How hoteliers will predict future bookings and consumer demands?

  • Published by Ozgur Tore

Hotel Reception2The answer is “Big Data” and “machine learning”. These two words are buzzwords in the travel industry.

But users want big data to take the next step, to move beyond collection and aggregation of data from multiple sources to insight and analysis of what the future holds. This forward-looking aspect of data is crucial to the ability of hoteliers to take action on what they learn in order to grow profitable revenue.

Here is what we know. Today’s hotel consumer can visit between 20-30 different websites using multiple devices before making a purchase. Depending on the nature of the trip, consumers like to take their time and compare availability, prices and value before booking. Each step of the way the potential guest is leaving a digital footprint that outlines which direction will most likely be taken to achieve the desired result, in this case, booking a hotel.

What if we could “see” that data not just after the fact but during the process and start training ourselves to predict future behavior? Let’s take that one step further, what if we not only foresee what actions might be taken but also influence and guide the final outcome to your advantage?

Every day millions of data points are placed on the Internet while consumers shop online and when aggregated and analyzed can paint a clear picture of the path to purchase. This is a goldmine of opportunity and revenue potential for any hotelier who sees what others may not.

Data from a hotel website can show us valuable information such as if a consumer has abandoned a search, stopped during a complicated checkout, ran into technical errors or appears to want to do more research before stepping up to the cash register. Looking beyond an isolated hotel website expands the view and the potential to consumers we have not yet engaged.

This full market perspective lets us see how consumers shop that hotel and its competitors. What hotels are consumers shopping? What hotels are they booking? Where are consumers from and what do they look like? Having this knowledge, we are able to understand consumer demand as well as pricing influence, and how it can be addressed, corrected and presented in a way to drive conversion.

big data learning machineThis is where big data + machine learning comes in.

Machine Learning refers to a system of algorithms that reveal patterns hidden in data. More data, a.k.a. big data, means more effective learning. That is why it’s good to look beyond your own organization’s data when you want to understand consumers. Hotels need to go beyond their website data and their own internal systems, including the CRM and PMS, to understand the true dynamics of the marketplace. Looking at consumer behavior across all third-party travel websites where 87% of consumers shop for travel is a good place to start.

Predictive Analytics is using machine learning, specifically the trends and indications, to predict future outcomes. By predicting what will happen in the future, predictive analytics provide the foundation for the remedy – or action – that should be taken. Predictive analytics are best when interpreted in the context of the industry. When your data scientist has a foundation in travel, assessments are smarter and conclusions are more effective. Always look for the people that interpret your analytics to have an understanding of the industry, what influences the market and how to translate the analytics into action that will increase your hotel bookings, RevPAR and/or occupancy.

Predictive analytics can take many forms, including:

  • What are the top geographic markets searching for your hotel for future arrival dates?
  • What persona types are the top consumers booking your competitors?
  • How are consumers responding to your future rate compared to your competing hotels?
  • Does market demand support your premium price over the next 30 days? 60 days?

There are a plethora of choices facing consumers today and competition is fierce within the hotel industry. Understanding the past is essential, however the ability to predict the future is quickly becoming a necessity when it comes to both capturing and shifting market share.

It is imperative that future data analysis and insights are used during your daily yield management meetings and honing this practice will provide an increase in RevPAR and guarantee everyone on the team understands their future goals.

nSight’s SaaS-based solution with target market reporting provides these insights and more for today’s hotelier. Hotels can try nSight data with complimentary benchmark reports comparing shopping on your hotel website with your shops on OTAs. Click here to learn more.

Source: Big Data + Machine Learning Equals Predicting Future Outcomes for Hoteliers



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