According to the Deloitte report Tourism and Hotel Market Outlook, the future of Australian tourism is under the auspices of a favourable orientation, consistent with the global economic conditions.
Although travel costs are expected to rise due to slight increases in oil price, the downward trajectory of the Australian dollar — 75 cents in 2019, and continuously falling toward 70 cents over the next couple of years — or the completion of hotel projects which will supplement by up to 3.4% room availability particularly in capital cities, will contribute to moderating this adverse trend.
Encouraging as this may sound for hospitality businesses, enthusiasm may be curbed by acknowledgment of the concurrent increase of discerning and demanding travelers, who are expected to claim additional amenities, bespoke services, or elevated customer service.
This brings about significant productivity and cost (resource- and operating-related costs) issues that must be dealt with. The main reason to encourage the use of robotic process automation in the hotel and travel sector is that leveraging RPA in hospitality may be at least part of the solution to solving industry-specific issues like those mentioned above.
As a road opener to understanding why robotic process automation and the hospitality sector are a ‘match made in heaven’, consider scalability, one of the most important cross-industry benefits of RPA. Scalability means that more robots can simply be deployed or re-assigned for a given process whenever the context calls for more volume. This feature is perfectly fit with seasonal variation, which is a trademark of hospitality businesses that can cause undesirable fluctuations in the revenue flow.
Digital transformation is currently positively disrupting the hospitality industry precisely because of its potential to address the growing demands of tech-savvy guests, increase productivity, and allow for auditability, while at the same time obtaining a considerable reduction in process operating costs, without having to make significant adjustments to the existing systems.
We follow the practical approach of our article series, and shed some light on a few concrete robotic process automation use cases in hospitality.
Real world use cases for RPA in hospitality
Software robots can scan customer data in order to make personalised suggestions regarding specific amenities that are most relevant to each customer, be those spa treatments, gym facilities, airport transfer, restaurant reservations, etc. Cognitive automation, understood as a joint venture between RPA and Natural Language Processing (NLP), may be used in the form of conversational chatbots that can ask customers pertinent questions and subsequently provide accurate booking directions in no time.
Booking bots can fulfill reservations or upsell rooms and promotional offers, everything in closest connection to individual preferences. This raises the efficiency of promotional materials, by ensuring that they reach precisely their targeted audience. Automated booking can proceed in a more streamlined fashion, considerably reducing waiting time and thereby enhancing customer satisfaction.
Moreover, the employees can pass on to bots the task of answering frequently asked questions, like “Are there any available rooms?”, or “What time is breakfast?”, and focus on efficiently pursuing their operational jobs. All in all, booking costs are considerably lowered by leveraging the potential of RPA in hospitality.
2. Loyalty processing
Developing customer loyalty by providing unique benefits to customers is a common strategy that hotel managers use to thrive in the hospitality sector, on top of pricing strategies. Economists have shown that a hotel’s favourable image is crucial for promoting customers’ loyalty. Well aligned with such scientific data, software robots can process and verify guests data.
They can then be trained to analyse the trends emergent from data files regarding the parameters which are most relevant for a hotel’s image (e.g., product, place, price). It goes without saying that they can do this much faster and quasi error free compared with humans.
Further along the way, the patterns extracted from the data can be used to ensure consistent marketing policies. So here we have another illustrative case of human — bot collaboration, meant to facilitate access to loyalty reward program information, and thus to enhance productivity.
3. Competitor pricing analysis
Competition in hospitality is fierce, if you only consider the large number of service providers. In order to set the prices that are likely to bring you most revenue, you should first carefully examine how much others charge their customers relative to what they have to offer.
Instead of asking your employees to manually run through the data to do the analysis, it seems wiser to rely on bots’ fast and accurate performance, which is also much cheaper than their human counterparts’. This will lead not only to more efficient pricing and hence more profit, but it will also make your employees more satisfied with their work in agile operating environment.