Data, information, facts – whatever you choose to call it – collecting and analysing it has become a crucial part of how we understand the world, taking us well beyond what our “gut instincts” were ever capable of.
NB: This is an article from InTouchData
And as our ability to store and process large amounts of data has become faster, easier and cheaper, so too has our potential for insight. With this, individual businesses right through to entire industries are adopting business intelligence (BI) tools to take advantage of all the available information.
Darwin said it – the harsh reality is that the pace of change is ever-increasing and the organisations that do not adapt, will be left behind.
Fortunately, hotels already have the data – loads of it – but we often witness them fall into the trap of underestimating its true potential. They get caught up in the time, money and effort it is going to cost them to introduce new tools and processes, failing to recognise the return on investment that’s on offer.
The irony is, an effective BI tool is designed to make accessing and analysing this information simpler and less time-consuming. At its simplest, BI reduces the workload of producing reports by consolidating the plethora of data sources into an easy to digest format. At its finest, hotels can leverage big data to identify trends, derive insight and gain the necessary intelligence to respond to opportunities and improve decision making.
Take these examples of BI tools in action.
MANAGE OPERATIONS
A hotel has identified a correlation between checkout time and F&B spend. They discovered that by extending the checkout time from 10 a.m. to 12 p.m., guests were more likely to consume breakfast. Of course, this had the potential to create operational issues as the rooms still needed to be cleaned for arriving guests. This was overcome, again, by using data-driven insights to better manage the housekeeping function. The result was more F&B revenue plus efficiency gains in the housekeeping department.
CAUSE AND EFFECT
A hotel compares airline passenger information to diagnose the reason certain international markets appear to have slowed. There is sufficient granularity in the airline data to understand the correlation between arriving guests (at the destination) and the number of rooms sold in the hotel, to guests from a specific destination. Further examination of forward booking patterns highlighted a dip in passenger numbers, relative to the hotel’s diagnosis. Rather than devising a plan to “recapture” the loss, the hotel understands it is a market-related issue rather than a hotel issue and can plan accordingly.
INCREASE F&B
A hotel seeking to increase F&B revenue tries to identify the most profitable mix at a restaurant table (i.e. a couple, a family of four, a party of six, etc.). From the available data, specific groups of guests can be analysed based on spending patterns. One of the takeaways was the influence of alcohol consumption on the total food bill for certain groups, allowing staff to better manage with a table-by-table strategy.
Of course, there is no “one-size-fits-all” hack to using your data; it is always going to depend on the hotel, the types of guests and the context. However, what these examples demonstrate is the potential to make better use of data to drive incremental improvements to revenue.
