# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 4 Results
### Emotion-Aspect Representation and Segmentation of Customers  
In prior research within the services sector, emotions have been utilized as a segmentation variable to identify meaningful customer groups. However, due to the inherently subjective nature of emotions, they can vary significantly among individual customers. Therefore, it is essential to model emotions at both the individual level and aggregate level to uncover similar emotional behaviours among customers. In this analysis, we showcase both approaches. Figure 10 displays the emotion-aspect profiles of two customers, based on their online reviews for the same hotel during the same time period (month). The two profiles reveal distinct experiences encountered by these customers, with varying emotions and mentions of hotel aspects. Customer 1 exhibits a predominantly positive emotional state compared to Customer 2, who expresses high levels of anger and sadness towards the hotel staff. Customer 1 reflects joy and trust emotions concerning the staff and room, while expressing anger towards the food and beverage aspect. Analysing emotions at an individual level enables the identification of specific pain points experienced by individual customers, thereby facilitating the development of targeted customer management strategies. By understanding the emotions and concerns of customers at an individual level, hospitality providers can tailor their services to meet individual needs, enhancing overall customer satisfaction and loyalty. Subsequently, customers' emotion-aspect profiles were vectorized and used as input data for the GSOM algorithm, which generated groupings providing an aggregated view of customers. Each data point in this map represents a customer, and the profiles reveal groupings of customers with similar behaviours in terms of the emotions and hotel aspects mentioned in their reviews (Figure 11). Figure 9: Segmentation of online reviews of one selected hotel using the GSOM. As observed, Cluster 1 exhibits angry customers with an intense expression of anger directed towards staff. In Cluster 2, customers express both anger and disgust, voicing complaints about staff and bathroom aspects. However, on the right side of the map, in Clusters 3 and 4, customers showcase more positive emotions. Cluster 3, comprised of positive customers, highlights mentions of staff and location in their reviews. In contrast, customers grouped in Cluster 4 have expressed high intensity of joy, complimenting the room and location. Notably, the use of emotion as a segmentation variable results in a clear separation of customers, even when considering the same hotel aspect. It demonstrates that there can be significant variations in emotions expressed by customers, allowing for a nuanced understanding of their experiences and preferences. This approach enables the identification of distinct customer groups with shared emotional characteristics, contributing to enhanced customer segmentation and personalized service strategies in the hospitality industry. Figure 11: Segmentation of customers using the GSOM. Figure 10: Emotion-aspect profiles of individuals based on their online reviews for the same hotel. The holistic view of customers of a hotel with such a visualisation provides direct insights for decision-makers and a deeper investigation would reveal details at a granular level. Hotels should incorporate such analytics into their customer management strategies to improve targeted customer care. Based on this premise, we have shown the applicability of applying computational modelling of deep emotions to determine the emotions of customers over time. This has been made possible due to the digital traces of customers that have accumulated over a long period. Continuous monitoring of such outcomes could be extended to developing time-series models to forecast the emotional fluctuations of customers.