In this report, we plan to use commonly available datasets from Kaggle (PATEL, 2021), which is basically customer’s records collected from a grocery firm’s database. In the course of the reporting process, we would be implementing RFM analysis (Correia, 2016) to be able to segment customers based on their buying patterns. This would help us understand the types of customers based on their historical purchase behaviour. Once that is done, we will also use classification models to be able to factor in the segmented customers and then target the customers with high potential or propensity to accept marketing offers. These customers would then be the ideal customers who would have higher conversion rates for any promotions sent to them.
Even before that, we would be going through the details of the dataset through some exploratory data analyses, and then cleaning the data for inconsistency and then finally performing RFM segmentation and then classification based on the information and data that we obtain to predict the response to the offers. It becomes a starting point to building relationships with consumers, who in turn become reliable with respect to generating businesses and provide improved conversion rates for the products offered by the businesses (Kim, 2006).
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Alfalasi, Majed, "Understanding Customer Purchasing Decisions using RFM and Machine Learning" (2022). Thesis. Rochester Institute of Technology. Accessed from