Abstract:
Modern business entities re-engineer all core business and secondary business activities for customer satisfaction, thereby boosting profit margins. In re-engineering efforts, businesses require immense data processing and decision-making on customers and buying patterns. Still, SMEs face challenges when moving on to customer-centric marketing due to the less accumulated data and resistance towards investing in erudite decision-making tools. Hence, this current research study aims to provide a feasible data mining approach for SMEs in customer recognition with low computation complexity. Data accumulation has happened during the introduction of SME’s mobile application to its customers. Hence, the dataset consists of demographic features age, gender, residency region and occupation of each customer. The proposed approach has two phases as follows; the first phase, the customer demographic data with the target variable of purchase value had subjected to data preprocessing. Null values and noise have treated with a binning method. In the second phase, feature engineering had carried out so categorical variables are in numerically manner. Therefore, the binary encoding was used for categorical. Finally, the dimensionality reduction of the processed data had done using Principal Component Analysis (PCA) to extract the most prominent and customer explanatory attributes within the SME. The PCA yielded 10% of a reduction in total explained variance percentage, meaning that the data had compressed. Using KMeans clustering 4 distinctive clusters were extracted. The usage of PCA had leveraged quick clustering and obtained 4 clusters represented the most impactful customers between the age of 0-17 and occupational level 10. With the implementation of PCA, the dataset narrowed down only with the most prominent features that an SME should care of and with this methodology SME can initiate the practising of more efficient customer data analysis using data mining and machine learning.
