Targeted Marketing on Social Media: Utilizing Text Analysis to Create Personalized Landing Pages

Abstract

Abstract The proliferation of social media has rendered it a critical arena for online marketing strategies. To optimize conversion rates, the landing pages must effectively respond to a visitor segment's pain points that they need solutions for. A one-size-fits-all approach is inadequate since even if the product meets the needs of all consumers, their priorities may diverge. In this study, we propose a pipeline for creating personalized landing pages that dynamically cater to visiting customers' specific concerns. As a use case, a pipeline will be utilized to create a personalized pharmacy discount card landing page, serving for the particular needs of chronic diabetics users seeking to purchase needed medications at a reduced cost. The proposed pipeline incorporates additional stages to augment the traditional online marketing funnel including acquisition of salient tweets, filtration of irrelevant ones, extraction of themes from relevant tweets, and generation of coherent paragraphs. To collect relevant tweets and reduce bias, Facebook groups and pages relevant to individuals with diabetes were leveraged. Pre-trained models such as BERT and RoBERTa were used to cluster the tweets based on their similarities. GuidedLDA exhibited superior performance in identifying customer priorities. Human evaluations revealed that personalized landing pages were more effective in getting the attention, building attraction by addressing their concerns and engaging the audiences. The proposed methodology offers a practical architecture for developing customized landing pages considering visiting customers' profiles and needs.