Abstract
In the current era of customers, retail industries are transforming themselves into the customer-centric business models, where predetermination of customer needs and serving according to that may increase the reliability of business and enhance the profit. With the advent of new technologies, the retail industries need to be updated and one step ahead from the customers of new generation, whose demands are increasing based on continually changing trends. Conventional machine learning algorithms enable such industries to determine the needs and interests of their customers and make them able to attain the maximum profit from their businesses and look toward the new directions to expand the business. Correct implementation of these algorithms and techniques helps in anticipating the retail needs of the customers. Shelf placement plays a vital role in sale of product and customer engagement. A well-organized and associated placement of products on shelves increases the sale and makes customer comfortable with the shopping. A well-known technique, association rule is implemented in this paper using Apriori algorithm in Python, to identify the most common item sets sold together, which further helps in figuring out the more beneficial shelf placement for better customer engagement. It was found that items having more confidence rate are more likely to be purchased together and should be placed together for profit maximization. Our research produces a maximum confidence of 30% which is the result of our novel work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zufryden, F.S.: A dynamic programming approach for product selection and supermarket shelf-space allocation. J. Oper. Res. Soc. 37(4), 413–422 (1986)
Hahsler, M., Hornik, K., Reutterer, T.: Implications of probabilistic data modeling for mining association rules. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nuernberger, A., Gaul, W. (eds.), From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 598–605. Springer-Verlag (2006)
Lee, W.: Space management in retail stores and implications to agriculture. In: Marketing Keys to Profits in the 1960s, pp. 523–533 (1961)
Curhan, R.C.: Shelf space allocation and profit maximization in mass retailing. J. Mark. 54–60 (1973)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Arun Kumar, P., Agrawal, S., Barua, K., Pandey, M., Shrivastava, P., Mishra, H. (2020). Dynamic Rule-Based Approach for Shelf Placement Optimization Using Apriori Algorithm. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1014. Springer, Singapore. https://doi.org/10.1007/978-981-13-9920-6_23
Download citation
DOI: https://doi.org/10.1007/978-981-13-9920-6_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9919-0
Online ISBN: 978-981-13-9920-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)