Customer is the king, and the hotels that really care for their guests can’t agree more. Respectable places pay a lot of attention to the thorough analysis of client feedback and draw conclusions. But it is difficult to track and count all the reviews, especially if a facility is fairly large. Each hotel is a complex system which consists of reception, catering, bar, interior, gym, stuff, etc. It’s not always easy to process all the comments to find out which aspect drags the place down, or, on the contrary, helps it to stand out.
Can machine learning cope with feedback management?
To gather visitors’ opinions is one thing, to identify the exact commentators’ target is another. What exactly did your guests like about stay in your hotel: food, room service, friendly stuff or maybe all of these aspects? Too much time is wasted to find it out manually. So, why not to employ machine learning systems to handle the problem?
A smart sentiment and opinion analysis system uses databases of words and phrases that help to sort the remarks to matching sections (laundry, stuff, interior etc.) and figure out clients’ assessment. Thanks to such valuable assistance, hotels get all the necessary information on a silver platter and can immediately move into action improving their facilities.
Advantages of automated data processing
• Quick access to your customers feedback. When a guest leaves a remark on a site, the feedback is processed in a split second and is added to statistics. The data appears at your fingertips almost immediately.
• Valuable information from the horse’s mouth. No one can provide a better insight into hotel services than a guest. Everything may look fine: plates are polished, rooms are cleaned and a hospitable receptionist is meeting guests in the hall. But something always eludes from the eyes. Often oversights and drawbacks can be identified only from guests’ comments.
• Cut on investments. Automated system is the cheapest employee you’ll ever have. You pay for the system just once to further spend on resource-intensive support only.
Disadvantages of this method
• Customers reviews may be quite subjective. Our emotions too often take over the voice of reason making our opinion biased. An application is unable to evaluate the level of objectivity.
• The system processes fake reviews that are written by competitors as real clients reviews. Is this remark written by your guest or someone trying to hurt your reputation? The system performs a linguistic analysis, but it can’t identify whether a comment was written by a disappointed traveler or a tricky business rival. Checking the credibility of client’s opinion lies primarily on your shoulders. On the other hand, it is usually easy to tell if a feedback is fake.
• You will still have to process a lot of information manually. AI is widely employed nowadays, but it is still not perfect, and failures still can’t be totally eliminated. Besides, Al isn’t almighty. Some data has to be entered manually.
Opinion and sentiment analysis in action
Tripadvisor.com aggregates information about 2250 hotels from all over the world. More than 1 million written reviews are analyzed by the means of sentiment analysis that reveals the opinion on hidden aspects, such as the hotel’s service and location. Aspect ratings are often not literally visible, because the reviewers are only asked to give an overall rating. Using latent aspect rating analysis (LARA), the hidden aspects are extracted, and it becomes clear what exactly the reviewer was commenting about.
Sentiment and opinion analysis, while not being flawless, allows hotels to identify the advantages and drawbacks of their establishments and not to fall back on expensive researches. Rapid access to feedback from a reliable source helps to spot a problem and get down to the solution immediately. It is better for hotel owners to turn to the specialists in custom software development domain, who will provide quality BI consulting services and create a product that will meet each business owner’s needs.