.

Another example is an in-store video recorder that helps identify the most strategic locations that are often accessed by customers. This information allows retail companies to place items they want to improve their sales. This also can make customers buy more than they need.

3. Optimize Price

Retail companies can benefit from big data to optimize prices. Consistent monitoring of relevant products lets companies predict trends even before they happen. Thus, retailers can have more time to prepare new products and anticipate a dynamic pricing strategy.

Pricing contributes to gaining more insights into customers as it is based on their purchasing habits and geographical location. Incorporating big data in retail, companies can perform beta tests for different segments to find out which pricing is the best.

4. Manage Supply Chain

Retail workers know how terrible it is when they run out of stock. Customers will get angry and customers’ trust will be endangered. Fortunately, companies can take advantage of big data to manage product distribution and supply chains.

The large data sets commonly include product logs that tell the retailers about the upstream operation. It works effectively to prevent retailers from depleted stock and angry customers.

5. Identify Potential Risks

Retail businesses run in a high-risk environment so an effective risk management solution is required to address this issue. Big data in retail allows companies to develop risk management strategies and processes by optimizing accurate decisions for potential threats.

How Big Data Transforms the Retail Industry

How-Big-Data-Transforms-the-Retail-Industry

Big data provides valuable information that is critical for companies to make accurate decisions. Not surprisingly, big data analytics is now applied in most stages of the retail process. Here’s a closer look at how big data transforms the retail industry.

1. Generate Recommendations

Based on information collected from each customer such as purchase history and recently viewed items, big data analytics and machine learning models help retail companies generate accurate recommendations containing items that a customer is likely to buy.

This provides the customers with a personalized and seamless shopping experience, which is expected to improve their trust and satisfaction. As a result, retailers can enhance their service quality and product sales.

2. Make Strategic Decisions

Big data in retail plays a critical role in a company’s decision-making. Before a retailer makes a decision, it needs to consolidate all the data acquired from trusted sources. Retail data analysis using Python or other tools reveals information that is needed in the decision-making process.

3. Forecast Demand

A few decades ago, predicting demand in retail was a daunting task. Thanks to significant improvements in technology, today’s retail companies can practically forecast potential demand in the future with excellent accuracy. This is another way big data transforms the retail industry.

Algorithms in big data analyze current trends in social media and web browsing, allowing retailers to draw conclusions. For instance, a retailer can cooperate with a weather forecaster to anticipate an increase in humidity, thus it can provide more anti-frizz products.

4. Promote Anticipatory Shipping

Once customers click an Order button, they can’t wait to see the products in front of the door. This is why giving extraordinary delivery service is critical to building trust and engagement with customers. Big data comes as a game changer as it allows retailers to perform anticipatory shipping.

Anticipatory shipping means retailers will ship products that a customer might purchase based on their transaction data and purchase history. Though it is a high-risk strategy, retailers can provide an excellent shopping experience that potentially improves customer satisfaction.

5. Help with Price Optimization

Big data in retail brings the industry to the next level by helping with price optimization. Many years ago, companies have to struggle to figure out the right price for their products and services so that target customers can afford them. Nowadays, big data makes everything easier.

Big data analytics allows retailer companies to track demand, inventory, and even competitors’ activity and pricing, so they can determine the right price for similar products. Big data also identifies if prices need to be lowered.

Implementation of Big Data in Retail

Implementation-of-Big-Data-in-Retail

After learning the benefits of big data and how it transforms the retail industry, a question might pop up in your mind. How big data is used in the industry? Let’s take a closer look at the implementation of big data to answer the question.

1. Big Data to Provide Marketing Insights

Company marketing is one of the most common applications of big data in retail. Big data analytics helps provide better marketing insights by monitoring online activity, customer behavior and transactions, and customer trends.

From the collected data, companies can reveal information to determine brand positioning, build better engagement with the target audience, design ads, etc.

2. Big Data for Risk Management

Big data has been used to develop risk management solutions in the retail industry. Large data sets help retailers predict potential risks and generate solutions that may solve the problems. To achieve the goal, businesses can collect internal data and identify which ones that may benefit them.

Big data analytics system helps retail companies identify their weaknesses that may lead to potential risks in the future. With internal data at their disposal, organizations can make further analyses for risk management.

3. Big Data for Recommendation Engines

Big data in retail is often used in recommendation engines. Based on customers’ transaction data and purchase history, big data analytics predicts which customers will buy which products. This will result in accurate recommendations whenever customers access the page.

4. Big Data to Fight Against Fraud

Fraud is one of the major problems for retailers these days. Retail frauds come in different forms, such as refund fraud, employee fraud, and discount abuse. Applying big data helps businesses fight against fraud in several ways.

For instance, companies can forecast sales at the SKU level by using predictive capabilities. If companies find a specific product that goes outside of the range, it means something fishy is happening in the business.

5. Big Data for Online Payment

Another application of big data in the retail industry is promoting easy and secure online payment. Using big data, retailers can integrate payment functions into a centralized platform. This method reduces the risks of fraud in real time.

Big data can detect money laundering transactions in every purchase though it appears like a legit payment. When this occurs, big data notifies customers and card companies before proceeding with the transactions.

Big Data in Retail: Challenges

Big-Data-in-Retail-Challenges

Despite the major benefits it offers, big data applications in retail companies come with some challenges. The challenges include:

1. Data Accuracy

Big data involves large data sets and it is rather daunting to make sure all data is accurate. Data accuracy and reliability become the main challenge in the retail industry as multiple factors can make data go wrong. Unfortunately, the final decision heavily relies on the collected data.

2. Data Security

Data breaches are among potential threats to big data applications. Ensuring data security is a challenge every retail business must overcome to achieve a successful big data application. Luckily, some data security tools are now available to protect big data in retail.

3. Technology

Technology plays a crucial role in big data applications. However, some retail businesses have financial restraints that prevent them from adopting advanced and sophisticated technology, providing supporting devices, and scheduling routine maintenance.

4. Privacy Concerns

Nowadays customers have bigger concerns over privacy and this can be another challenge for big data applications. Acquiring customer data may require extra effort, which means it delays big data implementation for data-driven operational decisions.

Retail companies are constantly trying to achieve a competitive edge by improving customer satisfaction, providing a personalized experience, and building better engagement with the target audience. Big data analytics allow retailers to achieve their goals, thanks to a bunch of benefits it offers.

The implementation of big data in retail industry varies widely, ranging from providing marketing insights and developing risk management to securing online payment and fighting against fraud. However, several challenges lie ahead so retailers need to find solutions to meet the goals.

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