Case Study: Traveloka

When the Indonesian tech unicorn Traveloka developed its first mobile travel booking app in 2014, they were faced with the primary challenge of converting their desktop users to mobile. They needed a way to measure their most effective marketing channels, but they also prized having a centralized integration with all their activities in one place.

We needed a way to measure all of our marketing channels without having to put more SDKs in the app which could jeopardize the user experience [crashes]. We also wanted to connect directly to Facebook and Google and scale with ad networks and vendors efficiently.

Following a comprehensive review of the ecosystem and the many players in it, Traveloka chose to work with Adjust and integrated in 2015.

As Traveloka offer travel booking services on both a desktop and mobile, they needed to see the full user journey right from the start, this meant capturing as much relevant data as possible and syncing this to their own internal database.

• Increase conversion rates through personalized offers across platforms
• Negotiate with publishers and networks and increase overall campaign efficiency
• Scale quickly and profitably

Many of our clients couldn’t work without callbacks. Adjust’s callback data is made up of all app user data available from the time of install to actually triggering an in-app event like a product search or actual booking. Once set-up, these callbacks are typically sent directly to a dedicated server owned by the app publisher themselves so they can collect and store everything in a way that suits them. Like many of our clients, Traveloka starting syncing Adjust callback data immediately. They could then use this granular app user data as a source of truth, cross-checking other data sources and fuelling business decisions across departments.

We do deep-dive analysis and user journey modeling in-house by leveraging the attribution data we're getting from Adjust callbacks and other sources. This means we are able to marry different touchpoints of
a user's journey - across mobile and desktop - when creating marketing programs and we can be generous and flexible with our models. It's what has driven our efficiency, and we've seen an increase of 30% by having a more complete view of our users.

Personalization is the key driver to increase conversion and revenue metrics. With travel booking, showing the right offers to the people most likely to buy at the optimal time can be the make-or-break of the business.

Once Traveloka had started running and measuring their marketing campaigns through Adjust, they began to create custom audiences using the callback data they were receiving from Adjust. According to eMarketer, the average cost to acquire a user who makes an in- app purchase has skyrocketed to more than $60. And with more than 90% of users churning before 90 days, companies who don’t target effectively could be losing out. But being able to effectively segment audiences reduces the cost associated with churn. Traveloka created different audiences based on their campaign goals, either retargeting existing users with personalized ads or creating lookalike audiences based on the ‘best’ users.

We used internal data modeling to understand which events would lead a user to purchase or would make a purchase that much more likely. We then used callback data to create audiences based on these events, and these were synced to Google, Facebook, and many other networks. Callback data gave us what we needed to hyper-target our marketing efforts and find new users.

With several network partners sending actionable cost data to Adjust, Traveloka could leverage the cost data they received in their callbacks to create custom payout models. This allowed them to be more flexible when scaling beyond Facebook and Google, and also helped them become more efficient when negotiating terms.

It was important for us to improve the cost of marketing performance. We used the cost and attribution data to help identify partners that could drive good quality installs and engagements with scale. Overall, we improved our cost of performance by over 25%.