4 Ways Data Orchestration is Transforming Travel & Hospitality

85% of consumers say being treated like a person, not a number, is important to them. 57% of consumers state they would share personal data if they were to get personalized offers. The problem? Effectively using data is still a significant barrier to success for many. Travel and hospitality, however, are ahead of the curve. In this blog, we’ll explore the 4 ways travel is orchestrating data to improve customer experiences (CX) and increase conversions. But before diving into our examples, let’s define data orchestration. Data orchestration is a process that:

  1. Connects disparate data sources across channels and devices
  2. Organizes data from these touch points to create 360 customer views
  3. Improves audience understanding by continually feeding data collected from campaigns back into your now centralized segments

In short, data orchestration breaks down siloed data so brands can more easily understand customer behavior and use it to create campaigns and websites that drive action. Now that you get the gist, let’s get started.

1. Improved Customer Experience & Service

Tourism has been on the rise globally, with the industry accounting for more than 1/10th of the world’s GDP and contributing $2.3 trillion to the U.S. economy in 2016. Additionally, digital innovation in general is what has propelled travel sales to grow from $500 billion in 2015 to an expected $800+ billion by 2020.

That said, digital innovation doesn’t mean robots showing you to your room or drones bringing you a poolside margarita (yet). No, it means using audience data to provide seamless customer experiences and personalized messaging. To do this, travel brands must mix, match, and merge their data to gain 360° customer views—helping improve loyalty, drive conversions, and boost revenue.

Wideroe, a Norwegian airline, explains that their 360° audience views allow agents to interpret an individual’s customer service history alongside website visits and promo email engagement. Because of this centralized customer view, Wideroe’s representatives can recommend the most appropriate ancillary services to drive revenue or offer complimentary upgrades based on an individual’s search history to ensure more personalized customer experiences.

2. Better Recommendations

With advances in machine learning and custom-built algorithms, travel companies must invest in recommendation engines to increase opportunities for cross-selling. By collecting information from travelers’ recent searches, companies can create a recommendation algorithm to offer suggestions for add-ons to their next trip or emails with discounted flights to destinations they prefer.

After analyzing thousands of data points, Expedia discovered that people search about 48 times across travel sites before booking a flight. Expedia’s new flight recommendation engine simplifies this process by analyzing customer booking patterns to provide more relevant searches with a list of alternative routes and the ability to compare query results with past searches.

3. Advanced UX Personalization

With the right orchestration technology, data can impact travel recommendations and create dynamic UX personalization as well. According to Salesforce 75% of consumers agree they expect companies to provide a consistent experience wherever they engage, be it social media, mobile, on-site, or in-store.

Take for example United Airlines, who uses customer actions and historical data (like previous purchases and travel destinations) to make changes to their website, landing pages, web copy, and on-screen layouts to reach specific consumer segments.

United does the above by assessing more than 150 variables about a customer, in real-time, to deliver dynamic personalization of their website in about 200 milliseconds based on the individual. According to United’s vice president of e-commerce and merchandising, Scott Wilson, this data-driven, customer-centric strategy has lead to a 15% increase year-over-year in ancillary revenue.

4. Price Optimization & New Revenue Streams

Predictive analytics is another huge asset for the travel industry. Using a combination of large data sets and custom-built algorithms, travel brands can create dynamic pricing for reservations based on customer booking patterns, the weather, average daily rates, and more.

Southwest Airlines, for example, uses aggregated customer data to promote their services, products, and featured offers across channels and devices to reach the right traveler.  Dan Landson, a company spokesperson, explains how the airline leveraged customer behavior data to increase revenue, “We look at the city pairs that are being searched to help us determine what type of service we should offer on a specific route.”  In doing so, Southwest’s customer and loyalty segments have grown year-over-year, with Landson attributing that growth to intelligent, data-based targeting.

A Shining Example in Airbnb

From stellar customer service to personalization, Airbnb leverages data to continuously strengthen its relationship with guests and hosts. According to Airbnb, consumer preferences are broken down into four categories:

  • The Behavioral Aspect: How an Airbnb user interacts with their site online
  • Dimensional Factor: The device used, language, and location preference
  • Sentiment: Their reviews, survey results, and ratings
  • Imputed: Sorts location preference of traveler, i.e. city vs. local town

By layering the above types of data, Airbnb can improve the types of travel recommendations it provides when customers search their platform, including factors like proximity to attractions, types of amenities, and more.

Airbnb uses their 11 petabytes of data to help hosts as well. Through machine learning, Airbnb lets hosts know which calendar dates are likely to be booked at their current price as well as which dates may not be booked, so they can receive more dynamic pricing suggestions. This, in turn, helps hosts understand when to make changes to their pricing around big events (think, SXSW in Austin or New Year’s Eve in NYC) and can also help them earn more money in the long run.

Airbnb also conducts “experiments,” or, A/B tests, on their product to ensure constant improvement. That said, it can be difficult to tell the exact impact of a particular product change since, as Neil Patel explains, there are several aspects of how Airbnb works that “make it more involved than simply changing the color of a button and measuring what happens.” Patel continues,

Users can browse Airbnb whether they’re logged in or not. This can make it a challenge to tie actions to a particular user. It’s also possible for them to browse on their mobile device, then come home and complete the booking process on their home computer. Furthermore, a successful booking may depend on the guest’s request and how responsive the host is–things that are beyond Airbnb’s control.

Data isn’t a magic Band Aid that will solve all your brand’s problems. Even Airbnb, struggles with optimizing for things like cross-channel browsing or host responsiveness. But, using data to provide stand out customer experiences can get easier as long as you have the right tools to streamline your data and analyze it holistically.

How Lineate Can Help

According to McKinsey, travel companies have a 23x higher chance of customer acquisition if their strategy is data-driven. Lineate’s data orchestration platform, DataSwitch, helps travel brands like Classic Journeys and more centralize disparate data sources to gain 360 audience insight and launch highly-targeted campaigns across every channel. The result? An ability to understand who your customers are, where they come from, and how to reach them at the right time and place. To see how data orchestration can take your marketing strategy to new heights, schedule a DataSwitch demo now.

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