Which data for personalization? A travel industry overview
In the last decade, the ubiquity of mobile phones and the rise of new tech superpowers like Amazon has completely transformed the way consumers interact with brands. People nowadays have largely accepted the idea that companies collect data about their online activities and, more generally, about their interactions with those firms. In turn, they expect businesses to employ that data to provide enhanced, personalised experiences at all stages of their customer journey.
Indeed, a survey by RetailTouch Points found that 87% of respondents would accept having some details of their activities tracked by brands to get a better customer experience. Coherently, McKinsey found that personalization can boost sales up to 10%, and multiply marketing ROI by eight, while Adobe cites personalization as one of the four pillars of digital transformation.
The travel industry seems to have understood the importance of digital transformation and personalization: 63% of travel brands state they are using data to personalize their website content, and travel marketers are reportedly more likely to personalize display ads than their peers from other verticals. Adobe foresees that digital travel sales will be worth $219 billion by 2021 and half of this value will come from mobile devices.
So far, travel brands have focused on 4 areas of personalization:
behavioral targeting, which consists in presenting relevant personalized content and offers to customers based on their current and past interactions with the brand. It is based on segmentation and aims at increasing the customer experience;
geotargeting, which happens when a brand distributes different content and offers based on the visitor’s location;
technographic targeting, i.e. targeting based on the different features of the technological device that is being used. This includes the hardware, software and settings that the customer has turned on and can increase conversion rates by offering a better customer experience;
weather-based targeting, which means delivering different messages and promotions depending on local temperatures, rain and snow levels, wind…
Nonetheless, personalization and digitalization remain a difficult and critical challenge. While everyone is trying to nail down this change process, not everyone seems to be doing it the right way. Many organizations, especially in the travel industry, are drowning in an overwhelming ocean of data and do not know how to actually sort it out. Understanding what type of data your company needs is the first step to achieve successful personalization.
What data should travel brands focus on for personalization?
Personalization can help brands make every step of the customer journey smoother, more enjoyable and… more profitable. However, data for personalization comes from multiple sources and is often siloed and unstructured. In order to get a clear picture of their target customer, travel brands need to put together data from all different sources and make sense out of it. Data can be classified into 3 groups, based on the type of information it describes:
Preference Data, which includes name, demographics, preferred contact device (mobile, email...), and expressed interests. Brands can use tools like live polls and interactive emails to collect and constantly update this type of data;
Behavioral Data, i.e. data describing a customer’s historical and real-time behavior when interacting with the brand. This category includes a lot of different statistics, such as page views, email opens, click behavior, site abandon data, push notification dismissals or click-throughs, path behavior and more. By providing details about a customer’s engagement, behavior data helps refine the understanding of individual customers to meet their current needs.
However, given how much information can be classified as behavioral data, this type of information might not always be meaningful. Brands must find out which data goes deep enough to provide them insightful information about customers’ engagement levels. More specifically, travel companies should always collect on so-called campaign engagement data, which refers to the actions an individual has taken in response to any of your campaigns across any of your channels;
Present Context Data, i.e. the data generated in real-time during an interaction. This set of data can be further divided into two subcategories: Native Open-Time Data and Live Business Context Data. Native Open-Time Data provides contextual information such as live location and weather or contextual information that is specific to the customer’s device. This type of data allows brands to personalize in real-time the content and offers that each customer is presented. Live Business Context Data, instead, is used to improve the message’s relevance so that customers get up-to-date information. It includes, for instance, airline price changes, the number of rewards points, inventory levels and system event data.
Clearly, without preference data, there would be no room for personalization. However, while every data category is insightful, behavioral data must be put at the heart of any personalization effort as it has the highest potential for effective personalization. Nonetheless, a customer’s behavior data won’t be as useful if it is not contextualized. Contextual data expands the power of behavioral data and actually enables brands to deliver relevant personalised experiences.
Still, we can go deeper in our analysis. The data classification presented above is a useful way to explain what type of information firms should focus on and how. However, by focusing more on the data sources we can actually gain a deeper understanding of what makes a great data set for personalization in the travel industry.
For instance, once a travel brand is collecting deep behavioral and contextual data, it would greatly benefit from looking into its cross-channel (or, even better, omnichannel) data. Cross-channel data is the information that relates to one individual customer but was generated on multiple different devices. It wouldn’t make any sense to treat the same person differently across different channels, especially if he or she is a regular customer. Most travel companies already made their customer service multichannel and are currently working to switch to the omnichannel paradigm, so this is definitely an important step for any company in the travel industry.
Other important sources of data, which should integrate all the data mentioned so far, are the company-specific or database-specific attributes. These are data points which are owned by the specific company in question and can be tied to other data sources, such as customer related management (CRM). From there, travel brands can retrieve information such as a traveler’s loyalty program status or the type of services a customer usually purchases post-ticket-sale.
Finally, travel companies can intelligently use surveys as data collection points for some very specific aspects that couldn’t be quickly inferred from other sources because they are more qualitative. For instance, questions about how customers feel about one particular aspect or subject. While this is not a must-have in a personalization strategy, it can be a good bonus tip, especially in verticals which feature a huge amount of data and need to refine it to make it more insightful, like the travel industry.
Where should brands store their data?
In conclusion, there are a lot of different categories of data and each can contribute differently to a brand’s personalization efforts. Data shouldn’t be siloed, as it would lose value. All the data a business collects should be brought together in order to have a fully integrated customer data set. Data lakes, i.e. flexible, scalable data stores that host data in its natural format, are essential to offering real-time, contextualized personalization.
What was discussed so far relates generally to any company in the travel industry. However, always remember to personalize your personalization. Indeed, personalization is about the relationship between brands and customers, not about data itself. Companies need to understand their unique needs and those of their customers if they actually want to offer a better, more profitable service