Big data and analytics can be used to transform companies in any industry. And that’s because no matter what the vertical or the data sets, analytics can find hidden patterns and connections — actionable knowledge that enterprises can use to gain a competitive edge.
Here’s an example of how manufacturers use analytics to reduce costs and downtime, while this post discusses how analytics is being used to turn a perennially unsuccessful professional baseball team into a World Series contender.
Forbes contributor Bernard Marr recently highlighted how big data and analytics are being used to change hotels and the hospitality industry. Not surprisingly, it’s mostly about figuring out what customers want — and how to retain them in the long-term.
Marr writes that “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.”
That’s where analytics comes in. By mining customer demographic and behavioral data, hotels can identify high-value customers and target them with special, personalized offers designed to build brand loyalty over decades.
Another way analytics is paying off for the hospitality industry is through “yield management,” which essentially is asset management, the asset being the optimal value of any room based on variables such as the time of year, special events in the area, and weather.
Among the hospitality companies embracing analytics are Marriott, Starwood Hotels and Resorts, Red Roof Inn and Denihan Hospitality.
This KDnuggets article ticks off a long list of ways that hotels and restaurants can leverage data analytics, including customer segmentation, customer profiling, forecasting, customer relationship management, menu engineering, and more.
The hospitality industry may be later to the data analytics game than the retail, manufacturing or finance industries, but as Marr notes, “that could be starting to change.”