Case Study: FLO
As the e-commerce branch of Ziylan, FLO is worth €45M, employs 120 people, and growing fast. Focused on expanding the business online and in-app, the team aims to reach as many new and existing customers as possible.
FLO was looking to scale up and improve on the processes they already had in place. The areas they identified as needing improvement were in measurement, attribution, and mapping out a customer journey.
The team had originally set their attribution window to seven days, meaning that any purchases made within those seven days were being attributed to the install campaign that brought them to the app in the first place. But users were typically making purchases between days three and five after seeing a retargeting campaign, reentering the app and converting.
This wasn’t being tracked, however, because the purchase was still within the initial seven days and was therefore being attributed to the install.
"What Adjust provides means we can see events, trackers, and the performance of different channels has been game-changing."
They faced similar issues with other performance channels as well, particularly with realtime bidding (RTB). As a result of these measurement issues, FLO allocated less budget to engagement campaigns and focused on getting more installs because they weren’t able to confidently factor-in the effect of these campaigns on driving users to purchase.
This is where Adjust came into the picture.
FLO utilized two key Adjust product’s to solve their problems - Measure and Audience Builder. Measure helped them to understand the sources that their users were coming from, providing all necessary insight into each customer’s journey. Having each user tied to the ad they interact with gives full transparency for performance across multiple advertising channels - but all in one place. With Audience builder, FLO no longer needed to gather data from multiple sources to untangle reattribution as it allowed them to define audiences and act immediately on them.
Adjust enabled the team to set the attribution window to one day and use the last click method. For example, if a user makes a purchase on day one, it will be attributed to the channel that drove the initial install. If the user comes back to the app through retargeting channels, however, FLO can now accurately attribute the click/conversion to RTB or an engagement campaign.
Deep linking, or links that send users directly to an app instead of a website or a store, has also helped FLO to more accurately assess the performance of shopping campaigns. After setting up a strong deep linking structure, they’ve been able to maximize revenue potential for various campaigns, including those that run on Google Shopping and Facebook. A user sees the ad, clicks it, is taken directly to the app, then to the product, thereby generating more revenue and showing a direct line of sale in the dataset.
"Adjust provides us with data that we can trust to be 100% accurate, which allows us to allocate budget to where the revenue is actually coming from and to make smarter investments. There’s no longer any guesswork or unnecessary time spent trying to make sense of multiple data sets”
After changing the attribution window, the team at FLO has also made significant changes to budget allocation. More budget is now channeled into engagement campaigns, where they’ve experienced an increase in revenue.
Since working with Adjust, FLO has increased their:
- Overall revenue in-app by 45%
- Budget by 30%, through informed data decisions
- Daily transactions by 33%
- Total number of sessions in-app by 47%
"There’s no alternative to Adjust in measuring ROI, multiple channel performance, and naturally, how to determine our budget allocation. I feel that Adjust’s experience in this field is unmatched. It’s the Google Analytics of the app world”