Costly Ignorance: Enhancing Consumer Financial Decision Making

Avoiding pension information, or having too little knowledge to responsibly handle a credit card are examples of consumers’ “costly ignorance”. This dissertation therefore researches how to enhance consumer financial decision making. A first study investigates what drives differences in participants’ search for pension information, and shows that beliefs, trust and retirement anxiety are significant factors. A second study demonstrates the power of framing pension communication in a large scale field experiment. A third study reveals that older adults can offset cognitive decline and make better financial decisions because of their higher levels of experience and lower levels of negative emotions

Show Me My Future:Data-Driven Storytelling and Pension Communication

In light of current pension reforms driven by the rise in life expectancy, aging populations, and changing labor markets (Merton, 2014), communication about thosereforms and communication to get people activated is crucial. In the Netherlands,communicating efficiently via the existing communication channels is one of thebiggest challenges for the pension sector (Prast et al., 2012), as current reforms canhave severe consequences for different groups of stakeholders. Written communication (such as the yearly pension overview sent by post or email) is often notread or understood by their recipients (Elling & Lentz, 2018; Montae, 2012; PanderMaat & Lentz, 2013), possibly because this type of communication consists of wordsand numbers, which is especially unappealing to people who are used to visualcommunication. Our brain is faster in processing visual information, and we are alsobetter at remembering information transferred in visuals as opposed to text (Potteret al., 2014). Besides this, the content structure of information could be improved bymaking use of storytelling (Sax, 2006). In this paper, we first discuss the status quo ofpension communication in the Netherlands. We then summarize literature from thefields of marketing, economics, and finance on the effects of visuals and storytelling,draw from the field of human data interaction to showcase a series of applications ofvisualization and data-driven storytelling in the pension communication field, anddevelop implications for managers and scientists wanting to work on visualizationsand storytelling

Show Me My Future:Data-Driven Storytelling and Pension Communication

In light of current pension reforms driven by the rise in life expectancy, aging populations, and changing labor markets (Merton, 2014), communication about thosereforms and communication to get people activated is crucial. In the Netherlands,communicating efficiently via the existing communication channels is one of thebiggest challenges for the pension sector (Prast et al., 2012), as current reforms canhave severe consequences for different groups of stakeholders. Written communication (such as the yearly pension overview sent by post or email) is often notread or understood by their recipients (Elling & Lentz, 2018; Montae, 2012; PanderMaat & Lentz, 2013), possibly because this type of communication consists of wordsand numbers, which is especially unappealing to people who are used to visualcommunication. Our brain is faster in processing visual information, and we are alsobetter at remembering information transferred in visuals as opposed to text (Potteret al., 2014). Besides this, the content structure of information could be improved bymaking use of storytelling (Sax, 2006). In this paper, we first discuss the status quo ofpension communication in the Netherlands. We then summarize literature from thefields of marketing, economics, and finance on the effects of visuals and storytelling,draw from the field of human data interaction to showcase a series of applications ofvisualization and data-driven storytelling in the pension communication field, anddevelop implications for managers and scientists wanting to work on visualizationsand storytelling

Emotions and technology in pension service interactions

With the upcoming pension reforms, it is important that pension participants become well informed about their pension situation and the choices they have. Personal interaction between pension provider and participant – via email, call center or chat – plays an important role in this. In contact with participants, managing negative emotions such as sadness, fear or anger is essential. This requires good interaction between technological tools such as chatbots and human employees. Negative experiences with the pension provider can get in the way of an optimal pension choices in the long run

Emotions and technology in pension service interactions

With the upcoming pension reforms, it is important that pension participants become well informed about their pension situation and the choices they have. Personal interaction between pension provider and participant – via email, call center or chat – plays an important role in this. In contact with participants, managing negative emotions such as sadness, fear or anger is essential. This requires good interaction between technological tools such as chatbots and human employees. Negative experiences with the pension provider can get in the way of an optimal pension choices in the long run

Personalizing Communication and Segmentation with Random Forest Node Embedding

Communicating effectively with customers is a challenge, especially in a context requiring long-term planning such as the pension sector. Engaging individuals to obtain information on their expected pension benefits, by personalizing the pension providers’ email communication, is a first and crucial step. We describe a machine learning approach to model email newsletters to fit individual interests. The data for the analysis is collected from newsletters sent by a pension provider of the Netherlands and is divided into two parts (N = 2,228,000 participants in total of which 465,711 participants were part of the pilot study). Our algorithm calculates node embeddings over the nodes of a random forest, which are then used as features for the machine learning task. We illustrate the algorithm's effectiveness in classification tasks using multiple benchmark data sets, and in a data mining task where segmentation rules can be inferred from the node embeddings. The proposed model demonstrates competitive performance with respect to other approaches based on random forests, achieving the best area under the curve (AUC) in the pension data set (0.948), while also identifying customer segments that can be used by marketing departments to better target their communication towards their customers.</p