BUSINESS ANALYTICS IN RETAIL SECTOR
Table of Contents
Background of retail industry and data analytics: 3
Challenges faced by the retail industry: 5
Benefits of big data analytics for the retail industry: 5
Big data analytics as to the decision support system in the retail sector: 7
The decision support system: 8
Executive summary:
Retail is an important industry that serves millions of customers around the world by selling different accessories and household products. With the increasing demand in this industry, the numbers of organizations have also increased as well. This has resulted in an increase in competition among these organizations for attracting and retaining more customers. In this aspect, data analytics has become a necessary process that helps the leaders of these organizations to make essential decisions on understanding customer behavior and to analyze the trends of the retail industry. Even the data analytics tools also help in managing the logistics and supply chain management of the retail business, which are necessary to achieve the resources effectively.
This report has thus focused on the use of big data analytics tools in the retail sector for helping the leaders and the managers to make decisions on supply chain management, logistics, and for attracting the customers. A decision support system has been identified, which can help the managers to analyze the impact of the decisions on the business. An assessment has been made on the contribution of big data for developing the pricing strategies and to analyze the demands of the products according to respective geographical areas. This report also helped in identifying that big data analytics can take the help of data mining techniques for gathering different data about the trends in the demands of the customers. Therefore, the leaders of retail organizations can make use of the data for predicting the changes that are necessary for attracting and retaining the customers. Similarly, it also acts as the decision support system for improving the management of the supply chain and logistics of the business.
Introduction:
General statement:
Big data analytics is now utilized as an efficient decision support system for the managers of the retail sector for managing and predicting the necessary elements of the business, such as supply chain management, logistics, and customer demand and fraud detections.
Background of retail industry and data analytics:
Nowadays, the retail sector is in high demand among the customers as they are now able to buy different accessories and household products from a single store. According to a report, in 2018, global sales in the retail industry have become $6 trillion (Meyer, 2020). Retailers such as Walmart and Costco have become hugely successful with their unique business strategies and be successful in meeting with the expectations of the customers. This is the reason why it has become famous for the organizations of this industry to assess the demands of the customers for different products so that they can be retained successfully (Kong, Ellickson and Lovett 2017). Even it has become important to manage the supply chain of the business with perfection in order to manage the stocks and to deliver the goods without delay. With the growing competition in this industry, it is no longer possible for the managers of this industry to use manual processes for managing the business processes. Even, it has become essential to make the decisions as such that the expectations of the stakeholders can be met successfully.
Image: Growth of sales in the retail industry
Image source: (Research, 2020)
Hence, a decision support system has become an essential requirement for the managers of the retail sector. In this aspect, data analytics is a process by which it becomes possible for the managers to gather and analyze the data regarding the customer behavior, demands of different retail products, and market scenarios of various organizations of this industry (Holmlund et al. 2020). Therefore, big data has been chosen as an important technology that can help in increasing the quality of data analytics for the managers of retail organizations. It has now become possible to create custom analytics with the help of big data technology (https://www2.deloitte.com/content/dam/Deloitte/ch/Documents/consumer-business/ch-cb-en-Deloitte-Analytics-in-retail-0514.pdf, 2020).
Challenges faced by the retail industry:
Organizations of the retail sector often face problems to analyze the customer demands and their expectations. Managers of these organizations fail to identify the products which are accepted by a large section of customers and which are not. In this way, they struggle to implement a proper resource and supply chain management (Fernie and Sparks 2018). This failure increases business expenses, and even, some of these organizations face financial losses due to weak sales growth. Also, difficulties come in the aspect of developing strategies for improving the performances of the employees. Further, managers face challenges while setting the sales price of the products due to lack of market research (Bolton and Shankar 2018). This is when data analytics helps the managers to analyze data from different sources and further helps in identifying the trend of the industry in the respective market.
Benefits of big data analytics for the retail industry:
Organizations of the retail industry perform various business activities such as assorting the goods, management of the inventories, and management of stocks and pricing of the products. According to authors Aktas and Meng (2017), it is a challenge for the managers of retail organizations to develop the prices for different products. This is mainly due to the fact that the costs of various products vary according to their demand, different locations, and competition. Hence, it is an essential task for the managers to assess the proper rates for the respective products for attracting customers. In this aspect, big data helps in analyzing the data regarding the price differences of the products due to many factors. It acts as a decision making as well as predictive analytics tools for the managers to set the proper price of the respective products (Bradlow et al. 2017). Many retail organizations around the world are now able to get insights about the pattern of customer demand and expectations, and the way pricing strategies are developed.
Image: Benefits of big data analytics
Image source: (Insights, 2020)
Companies such as Walmart and IKEA are now using a data analytics tool as the decision-making software for assisting the managers in making decisions on developing the business strategies (Umbel, 2020). Big data analytics is also helping the retail organizations to personalize and customize the customer experience, which in turn is helping them to meet with the level of customer satisfaction.
Authors Brinch, Stentoft, and Jensen (2017) highlighted that big data is helping the managers of retail organizations to manage the supply chain effectively. With the ability to analyze the data, big data technology is helping the managers to identify the demand of different products, to increase the visibility of the supply chain management process, and to identify and select proper suppliers for the business. Even, it has become possible for the managers of the retail organizations to identify the risks in the supply chain process and for determining the useful methods which can help in mitigating those risks successfully. The paper also described that big data helps in assessing the financial performances of supply chain management of the business within a particular period. In this way, big data analytics has become a decision-making tool for the organizations to increase financial performance and to cut down the losses in supply chain management of the business.
Authors Oncioiu et al. (2019) described that big data analytics is helping in enhancing the performance of the supply chain of the organizations of the retail industry. The research paper highlighted that it has become possible to increase sustainability in the production of the supply chain by assessing the data of the performances for a particular period. In this way, managers are not able to develop the strategies which are further helping the retail organizations to counter the risks in the supply chain of the business as well. Authors of the research paper summarized that with the help of big data analytics, it has now become possible for the managers of the retail organizations to identify the pattern of supply chain management in the other successful organizations of this industry. These data thus are helping them to create the strategies which can help in closing the gaps in the performance of the supply chain of their organizations successfully.
According to authors Hofmann and Rutschmann (2018), big data analytics helps significantly to forecast customer demand in the retail industry. This means that it becomes possible for the managers of the retail companies to analyze the data regarding customer behavior and application, which in turn helps them to assess the demand of the particular products among the customers. Therefore, managers are now able to manage those products effectively so that the market among the customers can be met successfully. The research paper also described that by assessing the customer behavior, it becomes possible to implement the strategies which can help in attracting as well as retaining them successfully. This method can also help in bringing sustainability to the retail business.
Authors Wamba et al. (2017) discussed that by using big data analytics, it has now become possible for the managers of the retail organizations to analyze the performances of the employees and to compare the achievements with the business goals. This has been possible by analyzing the past and present data of the performances of the employees and by comparing the results with the standard performance criteria that are required to improve the financial performances of the business.
Big data analytics as to the decision support system in the retail industry:
In the aspect of making important decisions of the business, it has become famous for the managers of the retail organizations to predict the outcome of the choices. By predicting the results, managers are now able to identify the impact of the choices on the business and different stakeholders (Feki, Boughzala, and Wamba 2016). In this way, if the outcomes are predicted to be not efficient, then it becomes possible to change the decisions without delay. These processes of the decision support system are getting enhanced by using big data analytics. A business analyst can assess a large volume of data and then can help in suggesting the managers of the retail organizations change or alter the decisions if those are not found to be useful for the business.
The decision support system:
The decision support system based on big data is helping the managers of the retail organizations to make use of the information system of the business so that the past and present trends of customer behavior can be properly utilized. It helps the managers to identify the changes that are required to be made for improving the business performances of the employees as well as for customer service. Managers are now able to receive real-time data regarding the demands of the products, price ranges in different areas, and application of the suppliers (Aloysisus et al. 2018). Therefore, by analyzing real-time data through their information system, managers of retail organizations are now able to make decisions that can help in meeting with the business goals. It has now become possible for the managers to track the day-to-day activities of the business by collecting the data. Therefore, these managers are now able to make the changes in the strategies as per the trends of customer service in the retail industry in a particular market. Even, it has now become possible to collect feedback and reviews of the customers around the world regarding specific products. These data are helping the managers to understand the preferences of the buyers, which in turn are helping them to customize the shopping experiences for the respective customers.
Image source: Importance of decision support system
Image source: (Scnsoft.com, 2020)
Conclusions:
Big data analytics, thus, has become a successful tool for business analytics and is also acting as an efficient decision support system for the retail industry. Retail organizations are now able to compete with each other efficiently by understanding the expectations of the customers and by developing the strategies according to the market trend. These have been possible significantly with the help of big data analytics. This decision support system has also helped in improving the customer experience, which in turn is helping the retail organizations to meet with the expectations of the customers. Therefore, the decision support system based on big data has become a relevant solution for retail organizations for making key decisions and to develop business strategies with the help of real-time data analysis.
This report has considered some research papers from where it has been identified that the managers of the retail organizations can also determine the frauds and risks in the business with the help of big data analytics. With the help of these data, managers can take the necessary control to avoid any losses in the industry. Some recommendations have been provided for utilizing big data analytics tools with efficiency.
References:
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