Visual analytics and artificial intelligence for marketing

In today’s online environments, such as social media platforms and e-commerce websites, consumers are overloaded with information and firms are competing for their attention. Most of the data on these platforms comes in the form of text, images, or other unstructured data sources. It is important to understand which information on company websites and social media platforms are enticing and/or likeable by consumers. The impact of online visual content, in particular, remains largely unknown. Finding the drivers behind likes and clicks can help (1) understand how consumers interact with the information that is presented to them and (2) leverage this knowledge to improve marketing content. The main goal of this dissertation is to learn more about why consumers like and click on visual content online. To reach this goal visual analytics are used for automatic extraction of relevant information from visual content. This information can then be related, at scale, to consumer and their decisions

The Champion of Images: Understanding the Role of Images in the Decision-making Process of Online Hotel Bookings

Images are vitally important in interesting consumers and helping them to make decisions. Images of a hotel are particularly important and were used to sell hotels even before the Internet, when travel agencies would often have brochures about hotel properties that they used to entice travelers. On many online travel agency (OTA) websites, the hotel's image can take up 33% of the space on the hotel property page, but the importance of this image in the decision-making process has yet to be studied. For many OTAs, there are currently no quantitative analytic methods that help determine which image to display in this critical location. In this research, we use deep learning to extract information directly from hotel images and we apply image analytics to understand the importance of this information in the online hotel booking process. To provide managerial insights, we will combine a prediction model, with the t-distributed Stochastic Neighbor Embedding (t-SNE) to classify and understand the types of images hotels generally use as their thumbnail or "champion" image and what aspects of these images elicit consumers to consider and book a hotel

Letting the Computers Take Over: Using AI to Solve Marketing Problems

Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications

A Spatio-Temporal Category Representation for Brand Popularity Prediction

Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new category based popularity prediction method by incorporating the spatio-temporal dimension in the representation. In particular, we focus on brands as a specific category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular

BigMove: A Group Intervention for People with Physical and Mental Health Conditions

INTRODUCTION: This article describes an innovative, integrated care intervention, called BigMove, which aims to improve the functioning, capabilities and quality of life of people with a combination of physical and mental health conditions. DESCRIPTION: Theoretical frameworks reflected in the intervention are the Capability Approach (CA) and Self-Determination Theory (SDT). Essential elements of the intervention included to expand participants' behavioural repertoire are motivational interviewing; functional goal setting (using the International Classification of Functioning, Disability and Health (ICF); cognitive behavioural therapy; enjoyment; support of the group; and physical activity. The design combines individual sessions and group sessions. DISCUSSION: By integrating the CA and the SDT, the intervention enables participants to make self-directed and value-driven choices in life and change their behaviour accordingly to strengthen their functioning and capabilities. To foster person-centred, integrated care, it is crucial to reform the interaction between professionals and patients and to re-structure the organisation and financing of care to enable the provision of complex integrated care interventions. CONCLUSION: For people with physical and mental health conditions, the intervention BigMove provides an innovative integrated care approach that addresses aspirations people have regarding their functioning and focuses on individual goal setting and behaviour change