- Bernard Marr
How Big Data And Analytics Are Changing Hotels And The Hospitality Industry
The hotel and hospitality sector caters to millions of travellers every day, and each one of them checks in with their own set of expectations. Meeting those expectations is the key to getting people to return, and increasingly hotel and leisure operators are turning to advanced analytics solutions for clues about how to keep their customers happy.
Additionally, although their marketing departments would be loathe admit it, not all guests are equal in the eyes of hotel and leisure operators. Some will simply check in and check out with a minimum of fuss. Others will spend hundreds or thousands of dollars on fine dining, entertainments, sports activities and spa treatments. Identifying those customers with a higher overall lifetime value to a particular business is hugely important in today’s market, but a customer’s lifetime value might not be empirically obvious from observing their behavior during one visit.
For example, a high-rolling customer spending money like it is going out of fashion in the hotel casino may be on a “holiday of a lifetime” following retirement, and unlikely to behave in this way every day. Meanwhile a frugal business customer taking an economy room and spending very little on extra services may be a travelling businessman who will potentially return frequently if the hotel meets his needs, and therefore have a higher lifetime value. Big Data analytics can help make this distinction.
A third overarching use of analytics in the hotel industry revolves around “yield management”. This is the process of ensuring that each room attracts the optimal price – taking into account troughs and peaks in demand throughout the year as well as other factors, such as weather and local events, which can influence the number (and type) of guests checking in.
Analytics has applications in all of these areas and although the hotel and hospitality sector has lagged behind others such as retail and manufacturing in adopting an analytics-first philosophy, that could be starting to change.
One pioneering example included US economy hotel chain Red Roof Inn who, during the record-setting winter of 2013/2014, realized the huge value of having a number of hotels close to major airports at a time when flight cancellation rate was around 3%. This meant around 90,000 passengers were being left stranded every day. The chain’s marketing and analytics team worked together to identify openly available public datasets on weather conditions and flight cancellations. Knowing that most of their customers would use web search on mobile devices to search for nearby accommodation, a targeted marketing campaign was launched, aimed at mobile device users in the geographical areas most likely to be affected. This led to a 10% increase in business in areas where the strategy was deployed.
Another US chain which has been recognized for their innovative use of analytics is Denihan Hospitality, which owns boutique hotels across the US including the James and Affinia Hotels brands. Denihan used IBM analytics technology to bring together transactional and customer data across its chains, and combine it with unstructured data such as customer feedback comments and reviews left on rating sites such as Tripadvisor. Menka Uttamchandani, the company’s vice president of business intelligence, said “Every company has massive amounts of data – it is what one does with that data – such as providing relevant dashboards, click through deep dive actionable reporting and analytical insight that can foster a competitive edge.”
After evaluating customer feedback and transactional data, the chain took strategic, data-driven decisions to rearrange many of their rooms to better cater to either business or leisure travellers, provide more bathroom storage for rooms popular with travelling families, and provide a greater range of in-room facilities such as kitchenettes where guests would appreciate them.
The chain even went as far as putting analytics in the hands of the frontline hotel staff, who were armed with dashboards on their smartphones enabling them to anticipate what a particular guest might expect or desire from their stay, in terms of restaurant meals, concierge services or excursions to local places of interest. Housekeeping staff receive real-time updates on whether customers in a particular room require an extra pillow or are likely to call room service for a sandwich and a coffee at 2am.
Of course as in most industries, a majority of analytical work in the hospitality industry is focused on marketing. The overall aim is often to launch personalized marketing campaigns in the form of email or targeted social media advertising. This involves analyzing all of the information available about customers who are visiting, by gathering customer feedback, transactional activity, use of loyalty programs and bought-in third party demographic data. This is then used to decide whether, for example, an offer of a free restaurant meal, or a ticket for a show at a nearby theatre is more likely to persuade a high lifetime-value customer to make a booking.
At Marriott, however, Big Data is not confined to marketing, and has been put to use across the hotel chain’s operations. Unstructured and semi-structured datasets such as weather reports and local events schedules are used to forecast demand and determine a value for each individual room throughout the year. This enables the chain to set prices with optimum efficiency – vital in an age where customers are used to saving pennies by scanning price comparison services for the best deals.
Starwood Hotels and Resorts, which owns 1,200 hotels around the world, is another large chain which as heavily invested in Big Data and analytics. Their system, too, is based around optimizing room pricing by analyzing data on local and worldwide economic factors, events and weather reports. Knowing how the home weather of their core customer base in North America impacts the price that those customers are willing to spend for a week in the Caribbean sunshine gives them prompts as to the best time to reduce prices or launch marketing promotions. This strategy has led to an increase in its revenue-per-room – a key metric for hotels – of almost 5%.
The hotel and hospitality industry may just be starting out with Big Data but it has an enviable volume and variety of data to work with. Customers leave a data trail from the moment they book to the moment they check out, and analysts are beginning to get to grips with turning that data into actionable insights. Once it gets into its stride, I expect we will see more innovation from this particular sector which should result in more satisfying stays for us as customers.