The personalization of travel: Connecting human and digital experience through data and machine learning by Jasmin Schmidt-Stiebitz

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Jasmin Schmidt-Stiebitz
Head of Data Product Steering (Personalized interactions)
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Personalization of Travel by Lufthansa Group

Welcome to an informative session led by Jasmine Schmitz, the head of data product steering at Lufthansa Group. She illuminates how Lufthansa, a leading German airline, personalizes travel experiences for their customers by leveraging data and implementing cutting-edge techniques.

About Jasmine Schmitz and Lufthansa Group Digital Hunger

Jasmine has been a part of the Lufthansa Group for five years now, contributing to various departments. Her current role involves steering the data product for personalization to create an optimal journey for customers across digital, human, and physical touchpoints. The Digital Hunger entity within the Lufthansa Group, which Jasmine helped establish, unites all departments and teams that focus on digital interactions for customers across affiliated airlines, like Austrian Airlines, Lufthansa Airlines, Swiss Airlines, and Brussels Airlines.

Data Enhanced Customer Interactions

In 2022, Lufthansa served over 100 million passengers, emphasizing the vast potential of personalized interactions. The ultimate goal is to create satisfied and loyal customers by providing a connected experience that combines physical features, human interactions, and digital solutions. To achieve this, Lufthansa largely relies on data-enhanced customer interactions, which Jasmine explains in detail.

Personalization on Digital Touchpoints

Netflix's personalized movie recommendations and Amazon's product suggestions are excellent examples of digital touchpoints. In a similar vein, Lufthansa employs machine learning mechanisms on digital touchpoints to stimulate customer inspiration for new destinations and offer relevant products. Jasmine provided two examples of how machine learning is currently applied.

Destination Inspiration

In the first example, a machine learning model uses customer location and timing data to suggest potential travel destinations. This model currently treats people located in the same city similarly. To make it more customer-specific, it will soon consider individual characteristics and personal interests to make the recommendations more accurate and targeted.

Pre-flight Email Recommendations

The second example revolves around an ancillary recommendation sent via email during the pre-flight stage. This model predicts the interest of each customer in different items and sorts them based on that prediction, pushing the most relevant content to the customer. The same model is applied to both emails and webpage touchpoints to ensure a streamlined experience. Current efforts are being carried out to fine-tune this model.

Human Interactions Onboard

Jasmine also elucidated how data about customers is brought to crew members to personalize interactions, especially on board, despite the challenging digital environment. One intriguing feature is a scoring system that predicts customer satisfaction levels, allowing cabin crew members to address any discontent proactively and improve the experience.

The Role of Customer Profiles in Personalization

A crucial element of personalizing a customer's journey is having access to a detailed customer profile. With customer permission, Lufthansa captures and analyzes data over time to improve its scoring methods. Besides predicting dissatisfaction, another scoring example involves calculating a customer's potential lifetime value, i.e., future spending behavior. This data helps enhance internal steering, making it possible to offer special treatment to high-value customers.

The Future of Personalized Experiences at Lufthansa

In the continuous quest to enrich customer experiences, Lufthansa is exploring ways to incorporate customer preferences and requirements into their profile, such as dietary preferences and language of communication. While physical preferences may not be as crucial for digital interfaces, it becomes invaluable for human touchpoints like cabin crew or service centers.

Active ideation efforts, including staff interviews, customer surveys, and workshops, thrive to identify more relevant data points to personalize the human experience further.

Conclusion

From utilizing machine learning to predicting passenger needs, Lufthansa is pioneering the personalized travel experience. There's an ongoing focus on optimizing methods and models to ensure customers enjoy a seamless, connected journey. Measuring these impacts is also a priority. Even though quantifying this impact becomes complex due to various influencing factors, attribution logic helps realize an uplift in sales due to tailored product offerings.

Though challenges exist, the future of personalized experiences at Lufthansa Group promisingly hints at improvements in app usage and providing for specific customer groups such as elderly and differently-abled passengers. If you have any ideas or thoughts, feel free to share and contribute to the continuous enhancement of personalized travel experiences.


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