The Next Frontier for Predictive Analytics in Travel and Hospitality
Written by GoodData Author |
Technology is enabling hotels, airlines, car rental companies, cruise lines, online travel agents (OTAs) and quick serve restaurants (QSRs) to deliver delightful experiences to their customers. It is also allowing these companies to monetize their data, particularly in the unseen middle layer of the travel and hospitality industry. This middle layer is comprised of merchants, franchise owners, and sales partners; in other words, the ones who are so critical in supporting sales, marketing, distribution, and operations.
So how can more companies in this vast global ecosystem use their big data to impact their bottom line? The newest technologies go well beyond the magic of simply sharing or reporting the data. In this post, we’ll explore examples of how to monetize that data with more advanced and predictive analytics. Let’s dive into three examples of common industry segments: hotels, QSRs and airlines.
Hotels: Checking In to the Power of Predictive Analytics
Using predictive analytics can help general managers pre-empt a costly dip in customer satisfaction. In the hotel business, there is a direct correlation between guest experience and guest satisfaction. While corporate brands may control the marketing and brand standards, the ultimate test is what the guest experiences at the individual property, which is largely managed by a franchisee.
Over the years, hotels have become very good at soliciting customer feedback through sophisticated survey tools. In today's digital era, hotels also receive a lot of unsolicited feedback on their properties and guest experience from social media and an array of online rating websites. That’s a lot of data, from a lot of sources.
As expected, guest satisfaction is dependent on how well the hotel is running its operations. However, the guest satisfaction data does not provide a direct linkage to what the hotel needs to change to get to higher guest satisfaction – is it the front desk staff, the way the staff interacts with the guests or something about the property? However, if you connect the data you have about your hotel operations to the guest satisfaction data, you can predict the impact of changes in operations on guest satisfaction. A large hotel chain found that it could increase the guest satisfaction by 10% by ensuring that it has a lower turnover among its front desk staff. A similar set of gems to improve guest satisfaction are there to be discovered, if we bring these disparate data together.
QSRs: Predict Customer Behavior Here, There, and Everywhere
QSRs can bring in more revenue at the individual store level and at the corporate brand level when predictive capabilities are applied to the data they are already producing.
For QSRs, there are many moving parts in each location. There are fluctuations in staff and foot traffic, changes in supply chain, responses to marketing offers, and other elements of daily business. Nearly all of these variances represent a larger trend, such as seasonal effects or changes in consumer behavior. While the franchisees are busy executing and ensuring customer satisfaction, corporate brands can help their franchise partners by letting them foresee and understand these variances. This enables the franchisees to proactively use the levers in their control to make better business decisions.
Let’s take the example of a burger chain. While the franchisee would know the information about sales at their outlet, it would be far more powerful to know that the marketing campaign they just participated in will result in a 8% increase in foot traffic in the next few weeks. With this predictive information, they can be prepared with inventory levels and staff to meet that spike in demand.
Airlines: Apply Analytics. Decrease Financial Turbulence.
Predictive analytics can help the airline ecosystem move more inventory faster—and sometimes at higher prices. A typical airline has a wide variety of sales and distribution partners, including large OTAs like Expedia and Orbitz, smaller regional OTAs, metasearch partners like Google and Kayak, corporate travel bureaus, domestic and international travel agents, and direct corporate clients.
All airlines have revenue management systems to handle the financials with their partners, but this shouldn’t limit their interactions. There is a great opportunity to align everyone in the relationship (airlines and all of the partners, regardless of who they are) with a broader view towards airline-partner interactions. Everyone in the partnership can be far more productive and engaged when they have access to a holistic view of airline information regarding bookings, traveler data, seasonality of demand, price trends, marketing metrics, sales metrics (such as number of inquiries), etc.
When combined together with a built-in predictive analytics layer, this wealth of information can be a game changer for both airlines and their partners. Trends will appear more clearly across partners, channels, and markets. Airlines can inform their partners of expected changes in advance and tap their respective resources to fill seats with less discounting.
Making the Power of Predictive More Accessible
These are just three examples that GoodData and Absolutdata see for the travel and hospitality industry. These examples may seem out of reach to some, but they are very do-able. You have the data. In fact, you have a lot of it. You just need to put it to work. But how?
The first step is to gather all that data in one place. This is a challenge, but there are new tools that make this daunting process surprisingly easy. The second step is to apply advanced analytics to increase revenue, margin and share—and even to create new revenue streams.
Predictive analytics aren’t simply a fancy new tool, available to only a fortunate few in the travel and hospitality industry. It’s our view that they should be – and will be – available to everyone. With predictive analytics in place, the travel and hospitality industry is poised to reach a new level of efficiency and productivity.
Written by GoodData Author |
Subscribe to our newsletter
Get your dose of interesting facts on analytics in your inbox every month.Subscribe