Impact Of Big Data On Customer Retention In The Retail Industry
OVERVIEW
- You will find the best dissertation research areas / topics for future researchers enrolled in Business & Management.
- In order to identify the future research topics, we have reviewed the Business & Management literature (recent peer-reviewed studies).
- Big data is concerned with the effective use of business information which can be used for informed decision making.
- Most of the retail transactions occur in silos with minimum scope for sharing vital information.
- The potential for growth and expansion for the retail industry is much higher than any other industry due to increased purchasing power and standard of living.
INVENTORY REPLENISHMENT
Though inventory is a crucial resource for any business but for the retail industry it determines the future. In case of non-availability of inventory at the right time in the right place, there are great chances of losing the customer forever. A lost sale has multiple impacts on the business ranging from lost revenue, customer dissatisfaction, higher cost of procurement, etc. big data can be used in inventory replenishment for optimization of inventory management. Inventory replenishment has three vital tasks, namely sales forecast, inventory management, and auto-replenishment. These missions put together to consist of inventory replenishment.
Big data is used in the sales forecasts focusing on understanding the buying behavior and pattern of customers. The forecast helps to project the market trend and pattern. Though retail businesses most often maintain Excel Sheets or ERP to record sales, however, least is done on the predictive sales analysis. With the help of big data, the business can be in a comfortable position of foreseeing future sales to amend the inventory position.
Inventory management includes a wide range array of functions including inventory control and logistics. By using big data retail businesses will be able to control the level of inventory in the business. Since over and under stocking has the direct brunt of operational efficiency. Inventory control, in turn, has two layers i.e. inventory level and vendor selection (Based on parameters such as price, quality, responsiveness, etc).
All of the data (from the above process) analyzed and complied with the previous two steps will yield no result if the required inventory doesn’t reach the business for the customer to pick up from the shelf. The last function of big data in inventory replenishment is auto-replenishment wherein the time, vendor and the rate of placement for the order are determined by using big data. The culmination of the business data with data analytics will help retail businesses in a big way by ensuring to avoid a lost sale.
Use of big data in inventory replenishment will have a retail business through:
- Increase the operational efficiency of the business by obtaining real-time information about inventory position.
- Increase customer satisfaction levels.
CURRENT CHALLENGES
It is certainly true that big data can help the retail sector however there are bottlenecks at the operational and implementation stage. First, to have a strong and robust analysis of data, the data collected from various stakeholders should be accurate and precise. Apart from the correctness of the data, the method of collection of data is also crucial. Since most often data for retail business is procured from various sources, the possibility of data tampering is high. Hence the cleanliness of the data collection method is questionable. Second, operational difficulties such as insights of the industry, logics of the data set, etc need to be streamlined. Over 95% of the retail industry is still considered as an unorganized sector which implies knowledge, expertise, and experience related to ground reality are still scattered and unrecorded. The third is the difficulty at the implementation stage due to a lack of skilled and thoughtful workforce. Though the industry has the potential to grow and become one of the most important sectors in international business, however, due to industry-specific issues, it is yet to grab the eyeballs of the potential workforce.
FUTURE SCOPE
Big data analysis will help companies to explore and captivate on the potential of using a large volume of data, compiled in a sensible format. Better collated data has the power to be considered as a competitive advantage. Due to the increased cost of logistics due to fluctuating fuel prices and inconsistent vendors, big data is considered as a perfect solution for concerns over inventory management. Due to easy accessibility and high feasibility, e-business has become a major game-changer in the retail industry. the industry is looking at gathering information from offline and online customer patterns to enhance the overall customer experience.
CONCLUSION
Big data has the potential to largely contribute to unleashing the scope for the retail industry. it will help the business to comprehensively understand and collaborate data for better decision making. Since business is incapable to handle data from multiple stakeholders and use them in favor of the business, the only feasible solution is big data. Since big data also provides data security and privacy, the information collected and analyzed can be stored and used for the business. In a nutshell, it can be concluded big data is capable to revolutionize the pace and nature of the retail industry across the globe.
References
- Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of Business Research, 97, 160-173.
- Liu, L., & Cai, S. (2019). Exploring the Optimization of Logistics Distribution Mode of Supply Chain Management under the Background of New Retail.
- Seranmadevi, R., & Kumar, A. (2019). Experiencing the AI emergence in Indian retail–Early adopters approach. Management Science Letters, 9(1), 33-42.
- Suriyantphupha, P., & Bourlakis, M. (2019). Information Technology in a Traditional Retail Supply Chain: A Structured Literature Review. Projectics/Proyectica/Projectique, (1), 89-102.