HOW AN INDEPENDENT APP STORE USING RAKAM TO MAKE DATA-DRIVEN DECISION

Executive Summary

“The game-changing alternative Android app store”

Aptoide is a driven independent App store and their claim is built on years of excellent services and intricate data driven-decisions.

To retain their spot as an industry leader, they needed a much more customizable UI than what they had in Google analytics and Facebook analytics to enable them to draw powerful insights from user experience, and consequently improve their services. That is where our services at Rakam came in.

About Aptoide

Aptoide is an independent Android app store with over 300 million users, 7 billion downloads, 1 million apps, and 1.5 million daily active users. Aptoide is a community-based platform that revolutionizes app distribution and discovery experience through a social environment, tailored recommendations, and the opportunity to create and share your own store.

Aptoide was founded in 2011 in Lisbon, Portugal, as an open-source alternative to Google Play Store. Today, Aptoide is one of the leading players in the world of Apps with an ever-growing community of users and partners, and three offices worldwide with about a hundred employees.

Challenges

• As their data grew at Aptoide, different teams had different user cases and reporting needs that both Google analytics and Facebook analytics could not properly cater for.

“I can say that our teams’ needs outgrew the capabilities of these tools [Google analytics and Facebook analytics] as our data grew. We needed a more customizable interface, preferably a SQL-based one, where we could ask more complex questions to get detailed insights on user behavior.”
Ana Lara Simões – Product Owner of Aptoide App Store

Their built-in features sufficed when Aptoide started, but along the line, it was evident that these tools lacked the customization and personalization required by the teams in the company.

• Alongside their need for customized and personalized features, Aptoide’s technical team needed an open-source event tracking solution: a solution that would allow them to track and load all their customer event data into their Snowflake warehouse.

How We Helped

• When we first met with Aptoide, our expert analysis showed that what their teams needed in the area of customization and personalization was an SQL interface to ask their questions about their data by writing SQL queries. Neither GA nor FA allows its users to interact with data via SQL.

“Rakam BI provided us with a much more customizable UI than what we had in GA or FA, that enabled us to ask complex questions with ease to our data and draw equally powerful insights on how users use our product.“
Ana Lara Simões – Product Owner of Aptoide App Store

By using Rakam API, Aptoide’s technical team started tracking and loading all their customer event data into their Snowflake warehouse. Rakam API offers flexibility and ease in tracking and setting custom events.

• Once they collected all their customer data into their Snowflake data warehouse, they started to use Rakam BI in conjunction with their Snowflake warehouse to analyze their data and create visualizations, reports, and dashboards for stakeholders.

Results

• Within a short period of using our services, Aptoide’s data grew immensely and they reached tens of millions of events daily. Their staff width also expanded to about a hundred employees.
• Today, many teams at Aptoide use Rakam to access all the data they need to make data-driven decisions daily. The Product, Marketing, and Business teams use Rakam’s built-in reporting features to instantly draw the insights they need from their data warehouse without waiting for their technical teams to write SQL queries. The reporting features are segmentation, funnel, retention, and flow.
• Aptoide’s Data teams, who are heavy SQL users, use Rakam’s reporting features for quick analysis and use Rakam’s SQL interface to ask their custom questions via SQL. Their Data teams are also working on the modeling interface to define the metrics (measures & dimensions) that their end-users want to use in their analyses.