Customer Data 101 for Travel Organizations
Data is one of the most powerful tools for an organization in the 21st century. Yet many travel organizations are still not using their available data to their maximum advantage. A commissioned study conducted by Forrester Consulting on behalf of Collinson found that only half of worldwide travel organizations collect a range of customer data on preferences, transactions, and engagement and augment it with third-party sources. This has caused 61 percent of consumers to ignore the majority of communications from travel providers because they simply are not relevant or personalized to their specific interests.
So what is the disconnect that is holding travel organizations back? One of the biggest barriers to “data success” is that the discussion of data collection and the insights that result from it can seem overly complex and filled with industry jargon. Because of this, it’s become a topic that many people shy away from. The first step to becoming “data successful” is to ensure that travel organizations fully understand the power of data and how to make best use of it.
Back to Basics: Data 101
Below are five concepts related to using data to optimize the success of organizations that are often misunderstood or seem to be overcomplicated.
1. First-, Second-, and Third-Party Data
One of the most common forms of data, first-party data, is customer information that an organization collects itself, through its website, sales channels, or by asking customers directly. Second-party data is someone else’s first-party data, usually acquired through a brand partnership. And third-party data is generic data that is usually purchased off-the-shelf from another organization.
When third-party data is paired with first- or second-party data, it helps to build a bigger picture of a particular customer base and their preferences, enabling organizations to offer more personalized customer experiences. As companies continue to seek methods of cutting through competitor noise to deliver more relevant experiences, more partnerships are expected to develop between travel organizations to not only boost sales, but to acquire more second-party data.
Shorthand for “origin and destination,” this form of data shows where a traveler has departed from and where they are traveling to next. This data is extremely valuable because it showcases a greater picture of their customers’ travel habits. As more O&D data becomes available, it’s predicted that more companies will partner with airlines to acquire this second-party data in order to analyze consumer behaviors and deliver targeted propositions.
3. Extract, Transform, and Load (ETL)
ETL is the process of sharing data between systems and companies. An example is when a travel brand looks to market to its customers and ETL from both an e-commerce system and an app. Through this method, an organization will copy text from one document to another and have the option to keep source formatting or comply to the formatting of the new document. From a brand perspective, this helps to build a bigger picture of their customer base. In simpler terms:
- Extraction is the process of picking the data points to incorporate and taking them from one source to the other.
- Transforming is the process of re-organizing or cleaning the extracted data so that it matches the same format your organization uses.
- Loading is the process of incorporating the data into the systems.
While industrial-sounding, ETL is a relatively simple concept. The challenges organizations often face with this data method is ultimately identifying the variety of processes and potential uses of the data. As data becomes more important to companies, data protocols will gradually become standardized in the travel sector to simplify ETL. Additionally, increased computer processing power will help to better streamline this process.
4. Deep Learning or Neural Networks
Often known as a form of AI, deep learning and neural networks are another computer-driven data analysis process that can allow travel organizations to build better predictive models. However, the programmed rules are even more sophisticated and organized in a way that is similar to how humans process information. This means the computer is able to draw conclusions, adapt, and make decisions on how to analyze the data in a nonlinear way.
In the travel space, an example is airports with the development of facial recognition scanners. Organizations may be tempted to leverage this form of data analysis, but it requires a significant investment and in-house technical expertise. Instead, travel organizations should consider what they are trying to achieve with data before they proceed with deep learning or neural networks, since machine learning is a much more viable option.
5. Machine Learning
Machine learning is the process of utilizing computers to quickly and efficiently analyze large amounts of information, revealing correlations between datasets. An example would be an airline wanting to explore the trends between their passengers that fly for both personal and business reasons, with the hope of identifying opportunities for more “bleisure” travel in the future. This specific data process involves a complex series of rules, statistical models, and programmed commands to examine data and identify patterns. Once the data is analyzed, the system then updates its analysis process based on what it finds.
While this term has become more well-known, what many don’t realize is that machine learning is now becoming more accessible to organizations as an off-the-shelf solution. This is a great step forward in the democratization of data analytics as it requires minimal specialist knowledge, enabling greater adoption of machine learning in travel brands and beyond.
Data Knowledge Is Power
Understanding the basics of data and the insights it produces is not a rigorous process. But to create a base of data supporters, travel organizations must be dedicated to instilling this company-wide commitment from the top-down. Travel organizations can improve processes and better utilize the information extracted from data to build a robust view of their customers while providing more personalized experiences. From the products or services suggested to the offers and rewards given, travel organizations will now have the tools to better understand their customers, which can give them a clear leg up on the competition.
Phil Seward is the senior vice president of loyalty strategy for the Americas at Collinson, a global loyalty and benefits company. Combining agency and client-side experience, he is responsible for driving devoted customer relationships for Collinson’s clients across the U.S. and Latin America.