Time series forecasting for understanding potential buyer behavior with ecommerce

Ecommerce is a platform for e-business Companies and hawkers for dynamically responding consumer demand and supply. Furthermore, responses to the consumer include blot-from-blue service with great quality of appurtenances. Moreover, the Indian retail industry is currently ranking in the world's top five concerning the growth. Thus, data is a new oil for this era of digitization. Henceforth, Cluster and distance classifier plays an important role in data-related findings. Besides, the cluster will give an identical pattern of data with the inclusion of centroid for finding out useful information. Furthermore, an already formed identical cluster pattern will be useful for mapping with another cluster. Thus, in this way cluster mapping done. Mapped cluster pattern will be useful in establishing the customer relationship with products. Moreover, it leads to the profitability of the e-commerce platform. Thereafter, cluster mapping is align with the new RFM model for getting more clarity about the consumer-buying pattern. Besides, it helps in identifying the potential buyer consumer. Moreover, time series results obtained are positive for potential buyer behavior. Thus, when time series forecasting is used on the RFM model it gives rise potential buyer loyalty with an e-commerce platform.