Understanding the consumer journey is crucial in hospitality, where satisfaction, delight and loyalty depend on service quality. While customer journey mapping is popular, research often overlooks variations in customers’ thoughts and feelings across the stages of the customer journey. This study aims to fill that gap by analyzing the customer journey in luxury hotels, examining how rational and emotional factors that influence consumer delight, behavior and satisfaction evolve across each stage – from initial engagement to post-stay evaluations.
K-means clustering was used to segment consumers based on delight. Chi-square and ANOVA analysis facilitated assessment of variances between delight segments along the customer journey.
K-means clustering identified three guest delight segments: “Dissatisfied seekers,” “Content but cautious” and “Delighted advocates.” Results highlight differing decision-making factors, experiences and emotions encountered at various stages of the customer journey by each segment, indicating the need to develop tailored service delivery strategies.
This study was conducted in the luxury hotel segment in Istanbul, which may limit generalizability. Future research should replicate this study across different geographical regions and hotel categories to explore cultural variations and broader applicability. Longitudinal studies could assess how emotional engagement evolves across multiple stays. The focus of this research is luxury hotels; future studies could explore whether delight-driven segmentation applies to other hospitality segments, such as mid-range or budget hotels and alternative accommodations.
To the best of the authors’ knowledge, this study provides the first holistic analysis of the entire consumer journey in the luxury hotel sector – from pre-stay decision-making through onsite experiences to post-stay evaluations. While previous studies have typically examined individual touchpoints or a single outcome variable, this research integrates both emotional and rational factors; segments guests based on their behavior and satisfaction profiles; and identifies critical touchpoints that influence both customer satisfaction and loyalty. It contributes to luxury hotel experience management theory and provides practitioners with concrete recommendations to improve each stage of the guest journey.
1. Introduction
Customer satisfaction and loyalty are no longer shaped by individual moments but by the quality and consistency of the entire experience, from initial brand interaction to post-stay evaluations (Lemon and Verhoef, 2016). These experiences encompass digital interactions, such as websites and reservations and physical processes, such as check-in and personalized services (Burston Webster et al., 2021). Although consumer journey mapping is becoming more widespread, research generally focuses only on purchase decision or post-consumption stages, overlooking the holistic and complex nature of the journey (Lemon and Verhoef, 2016). This segmented approach does not adequately reflect the nature of continuous interactions, particularly in the hospitality industry, where multi-stage service encounters are prominent (Anderl et al., 2016; Kannan and Li, 2017).
A significant gap in the existing literature is the lack of comprehensive consumer journey studies that include pre-decision and post-experience stages. Accommodation studies generally examine these stages separately, providing only limited explanations of the effects of touchpoints on customer perceptions and behavior. Therefore, academics emphasize the need for a comprehensive framework that reflects the dynamism of experiences developing across different service interactions (Kranzbühler et al., 2018). Additionally, there is a need for studies that acknowledge the multifaceted nature of accommodation experiences and investigate the combined effects of emotional and rational factors throughout the entire journey.
The hotel industry offers an ideal environment for examining the complexity of the consumer journey. Providing seamless, memorable experiences across multiple stages is critical to enhancing customer satisfaction and loyalty. Luxury, four- and five-star hotels place greater emphasis on physical and human elements (Walls et al., 2011). Guests at these hotels have higher expectations and expect superior quality at every stage of their journey (Padma and Ahn, 2020). However, this holistic approach has largely been overlooked in the literature, with studies typically focusing on post-stay evaluations or brand loyalty.
This article aims to fill a gap in the literature by providing a holistic analysis of the consumer journey in the luxury hotel industry. The study has three objectives:
to identify critical touchpoints that influence satisfaction and loyalty by analyzing the entire consumer journey;
to segment guests according to their satisfaction levels to understand different profiles and behavior patterns; and
to provide a comprehensive perspective on customer experience management by integrating emotional and rational factors.
These contributions propose a comprehensive model for consumer journey mapping, emphasize the role of satisfaction in emotional attachment and loyalty and provide practical insights to hotel managers on how to tailor their services to different segments.
2. Literature review
2.1 The consumer journey in the hospitality industry
In line with recent developments in the hospitality industry (Lemon and Verhoef, 2016), this study addresses the customer journey in three stages: pre-decision, decision-making and post-experience. Expectation confirmation theory (ECT) (Oliver, 1980) emphasizes that satisfaction arises when actual experiences meet or exceed expectations. The application of ECT enables a comprehensive understanding of consumers’ processes of expectation formation, experience evaluation and shaping future behavior in the hospitality context.
Emotional attachment and rational factors play a central role in consumer decisions in the hospitality industry (Prayag et al., 2013). Emotions such as happiness and enjoyment emerge as the primary drivers of loyalty, particularly during the experience and post-experience stages (Ali et al., 2016). In contrast, rational elements such as price, service quality and convenience are more prominent during the pre-decision and decision-making stages (Kannan and Li, 2017).
Previous studies have shown that hospitality brands that combine emotional and rational elements encourage greater long-term loyalty (Rather, 2017). For example, experiential features such as a unique atmosphere or personalized service increase emotional attachment and satisfaction (Ju and Jang, 2022; Kandampully et al., 2018).
2.1.1 Pre-decision phase.
The pre-decision phase represents the initial interaction between consumers and the brand, where potential guests gather information through word-of-mouth, prior experiences, online reviews and brand reputation (Xiang et al., 2015; Filieri et al., 2015). In today’s digital era, social media and peer reviews strongly shape expectations and influence decisions (Sparks and Browning, 2011; Belanche et al., 2024). According to ECT, expectations formed in this phase become reference points for evaluating future experiences (Oliver, 1980). Additionally, this phase builds initial consumer commitment, driven by positive information and emotional connection to brand messaging (Park et al., 2010).
2.1.2 Decision-making phase.
The decision-making phase marks the shift from consideration to action, where consumers decide whether to book a hotel (Kahneman, 2011). This decision is shaped by price, perceived value, convenience and emotional appeal (Sparks and Browning, 2011). Consumers assess the hotel’s value proposition against their expectations (Li et al., 2018), and as per ECT, perceived value guides final choices (Oliver, 1980). Real-time digital tools, including dynamic pricing and integrated reviews, further influence decisions. Emotional factors such as brand attachment and trust also play a key role (Gomez-Suarez and Veloso, 2024; Kim et al., 2023). Commitment at this stage is crucial, reinforcing the intention to book (Amin et al., 2021). Consumers who perceive a hotel as trustworthy and aligned with their values are more likely to finalize their choice.
2.1.3 Post-experience phase.
The post-experience phase involves consumers evaluating the service after consumption. Satisfaction or dissatisfaction arises from comparing actual experiences with prior expectations (Mauri and Minazzi, 2013). Positive experiences foster loyalty and repeat bookings, while negative ones lead to dissatisfaction and harmful reviews (Filieri et al., 2015). According to ECT, when experiences meet or exceed expectations, satisfaction leads to loyalty and positive word-of-mouth (Oliver, 1980; Kandampully et al., 2018). Conversely, unmet expectations cause dissatisfaction. This phase is crucial in hospitality, as feedback strongly influences future guests’ decisions (Ladhari and Michaud, 2015). Positive evaluations reinforce loyalty and help brands exceed future expectations, fostering delight.
2.2 Segmenting the consumer journey: the role of delight
In hospitality, where experiences shape loyalty, understanding emotional and rational influences is essential. Segmenting customers by emotional responses, especially delight, is effective. Delight, a stronger response than satisfaction, signals exceeded expectations and helps differentiate loyal customers (Oliver, 2014; Barnes et al., 2016). It fosters deeper brand attachment, repeat patronage and advocacy (Torres and Kline, 2013; Kandampully et al., 2018). Beyond satisfaction, delight enhances engagement and organic growth, increasing lifetime value and retention (Dolnicar and Grün, 2008).
In the luxury hotel sector, consumer behavior gaps persist (Mele et al., 2024). Segmenting by delight allows tailored strategies for specific needs, strengthening loyalty and satisfaction (Dolnicar and Grün, 2008). Understanding delight drivers helps exceed expectations and build emotional bonds (Füller and Matzler, 2008). Ultimately, this approach improves experience management by addressing emotional and practical aspects across all touchpoints.
3. Methodology
3.1 Data collection
A personal survey was conducted in Istanbul, Türkiye (NUTS 2 region), in 2024, with ethical approval from Bursa Uludağ University Social and Human Sciences Research and Publication Ethics Committee (2023–11). Istanbul, as a major hospitality hub, offers many luxury (four- and five-star) hotels. The final sample included 600 respondents: 300 international and 300 domestic tourists. The sample was 44% female and 56% male; 80% had university or postgraduate education, 19% had higher education and 1% had secondary education. Regarding socio-economic status, 15% reported medium, 60% medium-high and 25% high. About 28% stayed with family, 28% with friends, 18% as a couple, 12% with colleagues, 9% alone and 5% in an organized group. Respondents were required to have stayed at the hotel during data collection.
The questionnaire captured the customer journey across three stages:
Inspiration and search (pre-decision);
Decision moment; and
At the hotel (post-experience).
An evaluative component on consumer delight enabled cluster creation. Nominal measures were informed by prior studies (Bhinde et al., 2023; VanBergen et al., 2022; Castro et al., 2017; Kim and Fesenmaier, 2017; Burns and Neisner, 2006; Ho et al., 2012). Commitment and satisfaction were measured using five-point semantic differential scales (Ni et al., 2022; Busser et al., 2022). A 14-item, five-point Likert scale measuring delight was adapted from Liu and Keh (2015).
Survey data were analyzed using SPSS v29. K-means clustering segmented customers into delight groups to examine behavior across the three journey stages. This method suits the data type and sample size (Arimond and Elfessi, 2001) and has been widely used to analyze user group differences (Eibl et al., 2024) and segment customers in tourism and hospitality (Konu et al., 2020; Liu et al., 2016; Sánchez-Rivero et al., 2023). ANOVA tests showed all items significantly predicted cluster membership (F-values > critical value, p < 0.05; see Table 4). Chi-square and ANOVA tests further identified significant differences between delight segments at each stage (Pallant, 2020).
Appendix shows the frequencies for the inspiration and search, decision factors and post-experience stages. At the inspiration and search stage, the average commitment score was 3.64 (SD = 1.13), indicating some respondent commitment. Commitment levels remained similar in the decision stage (M = 3.80, SD = 1.18). At the post-experience stage, the mean satisfaction score was 3.85 (SD = 1.29). Table 1 presents the mean and SD values for each customer delight item and the total score. A Cronbach’s alpha of 0.934 indicates high reliability of the delight scale.
Delight evaluation among clusters
| Item | Overallmean | SD | Cluster 1: Dissatisfiedseekers, n = 117, low | Cluster 2: Content butcautious, n= 151, medium | Cluster 3: Delighted advocates, n = 332, high | F(ANOVA) | Sig.(ANOVA) |
|---|---|---|---|---|---|---|---|
| The staff seemed interested in helping me | 4.20 | 1.08 | 2.47 | 4.58 | 4.65 | 495.64 | <0.001 |
| They were really helpful and polite | 4.11 | 1.12 | 2.35 | 4.42 | 4.59 | 456.25 | <0.001 |
| I felt stimulated during the stay | 3.82 | 1.24 | 1.77 | 4.44 | 4.25 | 596.48 | <0.001 |
| The stay was very exciting | 3.80 | 1.22 | 1.74 | 4.32 | 4.29 | 671.67 | <0.001 |
| I felt that I was exceptionally lucky that day | 3.80 | 1.3 | 1.61 | 4.19 | 4.40 | 676.52 | <0.001 |
| My day/s in the hotel is truly a special one | 3.75 | 1.26 | 1.72 | 3.99 | 4.35 | 533.93 | <0.001 |
| The service I received was much more than generally necessary | 3.73 | 1.29 | 1.74 | 3.83 | 4.39 | 459.44 | <0.001 |
| The experience at the hotel was full of wonderful surprises | 3.70 | 1.29 | 1.69 | 4.03 | 4.27 | 428.59 | <0.001 |
| Most services were very satisfying | 3.69 | 1.24 | 1.74 | 4.23 | 4.14 | 451.41 | <0.001 |
| The experience in the hotel was very pleasant | 3.68 | 1.24 | 1.59 | 4.07 | 4.23 | 659.54 | <0.001 |
| The hotel was a pleasant surprise | 3.61 | 1.33 | 1.62 | 3.56 | 4.34 | 469.47 | <0.001 |
| I never thought that I could enjoy a stay at a hotel so much | 3.52 | 1.32 | 1.57 | 3.72 | 4.11 | 350.06 | <0.001 |
| They made me think that I was very important | 3.43 | 1.24 | 1.56 | 3.26 | 4.16 | 524.81 | <0.001 |
| I was treated like royalty | 2.75 | 1.26 | 1.47 | 1.86 | 3.60 | 414.01 | <0.001 |
| Summated mean score | 36.71 | ||||||
3.2 Analytical approach
K-means clustering explored two-, three- and four-cluster solutions to identify consumer segments (Steinley, 2006). This method minimizes Euclidean distances to optimize data partitioning and has been widely used in hospitality to analyze preferences and behaviors (Dolnicar and Grün, 2008). All variables were standardized to ensure comparability. Based on interpretability, variance explained and coherence, a three-cluster model was selected. This model identified low delight (Dissatisfied seekers), medium delight (Content but cautious) and high delight (Delighted advocates) (Table 1).
Cluster 1: Low delight (20% of the sample): “Dissatisfied seekers”
This group showed the lowest level with an average satisfaction score of 1.20. They reported dissatisfaction with personalized service and emotional connection, indicating a need for targeted improvements.
Cluster 2: Medium delight (25% of the sample): “Content but cautious”
With an average score of 2.79, they are generally satisfied but expect improvements in service consistency and speed. Addressing these needs could increase loyalty.
Cluster 3: High delight (55% of the sample): “Delighted advocates”
With an average score of 3.79, they have the highest level of satisfaction. Their expectations for personalized service and attention to detail were found to be aligned. This group is loyal and inclined to advocate for the brand.
4. Results
This section analyzes findings across customer journey stages, highlighting factors affecting behavior and satisfaction. Tables 1–4 show frequencies, means and standard deviations; Tables 2–4 present expected and observed counts. Only significant results are discussed. Adjusted residuals (ARs) from Chi-square tests were used to calculate p-values and identify categories driving cluster differences (MacDonald and Gardner, 2000). Residuals above 1.96 or below −1.96 indicate significant relationships: positive values show over-representation, and negative values indicate under-representation (Mele et al., 2021).
Pre-decision stage
| Cluster 1: Dissatisfied seekers,20% | Cluster 2: Content butcautious 25% | Cluster 3: Delightedadvocates | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Inspiration | Observed (%) | Adjustedresiduals | Observed (%) | Adjustedresiduals | Observed (%) | Adjustedresiduals (AR) | χ2 | p | Cramer’s V |
| Hotel reputation (132) | 7 | 3.7 | 26 | 0.1 | 67 | −3.1 | 18.008 | <0.001 | 0.173 |
| Previous experience of the hotel (130) | 0 | −6.3 | 24 | −0.4 | 76 | 5.4 | 45.478 | <0.001 | 0.275 |
| Previous knowledge of the hotel (n = 59) | 34 | 2.9 | 19 | −1.2 | 47 | −1.3 | 8.796 | <0.001 | 0.121 |
| Recommendation from friends and family (n = 117) | 32 | −4.2 | 25 | 0.4 | 43 | 3 | 15.138 | <0.001 | 0.159 |
| Search for information stage | |||||||||
| Google (or another search engine) (n = 379) | 21 | 1.5 | 27 | 1.5 | 52 | −2.5 | 8.404 | 0.018 | 0.103 |
| Hotel website (n = 229) | 22 | 1.3 | 30 | 2 | 48 | −2.8 | 48.862 | <0.001 | 0.116 |
| Feelings | |||||||||
| Happy (n = 196) | 1 | −8 | 29 | 1.5 | 70 | 5 | 63.909 | <0.001 | 0.326 |
| Sure (n = 151) | 2 | −6.3 | 29 | 1.3 | 69 | 3.9 | 39.69 | <0.001 | 0.257 |
| Pleased (n = 111) | 8 | −3.4 | 32 | 1.7 | 60 | 1.2 | 11.878 | 0.003 | 0.141 |
| Respected (n = 68) | 3 | −3.7 | 37 | 2.3 | 60 | 0.9 | 15.224 | <0.001 | 0.159 |
| Joyful (n = 65) | 8 | −2.5 | 26 | 0.2 | 66 | 1.9 | 6.783 | 0.034 | 0.106 |
| Unsure (n = 65) | 68 | 10.4 | 15 | −1.9 | 17 | −6.6 | 109.031 | <0.001 | 0. 426 |
| Worried (n = 60) | 73 | 11.1 | 15 | −1.9 | 12 | −7.2 | 124.789 | <0.001 | 0.456 |
| Loved (n = 46) | 2 | −3.1 | 33 | 1.2 | 65 | 1.4 | 9.645 | 0.008 | 0.127 |
| None of these feelings (n = 39) | 56 | 6 | 13 | −1.8 | 31 | −3.2 | 36.216 | <0.001 | 0.246 |
Decision stage
| Cluster 1: Dissatisfied seekers, 20% | Cluster 2: Content but cautious, 25% | Cluster 3: Delightedadvocates, 55% | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Selection factors | Observed (%) | Adjusted residuals | Observed (%) | Adjusted residuals | Observed (%) | Adjustedresiduals | χ2 | p | Cramer’s V |
| Price (n = 167) | 26 | 2.4 | 30 | 1.9 | 44 | −3.6 | 12.935 | 0.002 | 0.147 |
| Local character of the hotel (n = 44) | 2 | −3 | 23 | −0.4 | 75 | 2.7 | 10.658 | 0.005 | 0.133 |
| Feelings | |||||||||
| Sure (n = 252) | 0 | −10.1 | 33 | 3.5 | 67 | 4.9 | 101.5 | <0.001 | 0.411 |
| Happy (n = 128) | 4 | −5 | 30 | 1.3 | 66 | 2.8 | 25.217 | <0.001 | 0.205 |
| Pleased (n = 118) | 1 | −5.7 | 31 | 1.7 | 68 | 3 | 32.567 | <0.001 | 0.233 |
| Proud (n = 73) | 3 | −3.9 | 27 | 0.5 | 70 | 2.7 | 15.308 | <0.001 | 0.16 |
| Worried (n = 65) | 80 | 13 | 8 | −3.4 | 12 | −7.4 | 170.07 | <0.001 | 0.532 |
| Unsure (n = 45) | 84 | 11.4 | 11 | −2.3 | 0 | −7.1 | 131.81 | <0.001 | 0.4 69 |
| None of these (n = 32) | 65 | −4.3 | 16 | −1.3 | 19 | −4.3 | 46.279 | <0.001 | 0.278 |
| Loved (n = 23) | 0 | −2.4 | 39 | 1.6 | 61 | 0.5 | 6.649 | 0.036 | 0.105 |
4.1 Inspiration and search
Chi-square analysis (Table 2) showed that hotel reputation influenced Dissatisfied seekers more (AR 3.7), but less so for Delighted advocates. Previous experience also differed: Dissatisfied seekers did not rely on it (AR −6.3), while Delighted advocates valued it (AR 5.4). Recommendations from family and friends mattered more to Delighted advocates (AR 3.0) and less to Dissatisfied seekers (AR −4.2). Prior knowledge of the hotel was more important to Dissatisfied seekers (AR 2.9), highlighting the role of expectation-setting. Differences also emerged in online search behavior: Delighted advocates used search engines (AR −2.5) and hotel websites (AR −2.8) less, while Content but cautious consumers relied more on hotel websites (AR 2).
Emotional experiences differed notably across clusters. Delighted advocates felt “happy” and “sure,” Dissatisfied seekers felt “unsure,” “worried” and “less happy,” while Content but cautious customers felt more “respected.”
A one-way ANOVA showed a significant difference in commitment during the inspiration and search stage among the three groups [F(2, 597) = 169.379, p < 0.001]. Tukey’s HSD tests indicated higher commitment in Delighted advocates (M = 4.02, SD = 0.83) and Content but cautious (M = 3.89, SD = 0.84) than in Dissatisfied seekers (M = 2.26, SD = 1.16). The effect size (eta squared) was 0.36.
4.2 Decision-making stage
In the decision-making stage, consumers evaluated options based on specific attributes (Table 3). Price was more critical for Dissatisfied seekers (AR 2.4) and less important for Delighted advocates (AR −3.6). Local character mattered more to Delighted advocates (AR 2.7) and less to Dissatisfied seekers (AR −3.0).
Emotional responses also varied: Delighted advocates felt “happy,” “sure,” “pleased” and “proud”; Content but cautious felt more “sure”; and Dissatisfied seekers felt “unsure” and “worried.”
ANOVA revealed significant differences in commitment [F(2, 597) = 306.846, p < 0.001], with a large effect size (0.507). Delighted advocates (M = 4.27, SD = 0.76) and Content but cautious (M = 4.10, SD = 0.81) showed higher commitment than Dissatisfied seekers (M = 2.10, SD = 1.03).
4.3 Post-experience
In the post-consumption stage, satisfaction was shaped by tangible and intangible factors (Table 4). Delighted advocates valued originality (AR 2.9), prestige (AR 2.8) and exclusive atmosphere (AR 2.3). Dissatisfied seekers prioritized room size (AR 3.3), location (AR 2.7) and price (AR 2.6).
Post-experience stage
| Cluster 1: Dissatisfiedseekers, 20% | Cluster 2: Contentbut cautious, 25% | Cluster 3: Delighted advocates, 55% | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Selection factors | Observed (%) | Adjustedresiduals | Observed (%) | Adjustedresiduals | Observed (%) | Adjustedresiduals | χ2 | p | Cramer’s V |
| Positive aspects | |||||||||
| Location (n = 223) | 25 | 2.7 | 28 | 1.3 | 47 | −3.3 | 11.925 | 0.003 | 0.141 |
| Originality (n =194) | 12 | −3 | 24 | −0.6 | 64 | 2.9 | 11.531 | 0.003 | 0.139 |
| Price (n =187) | 25 | 2.6 | 27 | 0.6 | 48 | −2.6 | 8.509 | 0.014 | 0.119 |
| Prestige of the hotel (n =135) | 10 | −3 | 24 | −0.4 | 66 | 2.8 | 11.126 | 0.004 | 0.136 |
| Exclusive atmosphere (n =82) | 6 | −3.3 | 27 | 0.4 | 67 | 2.3 | 11.219 | 0.004 | 0.137 |
| Size of room (n =44) | 39 | 3.3 | 20 | −0.7 | 41 | −2 | 11.121 | 0.004 | 0.136 |
| Negative aspects | |||||||||
| Breakfast (n =91) | 33 | 3.5 | 24 | −0.2 | 43 | −2.6 | 13.036 | 0.001 | 0.147 |
| Parking (n =90) | 5 | −3.9 | 33 | 1.9 | 62 | 1.4 | 16.022 | <0.001 | 0.163 |
| Restaurant (n =88) | 47 | 6.9 | 19 | −1.4 | 34 | −4.3 | 48.624 | <0.001 | 0.285 |
| International atmosphere (n =56) | 9 | −2.1 | 20 | −1 | 71 | 2.5 | 7.18 | 0.028 | 0.109 |
| Price (n =53) | 38 | 3.5 | 28 | 0.6 | 34 | −3.3 | 14.94 | <0.001 | 0.158 |
| Atmosphere in terms of other clients (n =40) | 58 | 6.3 | 14 | −1.5 | 28 | −3.7 | 39.497 | <0.001 | 0.257 |
| Feelings | |||||||||
| Sure (n =250) | 1 | −9.8 | 34 | 4.4 | 65 | 3.9 | 98.314 | <0.001 | 0.405 |
| Happy (n =109) | 1 | −5.4 | 22 | −0.8 | 77 | 5 | 35.479 | <0.001 | 0.243 |
| Unsure (n =47) | 100 | 14.5 | 0 | −4.1 | 0 | −7.9 | 210.516 | <0.001 | 0.592 |
| Pleased (n =43) | 0 | −3.3 | 30 | 0.8 | 70 | 2 | 11.249 | 0.004 | 0.137 |
| Worried (n =36) | 100 | 12.6 | 0 | −3.6 | 0 | −6.9 | 158.101 | <0.001 | 0.513 |
| Understood (n =33) | 0 | −2.9 | 45 | 2.8 | 55 | −0.1 | 12.525 | 0.002 | 0.144 |
Regarding negatives, Delighted advocates noted the international atmosphere (AR 2.5), while Dissatisfied seekers rated the restaurant (AR 6.9), other guests’ atmosphere (AR 6.3), price (AR 3.5) and breakfast (AR 3.5) more negatively. Emotionally, Delighted advocates and Content but cautious felt more “sure,” while Dissatisfied seekers felt more “worried” and “unsure.”
Post-purchase satisfaction was strongly linked to customer delight. ANOVA showed significant differences among groups [F(2, 597) = 781.528, p < 0.001], with a large effect size (0.780). Delighted advocates reported the highest satisfaction (M = 4.45, SD = 0.55), followed by Content but cautious (M = 4.30, SD = 0.62), while Dissatisfied seekers had the lowest satisfaction (M = 1.53, SD = 0.75).
5. Discussion
This study segmented the luxury hotel market by delight and examined how factors and emotions differ across the customer journey. Table 5 presents the characteristics of each segment, including respondent profiles and journey details.
Characteristics and customer journey for each delight segment
| Dissatisfied seekers | Content but cautious | Delighted advocates | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | Education | SE status | Stayed with | Gender | Education | SE status | Stayed with | Gender | Education | SE status | Stayed with |
| Female 31% | Higher education 17% | Medium 23% | Family 30% Couple 24% | Female 39% | Secondary school 1% | Medium 13% | Family 26% | Female 51%, Male 49% | Secondary school 2% | Medium-low 0% | Family 33% |
| Male 69% | Post-graduate 23% | Medium-high 61% | Friends 22% | Male 61% | Higher education 13% | Medium-high 60% | Friends 23% | Higher education 21% | Medium 14% | Friends 21% | |
| High 16% | Alone 12% | College or university 59% | High 28% | Work colleagues 18% | College or university 57% | Medium – High 59% | Couple 20% | ||||
| Work colleagues 10% | Post-graduate 27% | Couple 15% | Post-graduate 20% | High 27% | Work Colleagues 12% | ||||||
| Organized trip 2% | Alone 13% | Alone 7% | |||||||||
| Organized trip 5% | Organized trip 7% | ||||||||||
| Stages | Inspiration and search | Decision-making | Post-experience | Stages | Inspiration and search | Decision-making | Post-experience | Stages | Inspiration and search | Decision-making | Post-experience |
| Main factors | Hotel reputation | Price | Location | Main factors | Hotel website | Main factors | Previous experience of the hotel | Local character of hotel | Originality | ||
| Previous knowledge of the hotel | Price | Recommendations from friends and family | Prestige of the hotel | ||||||||
| Size of room | Exclusive atmosphere | ||||||||||
| Emotions | Unsure | Unsure | Unsure | Emotions | Respected | Sure | Sure | Emotions | Happy | Happy | Happy |
| Worried | Worried | Worried | Sure | Sure | Sure | ||||||
| Pleased | Pleased | ||||||||||
| Proud | |||||||||||
| Commitment | Low | Low | Commitment | Medium | High | Commitment | High | High | |||
| Satisfaction | Low | Satisfaction | High | Satisfaction | High | ||||||
The findings show that external validation, prior experience and emotions shape decision-making and post-consumption experiences, supporting research on social proof and experiential memory (Wahba, 2024). Delighted advocates relied on past experiences and social proof from friends and family, while Dissatisfied seekers focused more on hotel reputation. This underscores the importance of strong brand reputation and consistent guest experiences for building trust and loyalty (Yaghi, 2024).
Information search behavior is dynamic (Mieli, 2023) and varied across segments. Delighted advocates were confident, conducting fewer online searches, while Content but cautious consumers sought more reassurance via hotel websites. This reflects different cognitive strategies (Kahneman, 2011). Emotionally, Delighted advocates felt “happy,” “sure” and “pleased,” whereas Dissatisfied seekers felt uncertain and worried, highlighting the impact of positive emotions on satisfaction and commitment (Gao and Zhang, 2025). ANOVA confirmed greater decisiveness among Delighted advocates and Content but cautious consumers. For managers, providing engaging, reassuring content early can boost satisfaction and reduce uncertainty, suggesting hotels should tailor digital content to build emotional confidence and trust (Kim et al., 2012).
In the decision-making stage, price was crucial for Dissatisfied seekers, while Delighted advocates valued the hotel’s local character. This supports segmenting consumers by value orientation: cost-conscious customers prioritize financial factors, while satisfied guests seek authentic, hedonic experiences (Kim and Fesenmaier, 2015). Emotionally, Delighted advocates felt pride and pleasure, whereas Dissatisfied seekers remained uncertain. Trust-building strategies such as personalized recommendations, flexible policies and transparent pricing can boost confidence among Dissatisfied seekers.
In the post-consumption phase, satisfaction depended on both tangible and intangible factors. Delighted advocates valued prestige, originality and exclusivity, highlighting the importance of premium experiences. In contrast, Dissatisfied seekers prioritized location, price and room size. Their negative views on breakfast, restaurant quality and atmosphere emphasize the need for consistent service. Empowering employees to apply trust-building and recovery strategies can strengthen emotional bonds at this stage (Hyun and Kim, 2012).
6. Conclusions
This study offers valuable insights into consumer behavior across the customer journey. By segmenting consumers into Dissatisfied seekers, Content but cautious and Delighted advocates, it highlights differences in decision factors and emotional states affecting all stages. Emotional segmentation allows hotels to optimize resources and tailor services, enhancing satisfaction, fostering loyalty and creating sustainable competitive advantage.
Dissatisfied seekers are consumers whose high expectations were unmet, leading to emotional disengagement. Consistent service and proactive recovery are essential to improve their experiences. Content but cautious consumers balance practical and emotional factors, showing moderate satisfaction and conditional loyalty – presenting an opportunity to convert them into loyal advocates through consistent service, personalized touches and loyalty offers. Delighted advocates represent the ideal segment, with high satisfaction and strong brand advocacy. They value personalized, emotionally resonant experiences that foster repeat visits and word-of-mouth. Maintaining their loyalty requires continuous innovation, personalized enhancements and efforts to make them feel valued (Mele et al., 2024).
6.1 Theoretical contributions
This study challenges the notion that satisfaction is a static concept, demonstrating that guest experiences are multidimensional and dynamic and that satisfaction plays a critical role in loyalty and recommendation behavior. Satisfaction has been defined as an important factor influencing future consumption behavior, and this study contributes to the literature (Kim et al., 2013; Oliver, 2014). Segmenting guests into segments based on their satisfaction levels using K-means clustering empirically reveals the dynamic structure of emotional attachment and its strong relationship with loyalty. The emphasis on transition from satisfaction to satisfaction and exceeding expectations questions traditional models that focus on functional quality and highlights the importance of psychological and emotional dimensions in consumer behavior.
This study builds on the consumer journey understanding in the luxury hotel industry (Kandampully et al., 2018) by segmenting customers according to their satisfaction levels in the pre-decision, decision-making and post-consumption stages. While existing models focus on rational factors such as price and convenience, this research emphasizes the importance of emotional and social factors and provides rational, emotional and demographic profiles for each segment. The use of K-means clustering enhances the method’s rigor by identifying segments that exhibit different behavioral patterns and emotional responses. This approach contributes to a deeper understanding of consumer behavior beyond aggregate analysis in the luxury hotel industry and provides a replicable framework for future studies in the service sector.
6.2 Practical contributions
These findings provide actionable insights for hospitality managers to enhance guest satisfaction and loyalty. By segmenting guests into three categories and analyzing rational and emotional factors at each stage, hotels can deliver more strategic and personalized service.
For Dissatisfied seekers, proactive service recovery and real-time feedback are vital, as they are highly sensitive to unmet expectations. Staff should detect early dissatisfaction and respond empathetically; tools such as in-room surveys help resolve issues quickly. Post-stay follow-ups that acknowledge feedback and outline improvements rebuild trust and encourage positive word-of-mouth.
Content but cautious consumers value consistency and reliability; any service deviation impacts them. Clear pre-arrival communication and personalized offers (e.g. upgrades, curated experiences) enhance perceived value. Targeted loyalty programs and exclusive incentives drive repeat bookings and deepen loyalty.
Delighted advocates should receive exclusive benefits, personalized amenities and structured advocacy programs (e.g. referrals, social media). Personalized welcomes and unique experiences reinforce their status, while public recognition further strengthens loyalty, turning them into brand ambassadors.
6.3 Limitations and future research directions
This study focused on luxury hotels in Istanbul, which may limit generalizability. Future research should replicate it in other regions and hotel categories to examine cultural differences and broader applicability. Longitudinal studies could explore how emotional engagement changes over multiple stays. Further research might also test delight-driven segmentation in other segments, such as mid-range and budget hotels or alternative accommodations.
As digitalization transforms hospitality, technologies such as AI, virtual reality previews and chatbots may strongly shape consumer expectations and experiences. The impact of these technologies on consumer delight deserves further study. Additionally, post-consumption engagement – such as user-generated content, reviews and advocacy – warrants exploration. Future research could examine how delighted consumers influence others through digital word-of-mouth and social media. By integrating technology and expanding delight-based segmentation, future studies can deepen understanding of emotional engagement in hospitality.
This study is supported by REMODEL project which is funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101079203. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
