How a Travel Club Gets Personal to Boost Sales


How a travel club used customer segmentation to personalize its customer targeting and improve customer satisfaction.

Name of Organization: Voyage Privé

Industry: Travel

Location: Aix-en-Provence, France

Business Opportunity or Challenge Encountered:

For online retailers, customer satisfaction is key to staying ahead of competition. To provide customers with the best possible experience, companies must shift their outbound marketing strategy from static segmentation to highly personalized recommendations, based on personal unique patterns.

For Voyage Privé, a members-only travel club, the challenge was to create personalized offer displays to boost transaction value and improve customer satisfaction. Known as a boutique vacation retailer, it was critical for the company to be able to offer travel options that were appropriate for members. In terms of data analysis, this meant expanding the range of customer signals that could be captured and analyzed.

Voyage Privé, which offers deals on four and five-star hotels, started operations in France and now has more than 25 million members worldwide, with operations in both Belgium and Switzerland, and offices in France, UK, Spain, Italy, and Poland.

Voyage Privé sought to use smart user segmentation to create personalized offer displays on their website. The company required a system that could capture and make sense of large amounts of data, develop effective customer segmentation, and implement an entirely new non-rule-based approach for analyzing incoming and historical data. From a marketing standpoint, the goal was to increase customer satisfaction by providing users with personalized offer selections while simultaneously boosting the total transaction value by customer.

How This Business Opportunity or Challenge Was Met:

Voyage Privé took the first step towards understanding their customers by implementing Dataiku Data Science Studio (DSS). The strategy started with establishing a mechanism for collecting data from customers’ online behavior, such as click paths and bookmarking. With the collected data, the focus shifted to creating a machine learning-derived score for each customer — essentially a value that reflected the likelihood of members pursuing specific travel offers.

The process empowered the company’s teams to collaboratively work together on specific types of data before merging it. Its drag-and-drop interface simplified data diagnostics while facilitating the iteration process. Ultimately, Dataiku DSS helped the company’s IT teams to develop a machine-learning approach to address customer data. This coupling of online behavioral data and tailored offer selections enabled Voyage Privé to automatically present relevant buying opportunities that had the highest likelihood of customer acceptance.

To implement this project, Voyage Privé’s technologists leveraged technologies including Python, HP Vertica, Impala, and used gradient boosting tree and logistic regression models. It took three months, two data scientists, and a CTO to deploy the first version of the application.

Measurable/Quantifiable and “Soft” Benefits from This Initiative

Armed with Dataiku DSS and a machine-learning analysis methodology, Voyage Privé can now optimize their marketing and sales campaigns based on a precise customer segmentation. The entire process has resulted in several competitive advantages, such as a six-percent increase in the total transaction value by unit member; and the complete internalization of the company’s data workforce.

This targeted recommendation system significantly increased Voyage Privé revenue per member and paved the way for many future promising data projects.

Voyage Privé is determined to always make its customers’ lives easier when it comes to travel. To answer this challenge, Dataiku DSS gives us the power to develop a machine learning approach that offers each of our customers a customized selection, says Laurent Dupé, Voyage Privé’s Head of International Marketing. Next steps? Implementing more external datasets to refine their scoring.

(Source: Dataiku)


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