“How was your stay?”: Exploring the Use of Robots for Gathering Customer Feedback in the Hospitality Industry

Authors: Michael Jae-Yoon Chung
Abstract: This paper presents four exploratory studies of the potential use of robots for gathering customer feedback in the hospitality industry. To account for the viewpoints of both hotels and guests, we administered need finding interviews at five hotels and an online survey concerning hotel guest experiences with 60 participants. We then conducted the two deployment studies based on deploying software prototypes for Savioke Relay robots we designed to collect customer feedback: (i) a hotel deployment study (three hotels over three months) to explore the feasibility of robot use for gathering customer feedback as well as issues such deployment might pose and (ii) a hotel kitchen deployment study (at Savioke headquarters over three weeks) to explore the role of different robot behaviors (mobility and social attributes) in gathering feedback and understand the customers’ thought process in the context that they experience a service. We found that hotels want to collect customer feedback in real-time to disseminate positive feedback immediately and to respond to unhappy customers while they are still on-site. Guests want to inform the hotel staff about their experiences without compromising their convenience and privacy. We also found that the robot users, e.g. hotel staff, use their domain knowledge to increase the response rate to customer feedback surveys at the hotels. Finally, environmental factors, such as robot’s location in the building influenced customer response rates more than altering the behaviors of the robot collecting the feedback.

Transcript

1. “How was your stay?”: Exploring the Use of Robots for Gathering Feedback in the Hospitality Industry Michael Jae-Yoon Chung and Maya Cakmak University of Washington
3. Research Questions 1. Can we use robots to gather feedback from guests? 2. How should we design robotic systems to gather better customer feedback?
4. How was your stay? A. Need finding interviews at 5 hotels B. Online guest scenario survey (n=60) C. Deployment studies 1. At 3 hotels (~4 months) 2. At 1 company kitchen (1 month) Approach
5. Need Finding: Results Image source: https://www.ihg.com/crowneplaza/hotels/gb/en/milpitas/sfomp/hoteldetail/photos-tours • = Disseminate widely • = Respond immediately
6. • 60 participants from Amazon MTurk • A positive or negative scenario • 4 solicitation conditions ◦ 5-point Likert scale question ◦ Open-ended question Online Guest Scenario Survey
7. “A feedback robot? That's just weird, the world isn't ready for that.” “I wouldn't trust the robot.”
8. “It sounds easy and kind of cool.” “It is too rude to not respond to this cute robot.”
9. Online Survey: Results • Perception of the robot solicitations • (In)convenience • Privacy issues “Less likely, but I’d feel the staff might be watching over my shoulder.” “Checking out. I just want to leave and get home.”
10. Video
11. Breakfast Room Survey • Prototyped with UX designer & hotel manager • Deployed for 129 days • 229 min, 43 responses on avg per day • Passive observations & follow-up interviews during 2 visit days
12. elevator
13. Hotel Deployment: Results • Feasibility • Environmental factors • Privacy issue
15. • 3 times a week for 3 weeks ◦ Monday Breakfast (Mon BF) ◦ Monday Lunch (Mon LU) ◦ Wednesday Breakfast (Wed BF) • 3 behaviors ◦ Default ◦ Social ◦ Mobile Kitchen Deployment
16. Kitchen Deployment • Video recorded ◦ Annotated & analyzed • Customer opinion questionnaire ◦ Sent after each session ◦ 4 5-point scale Likert questions ◦ 1 open-ended question
17. Response Rates Robot Behavior Week # Mon LU Meal Type WED LU
18. • Among the dissatisfied customers • Mixed responses Change in Customer Perception of the Service “It seems good to know that I can provide input, whether it’s good or not, to make things better next time.” “No confirmation on where the feedback is going, how it would be actionable.” “The food is what matters.”
19. Design Recommendations • “Middle-man” • Simple and fun interaction • Respect the guest privacy first • UI for customizing behavior by the hotel • Follow-up or explain
20. Limitations • Need finding: small number of hotels • Online survey: small number of conditions • Deployment study: inevitable bias • Results unlikely to generalize
21. Thank you! • Mixed-methods • Two stakeholders • In-the-wild study • Demod feasibility • Design guidelines Summary