The week's pick
Random Articles
Reseach Article
A Seasonal Approach for Analysis of Temporal Trends in Retail Marketing using Association Rule Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 71 - Number 13 |
| Year of Publication: 2013 |
| Authors: S. Hariharan, M. Kannan, P. Raguraman |
10.5120/12417-8904
|
S. Hariharan, M. Kannan, P. Raguraman . A Seasonal Approach for Analysis of Temporal Trends in Retail Marketing using Association Rule Mining. International Journal of Computer Applications. 71, 13 ( June 2013), 10-15. DOI=10.5120/12417-8904
Abstract
In an era when the customer is deemed as the king of the retail market, it pays heed to analyze every dimension of the customer's purchase behavior to provide an insight into their buying patterns and to better understand retail rationale of the customers. This dissertation has attempted to envisage the temporal traits of the customer's behavior in the Retail marketing. The research has put forth a seasonal study on the association amongst products in the realms of Retail Industry. In a country like India, obsessed with diverse seasons, the companies need to come up with better seasonal strategies that drive the market. Seasons have various dimensions to it viz. climatic seasons, festival seasons, etc. For each of those seasons the target audience keeps changing and the association of a set of products with customers also changes. This dissertation carries out an empirical study on the impact of seasonal and socio-economic factors on the buying patterns of the customers using Data mining tools and specifically applying the association rule for the products sold in the retail market sector. The results were analyzed by segregating the dataset into three seasons namely, January-April, May-August, and September-December. The results were interpreted with relevance to the seasonal behavior of the customers defined by the association of products. Weka 3. 7. 9 data mining tool is used for analyzing the data collected from XYZ supermarket located in Kanchipuram, TamilNadu, India. A mammoth 12000 transaction dataset and 215 product categories were involved in the research. This study reveals threadbare about the temporal association of products, for the Retail market to capitalize upon and to establish a better understanding and to enhance their customer relationship.
References
- Paulo J. Azevedo1 and Al´?pio M. Jorge "Comparing Rule Measures for Predictive Association Rules"
- pinnaboyina, Dhulipalla, Radharani "Survey Methods on Measuring Minimum Threshold for Association Rules"
- Maike Krause-Traudes, Simon Scheider, Stefan Rüping and Harald Meßner, "Spatial data mining for retail sales forecasting "
- Mu-Chen Chena, Ai-Lun Chiub, Hsu-Hwa Chang, "Mining changes in customer behaviour in retail marketing"
- Nikolaos Katsaras, Paul Wolfson, Jean Kinsey, Ben Senauer, "A Segmentation Analysis of U. S. Grocery Shoppers"
- Kenneth L. Reed and Joseph K. Berry, "Spatial Modeling And Data Mining In Retail Customer Loyalty, Competition Analysis, Propensity to Defect and Ad Media Selection".
- Dr. M. Dhanabhakyam, Dr. M. Punithavalli, "A Survey on Data Mining Algorithm for Market Basket Analysis".
- Tom Brijs Gilbert Swinnen Koen Vanhoof Geert Wets, "Using Association Rules for Product Assortment Decisions: A Case Study"
- Sema Kurtulu, and Kemal Kurtulu, "Prospects, problems of Marketing Research and Data Mining in Turkey"
- Raorane A. A. 1 Kulkarni R. V. 2 and Jitkar B. D. , "Association Rule – Extracting Knowledge Using Market Basket Analysis".
Index Terms
Keywords