Competition in the travel industry is so fierce, even for small margins, that every dime counts and process improvements are implemented quickly. This approach makes technological adoption much quicker compared to other sectors, like healthcare and banking which have stricter rules.
Predictive analytics deals with finding a pattern in data. This could have countless uses, including customizing the travel experience, predicting peaks and lows, implementing dynamic pricing strategies and raising the bottom line overall. Let’s review the top ways in which predictive analytics can make a difference in this oversaturated business.
Personalization and recommendations
The real win of predictive analytics in the hospitality and travel industries is linked to pre-emptive customization. Simply put, the webpage implementing this tries to guess what you need and present you with your answer even before you ask the question. The same goes for the offline environment when hotels try to accommodate your needs judging by past interactions or client profiling.
Recommendation engines are also powered by predictive analytics. Just think of all the possibilities regarding traveling and accommodation for two people going from the US to Europe. If these are the CEOs of two large corporations going on a business trip, it is very different from a scenario involving a honeymooning couple.
The travel industry needs this kind of filtering and sorting of options to avoid information overload which leads to decision paralysis. Although people like to have a few things to choose from, too many alternatives usually result in frustration and even abandonment.
Predictive analytics strives to find those patterns that define the preferences of each client and put the most natural choices first. For example, if you usually book 3-star hotels on your trips, it wouldn’t make sense to show you a 5-star option unless it’s part of great promotion, with a price similar to a 3-star room.
Dynamic pricing
The simple rule of classic economics says that an equilibrium is reached when an offer meets the demand. Luckily, the online environment is the closest to this utopic approach of the free market phenomenon. In a traditional set-up, changing prices for a product based on demand, although it makes sense would be a very unpopular practice. Since online it is much harder to see what other searchers are getting in their window, makes this idea come to life.
Dynamic pricing is already implemented on a large scale on platforms like booking.com and skyscanner.com, which look at the immediate demand for rooms and destinations and aim to maximize sales but also price. It’s a matter of striking a delicate balance between what people are ready to pay for and what is out of their budget, even if they would like it.
Since this is a sector which is heavily influenced by weather and other seasonal factors the prices need to adjust accordingly to create a steady flow of income and protect economic agents from severe variations.
This model can be extended with price alerts and recommendations within a specific budget or timeframe to boost sales by attracting impulse buyers.
Better marketing campaigns
Personalization is not applicable only when the client is already on the website or at the reception desk. In fact, the process starts much earlier, in the leads acquisition phase when a targeted message is delivered to the selected audience, luring them into learning more.
With the help of browser cookies, a company can monitor the activity of potential clients closely and create more tailored offers, neatly wrapped in more attractive narratives. This not only boosts the conversion rates but also decreases the clients’ perception that advertising is intrusive and annoying.
Online marketing is all about conversion. Predictive analytics helps determine ad placement, the frequency it needs to be shown and even the time of day or the day of the week when there are the highest chances to turn a click into a sale. It is a way to replace hours of A/B testing and the work of marketers by simply using real data from the market to calibrate.
Now algorithms are so sophisticated that through sentiment analysis they can match offers with the mood of the potential client. For example, if there are clear indicators of a romantic atmosphere, for instance, posts related to weddings and honeymooning in the social media profiles of the person searching, a well-thought algorithm will suggest romantic destinations or at least couple-appropriate options.
Resource allocation
There is a valuable lesson to be learned from marketing improvement by predictive analysis. It is that through pattern detection that resources can be allocated where they are needed the most, with the highest impact. Since all the possible combinations of factors are too many to be analyzed by humans, it follows that only an algorithm is robust enough to treat the heap of data in the right way to extract value.
As we’ve mentioned in the beginning, since every dollar counts and this is a volume-based business model, making the right decisions can add up consistently. Predictive analytics is just like a personal counselor telling you where you can most likely conduct good business and with whom.
Ready, set, predictive
Adopting predictive analytics solutions will become a matter of survival in the travel industry. In this fast-paced environment, humans are not enough to create a thriving business. The power of machines is necessary to bring incremental changes which accumulate and translate into important business gains.
Companies adopting this will stay competitive by having access to tools which create a more personalized experience. This is a response in line with the overall trend of clients favoring a tailor-made approach to their needs. This is not important only for the last step in the business, the sale, but is in fact a toolset which works from the initial lead attraction phase down the sales funnel.
