Applications of Deep Learning in Marketing Analytics: Predictive Modeling and Segmenting Customers

Abstract

These days, modelling techniques for forecasting consumer behaviour based on historical datasets are built utilising machine learning and deep learning, which are considered important tools for successful E-commerce. Advertisers aim to target particular people or groups with their messaging in today's fiercely competitive market, frequently ignoring their most important customers in the process. At this point, estimating customer lifetime value (CLV) becomes essential for helping businesses use their resources wisely. In order to accomplish this, businesses can use analytical tools and predictive models to target particular customers with customised campaigns, allowing for educated choices regarding advertising expenditures. Organisations can gain an edge over others by implementing the concepts discussed in this research to identify and retain their most valuable clients. When advertising strategies are modified in response to changes in customer categories, there is enormous potential for growth and client retention. In order to help marketers optimise their advertising campaigns, this work offers a thorough approach, tools, and insights that help anticipate changes in consumer segmentation and CLV.

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