Social media and customer behavior analytics for personalized customer engagements

Abstract

Companies in various industries, including travel, hospitality, and retail, increasingly focus on improving customer relationships and customer loyalty. In this paper, we propose a new systems architecture that combines the textual content in social media messages with product information, such as the descriptions summarized in catalogs, in order to provide marketing campaign recommendations. Companies commonly build user profiles based on purchase histories and other customer-specific information; however, when dealing with social media, we often cannot match the social media users with the customers. In this regard, we address the problem of targeting individual social media messages for which no personalized profile information can be retrieved. Our solution combines two disparate computational toolboxes for text analytics - natural language processing and machine learning - in order to select social media users for whom to target with topic-specific advertisements. Natural language processing is used to analyze the context of social media messages, and machine learning is used to analyze product information, with the goal being to match social media messages to products and ranking potential advertisements. To demonstrate the framework, we detail a real-world application in the travel and tourism industry using Twitter® as the social media platform.