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Impact of Big Data Analytics in Retail Industry (Simplified)

There is a huge demand for automating the traditional data management system for retail industries. The data generated online is increasing every second with the ever-changing shifts in customer behavior. In fact, about 40% of marketers study the consumer journey for crucial decision-making. The traditional methods might have been very useful and served you well but they are not as accurate as of the latest methods. To track the volume, velocity, and variety consequently is a complicated procedure but cannot be ignored in the digital era. 

This is why big data has originated as an independent stream in data science.

The first step of analytics is to collect data. The aim is to find out an easy way to process the data that will provide you with insights to take corrective actions. Big data analytics plays an important role in this situation, especially in the retail sector. Let us understand how?


What is Big Data Analytics in Retail?

Retail analytics is the concept that uses big data to optimize the price and supply chain while analyzing consumer behavior. Thus, a huge amount of data is used to determine the patterns, trends, human behavior and their interactions. 

In the retail industry, big data analytics helps companies collect and analyze customer purchase history and preference data, which further helps them attract new customers. The retail industry needs to collect a large amount of data for the sales of their product and this includes a customers’ purchase history. The amount of the data collected proceeds to expand further due to the availability and range of the business in online mode. 

Once this data is collected, stored and ready, big data analytics helps to identify customer behavior, shopping habits and patterns. It helps to improve the quality of service provided to the customers. Thus, leading to customer satisfaction.  No wonder, big data analytics in the retail market was estimated at USD 4.18 billion in the year 2020. The market is expected to reach USD 13.26 billion by the end of 2026.

Solve your business problems with the help of Big Data Analytics

Applications of Big Data Analytics in Retail 

The global retail analytics market is predicted to grow from $5.84 billion in 2021 to $18.33 billion in 2028. From a business standpoint, retailers will need to empower people across their organization to make decisions swiftly, accurately, and with confidence. The only way to achieve this is to harness big data and behavior retail analytics, to make the best plans and decisions, understand customers more deeply, uncover hidden trends that reveal new opportunities, and more. 

Below is the list of applications of big data analytics in the retail industry. 

Customer Segmentation

This is one of the most important components in the retail organization. It provides you with various ways and shows how different sections of society respond to the shifts in demographics and trends. It helps to segment customers in the following manner: 

Segment A: Customers who respond to the new promotions and advertisements 

Segment B: Customers who acknowledge the new product commencement. 

Segment C: Customers who respond to various discounts. 

Segment D: Customers who show a tendency in purchasing specific goods.

Segment E: Customers with varied purchasing power

Campaign/ Promotion Effectiveness Analysis

Once the campaign has launched, it’s necessary to study consumer response. The effectiveness of the campaign can be observed across various social media to study the ROI. This will greatly help you in understanding the main factors contributing to the success of the campaign. 

Customer Lifetime Value (CLV)

Not every customer will respond in a similar pattern. In this case, Customer Lifetime Value will help to calculate a relative amount of Risk-Adjusted Revenue and Risk-Adjusted Loss, which helps to assess the risk-return link. This gives an examined level of possibility of making money or suffering a loss on an investment. This includes adding some Net Present Value (difference between the present value of cash inflow and cash outflows for a certain period of time) and deducting the customer’s services.

Customer Potential

Presently, the customers who are not profitable for you may have a possibility of being profitable in the future. By this, we can say that it is very important to recognize the customer who has a high capability and adjust the marketing strategy accordingly.

Customer Loyalty Analysis

To retain an existing customer is more economical and effective rather than getting a new one. It’s important to get the best plan for the retention of customers by evaluating the reason they are leaving. Here, big data analytics helps you to understand customer retention considering the various factors that influence customers to drill down any transaction which might result in the change of the loyalty of customers.

Cross-Selling

Retailers rely on the data of the existing customers while cross-selling other products at the time of purchasing. Cross-selling can be done through product portfolio analysis as the portfolio represents all the products or services offered by a company. This way, it is possible for the retailers to sell the products that are missing from the portfolio. 

Price Optimization

Data analytics uses algorithms that perform several important functions for price optimization. It tracks the demand for the products in the market and observes the activities of the competitors. These factors are considered while optimizing the price of the product. 

Future Performance Prediction

Big data analytics helps collect and observe the customer’s interaction behavior about the product and services, such as customer queries via call, email, or social media. This further helps the companies compare the test to take preventive measures.  

To Select the Highest ROI Opportunities

Most retailers benefit from big data analytics in understanding the possibility of the customers base. With all this information they can estimate the market strategies with the highest Return on Investment (ROI).

Demand Forecasting

Demand forecasting is another benefit big data analytics contributes to the retail sector. Here, the factors to be considered are sales figures, environmental conditions, market conditions as additional help to determine the demand for the production services. 

Out-Of-Stock Analysis

Big data analytics in the retail sector helps retailers to analyze an ‘out of stock’ situation. As the number of variables is involved, there is a possibility for the procedure to become complicated. The integral part of the data analysis will help to calculate the lost revenue that has been caused since the product’s stock out. 

Channel Profitability

Data analytics helps to evaluate channel profitability, also known as distribution channels. A channel is a way that products or services get from the producer to the consumer. This also helps the retailer to compute whether it is profitable and whether they should continue to build up expertise in the channel. In case the retailers want to continue the channel, they should include some subjective factors that would enable technologies and techniques for the channel. 


How Big Data is Transforming the Retail Industry?

Big data analytics and artificial intelligence are playing a vital role in the transformation of the retail industry and have unlocked all the hidden possibilities of the business. According to Mckinsey, using big data analytics in the retail industry will help you to improve the operating margin of your form by an average of 60%. Clearly, with the help of big data analytics, companies are able to execute their plans easily– right from planning to sales.

Another survey revealed that the data in the retail industry is multiplying by infinity. Below are a few ways big data analytics can help the retail industry:

Estimates Consumer Buying Habits 

The retailers are constantly updating themselves with the changing taste and preferences of the customers. But it is a little difficult to accomplish as the demand of the customers is unpredictable. Big data analytics helps them point out the customer needs. It also explains to them the technologies and strategies that observe the customers’ tastes and preferences. 

Fraud Detection and Prevention

There are various hackers trying to steal important data from a valuable company’s system. This affects your business because your data and information are vulnerable if not safeguarded with the highest tech. This issue can affect the customer’s trust. By using big data analytics and technologies like Hadoop, Spark, or Map Reduce, retailers can analyze the transactions. Moreover, track users using the IP address to check the risk involved. 

Promotes Customer-driven Advertising

Expert and experienced marketing strategies are used and executed to analyze the customer’s location by buying behavior or patterns. With the help of big data analytics in the retail sector, retailers can share customized messages and offers to attract the attention of customers. 

Supply Chain Management

Logistics says that efficient management of the supply chain is an essential element to run your business in the long run. To evaluate the insufficiencies and any other inventory-related problem, big data analytics will help to collect all the necessary information.


Conclusion

Big data analytics helps retail companies outgrow their business at a large scale in a dynamic environment. This technology helps retailers get a clear idea to take immediate actions and enable great selling techniques. Retailers can observe and analyze the customer’s needs and are able to increase the profit. 

It is high time that retailer leaders should implement the power of big data analytics which helps you to solve problems and outgrow them in the long term and short term. DataToBiz, a team of experts helps businesses and various enterprises to adopt new technologies and strategies like artificial intelligence and big data analytics. The company is experienced in designing, planning, and implementing strategies to make a retail business profitable and help them achieve success online. 

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