Case Study: ABA English
About ABA English
American & British Academy is an online school specialized in teaching English with a unique methodology based on the principles of a natural approach to language learning. The ABA method brings to the digital space an intuitive and natural method where language is learned by using short films and other audiovisual materials developed specifically for learning English, improving the student’s experience and success rate. Students can learn the English language wherever they are.
The company revolves around different target groups, from students to businesses, and helps them to either learn or improve their English language skills. With more than 12 million active students spanning both desktop and mobile, ABA English is a top choice for learning English, and is active in over 170 countries across the globe. This success has lead them to raise $12 million in growth capital, led by Kennet Partners in 2016. ABA English has aggressive goals for 2017, and plans to double its user base, while continuing to convert the freemium app users to paying users.
How to drive growth by creating in-depth user behavior models
The ABA English team thinks big, and has a tremendous vision for the business. Its strategy has two main directions: to grow the active user base by acquiring new users and to maintain its current user base, while increasing conversions to purchase rates and overall LTV (reducing dropout). ABA English invests a lot in user acquisition campaigns, and as a consequence, worked with a large number of ad networks.
Each network introduced its own conversion SDK and reports. It was hard to analyze the overall performance and success of each campaign since ABA English had to pull separate reports from different networks, combine these together and try and figure out which marketing campaigns (from which networks) were performing the best and bringing in the most value. ABA English needed to track all of its user acquisition channels and marketing campaigns from a single platform, in order to optimize the marketing strategy and allocate ad spend to the best-performing campaigns.
Having a high volume of users using both of its major channels, mobile and the web, ABA English needed deeper insights into the data it was collecting, in order to better understand user behavior patterns and ultimately optimize towards its findings. Why did users tend to drop-out? Which platform is used more and for what reasons? It was also critical for the team to understand the best moment to capture and re-engage users.
In short, the company needed a way to tie together both channels while connecting the dots between desktop-to-mobile and mobile-to-desktop.
There were several reasons why ABA English decided to work with Adjust.
Working with Adjust seemed like the most rational decision. Adjust stands for quality, which is highly important when you think about the most valuable assets they bring to our company—data. Adjust has quickly become a pillar in our decision-making process.
Learn how Aba English worked with partner modules to retarget the right user base in real-time
ABA English started by integrating the Adjust SDK into their apps, tagging and tracking all its own custom in-app events, like registration, login, purchase, and interactions. This allowed ABA English to measure users’ behavior patterns, so it could better segment users and ultimately increase overall return on ad spend. By integrating the Adjust SDK ABA English was able to instantly connect with any network partner without the need to integrate additional SDKs, and track all of their marketing campaign performance in one unified platform, along with all the downstream user events tied back to the marketing source.
Instead of multiple network SDKs, ABA English integrated the Adjust SDK and transferred data to networks via server-to-server integrations. All of their campaigns were then displayed in the Adjust dashboard. After integrating the SDK, ABA English then set up real-time callbacks, streaming all of their raw user data directly into their own BI system. The callbacks they set up also sent back custom user IDs, allowing them to track their user journey on desktop and mobile, giving the company a full picture of users’ journeys across platform. This gave it the data ABA English needed to create personalized experiences, ultimately converting more freemium users into paying users. This integration meant they could monitor all of their traffic sources and identify the best-performing marketing campaigns as well as track retention rate and other key post-install metrics.
Additionally, ABA English made use of Adjust retargeting partner modules, which sends all install and event data in real-time to partners like Criteo and Remerge, creating seamless retargeting campaigns based on real-time data.
Tying desktop to mobile to increase conversion to paying by 43%
Since working with Adjust, ABA English grew tremendously. In just three years, its acquired almost 12 million new users, with the majority of those being on mobile. The company is currently tracking more than 3 million unique users per month in its apps. By tracking marketing campaigns in such detail it has been able to make more informed marketing decisions, cutting campaigns that didn’t deliver them high value users, and investing in those that did. Working with Adjust’s partner modules, all of ABA English’s install data was automatically passed back to partners in real-time — making it possible to retarget high value users and convert them to paying customer. Working with Adjust has meant that ABA English has the data it needs to spend its marketing budget on the right platforms so it can continuously acquire users who net a positive LTV. By analyzing campaign performance ABA has optimized week on week and increased its RPU by 215% and acquisition by 143%. By tying together desktop and mobile data, ABA English can now see the full user journey, which enables the team to target users at the right time, and ultimately has increased conversions to paying users by 43%. The company has done this by building a more comprehensive model of user behavior which in turn has allowed for more targeted ads and user notifications.