5 Things You Need To Understand About Data Science

By Jordan Hollander

Last updated January 26, 2022

6 min read

Revenue Management Systems
Revenue Management

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It takes years of study and multiple degrees to become an expert in data science – time that hotel industry professionals don’t have. But, understanding the basics of data science can increase profit at your property with very little studying required. The latest hotel technology, like a smart revenue management system, makes data science accessible – and even easy – to hotel owners and revenue managers, no matter what your level of expertise.

This guide will take you through the elements of data science that apply to running your property more profitably, starting with a quick overview of what data science actually is. We’ll show you how a revenue management tool can utilize machine learning to make the more technical aspects of data science easy for your managers. Read on to learn how to use data science to take advantage of customer data, pricing trends, and industry data to increase profit. 

 

What is Data Science?

Data science is the practice of extracting information from data. Data science involves analyzing large amounts of data through programming and data mining to uncover useful insights and intelligence for an organization.

There are five commonly accepted phases to the data science life cycle. They are: 

  1. Capture: data is acquired or extracted (for example, Google Analytics logs the number of unique visitors to your hotel website); 

  2. Maintain: data is cleaned and stored in a data warehouse (Google Analytics stores the number of unique visitors over five years); 

  3. Process: data is classified, modeled, clustered and/or summarized (Google Analytics can reorganize unique visitors by time of day, geographic location, and referral source);

  4. Analyze: through predictive analysis, regression, qualitative analysis, or other (Google Analytics can tell you where your bounce rate might be causing you to lose unique visitors); 

  5. Communicate: insights are reported and used for business intelligence and decision making (Google Analytics reporting can tell you whether a marketing campaign is adding unique visitors to your website)

Data science is an increasingly popular field due to the rise of big data. Big data describes the large volume of structured and unstructured data that a business collects every day. Data scientists analyze big data in order to provide hotels with insights to beat competitors, learn about their customers, and run targeted marketing and pricing campaigns. 

 

What Does a Data Scientist Do?

If the description of data science sounds broad, that’s because it is. Data scientists perform a wide range of data-related tasks, from “optimizing Google search rankings and LinkedIn recommendations to influencing the headlines Buzzfeed editors run,” writes Harvard Business Review.

Generally speaking, data scientists go through this process to analyze big data: 

“First, data scientists lay a solid data foundation in order to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth. Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions. In other words, in tech, data science is about infrastructure, testing, machine learning for decision making, and data products.”

Data scientists are in high demand. LinkedIn ranked data scientist as “the most promising job of 2019.” Data scientist topped Glassdoor’s list of the ten best jobs in America. Data from Indeed’s job openings show data scientists can earn between $86,000 and $123,000 per year.

Often, the terms “data scientist” and “data analyst” get used interchangeably, but these roles are slightly different. A data scientist focuses on creating the questions; a data analyst focuses on answering an existing set of questions. For instance, a data scientist tries to estimate the unknown with statistical models and predictive analytics. They mine existing data points from your hotel’s CMS, PMS, marketing campaigns, and more to shed light on areas like customer behavior, operational efficiency, pricing, and demand forecasting. A data analyst, on the other hand, might dive into big data to answer a specific question, such as “why did my RevPAR drop in the third quarter?” Analysts are more focused on solving problems than data scientists. 

 

How is Data Science Different from Machine Learning and Data Analytics?

Things get even more complicated when you add the field of machine learning into the mix. Machine learning deploys algorithms to extract data and to forecast future trends. Machine learning is a subset of data science; the rise of big data means that data scientists can’t efficiently manipulate data sets by hand anymore. Machine learning processes data sets autonomously so that data scientists can focus on the bigger picture.

Machine learning can be seen in everyday life; Netflix uses machine learning to recommend new shows and movies based on your viewing history. Facebook uses machine learning to predict interests, recommend friends, and notify you of potential pages to follow based on user behavioral data. Amazon uses machine learning to recommend products based on your browsing and purchase history.

Machine learning also plays a big role in the hospitality industry, specifically in advanced revenue management systems. An RMS like IDeaS G3 utilizes machine learning with statistical methods to produce cutting-edge forecasting and decision optimization. IDeaS’ algorithm factors in data from competitor rates, search penetration, booking trends, and optimization scores to power a continuous pricing model. The tool can automate many of the tasks a data scientist would have had to perform manually; the pricing model pulls information to continuously update pricing decisions based on the latest information. 

 

How Can Hotel Groups Leverage Data Science?

Smart pricing is just one instance where data science can make a big difference in your profit margin. Yield management is a similar process that involves the use of dynamic pricing to control profitability around fixed inventory supply. Yield management is tricky: set your rates too high, and demand drops. Set your rates too high, and you sacrifice revenue for volume. IDeAS’ can overcome these challenges by using machine learning to forecast demand. The tool looks at patterns in historic data from your properties and the market at large. It then forecasts demand for smarter rate recommendations, which can be automatically applied in real-time to keep inventory priced optimally. “IDeaS revenue management systems do more than determine the best price, they also provide hotels with the power to yield and analytically price by room type and determine the most profitable group business to accept,” writes one reviewer.

Data science can also drive smarter customer segmentation and marketing automation. A typical marketing tool like Mailchimp can’t automate segmentation – meaning the platform can’t decide when is the best time to send an email to a specific guest category. Only a platform that uses data science can factor in existing data points, such as recency, frequency, monetary value, and length of stay, to time the right message to the right audience perfectly. “For example, if a hotel targeted guests who would likely take advantage of spa services, golf and restaurants, rather than guests who only generate room nights, they could significantly increase revenues and profitability. Unfortunately, money often gets spent on blanket campaigns that don’t target individual guests or segments with offers they’re most likely to respond to,” writes one industry analyst.

Getting more targeted with data science helped Starwood Hotels determine how to get the best value out of translation services for its property websites worldwide. Starwood turned to data scientists to determine if it was worthwhile to spend time and resources translating some of their branded websites into certain languages. They used a model that weighed revenue over two years versus the full on-going cost of translation at the market level. The formula delivered huge business results – up to 97% more revenue, according to Skift.

Data mined from your website can be used to improve conversion rates by experimenting across a range of user experience variables, as Skift’s example conclusively proves. A data science approach can successfully improve conversion rate optimization. Through experimentation, a manager can learn which metrics to influence to test different landing page configurations, analyze the data, and repeat. Test each aspect of your website experience, from the headline to the size of the “Book Now” button, to make sure every design detail is primed for conversion.

Finally, market intelligence data, from competitors’ RevPAR, occupancy rates, and average daily rates from STR to event data and rate parity data, is critical to optimize how you price your rooms. Rate parity is the difference between prices quoted on a hotel’s branded website compared to prices quoted by an OTA. This data set is often obscured by non-contracted OTAs and other third parties. Rate shopping tools serve to mitigate this issue by showing how your direct competitors are pricing their rooms. 

 

You Don’t Need to be a Data Scientist to Embrace Data Science at Your Hotel

Being a data scientist takes years of training. Instead, IDeaS revenue solutions can give you immediate strategic advice and tech tools to improve your revenue management. Learn from their team of data scientists and use their software to perform analyses that would otherwise take a Ph.D. to accomplish. Here are just a few of the features a revenue management software can offer: 

  • Demand-based pricing by room type: when demand for your suites rises, automatically increase the price of a suite without also increasing the price on a double room.

  • Virtual Revenue Management Service: get an assigned  industry revenue expert to work directly your staff to accelerate your revenue strategy

  • Continuous pricing: the tool mines search penetration, competitor rates, booking trends, and reputation scores, to forecast demand, continuously updating as new data comes in 

  • Rate publishing: consistently and accurately update rates across channels to achieve the highest booking value at the lowest acquisition costs, with no manual updating 

  • Work with limited data: if your data sets are limited, IDeaS can clone data from existing hotels to provide baselines for demand and predict guest behavior.

As a hotelier, it’s important to understand the basics of data science. Leave the actual data science up to a firm like IDeaS with dedicated data science teams that can deliver solutions for all of your data needs.