Bending Spoons: Leveraging Big Data for App Optimization and Monetization
Technology Category
- Analytics & Modeling - Big Data Analytics
- Cybersecurity & Privacy - Identity & Authentication Management
Applicable Industries
- Cement
- Equipment & Machinery
Applicable Functions
- Sales & Marketing
Use Cases
- Behavior & Emotion Tracking
- Livestock Monitoring
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Bending Spoons is a technology company based in Italy, responsible for over 20 popular apps. The company focuses on delivering an excellent user experience with a data-driven approach. Their portfolio includes the popular app, 30 Day Fitness, and in total, their apps have been downloaded 120 million times. Bending Spoons' business model is built around auto-renewable subscriptions and in-app purchases, with a little advertising revenue. The company uses data science to make decisions about which apps to develop based on potential market reach and monetization possibilities.
The Challenge
Bending Spoons, a Milan-based app developer, was faced with the challenge of standing out in a saturated marketplace where hundreds of apps often offer similar functionalities. The company needed to make data-driven decisions to identify the best apps to develop and optimize them for user experience. Their business model, built around auto-renewable subscriptions and in-app purchases, required a deep understanding of user behavior. However, to make these data-driven choices and grow the business, Bending Spoons needed to analyze large volumes of data very quickly. They were looking for a powerful data storage and analysis system that didn't require specialist technical support. Additionally, as a start-up, they wanted to grow quickly without investing too much in operations and infrastructure, but also needed the capacity to expand rapidly once the business grew.
The Solution
Bending Spoons implemented BigQuery on Google Cloud to provide the analytics power for its internal data tools. This allowed them to analyze usage data and in-app events using their internal Pico tool. They collected data points from advertising platforms and app stores, and used BigQuery to perform hundreds of thousands of queries with this data daily. BigQuery enabled them to aggregate the raw data by segment or type of user and carry out complex queries across these segments. They also used Google Cloud to host some of their apps with Compute Engine and App Engine, and started using Kubernetes Engine to run certain data tasks. Another internal tool connected to BigQuery, called Crystal, collected hundreds of millions of data points from their own apps as well as a lot of external sources, every day, and then fed them to a number of models they've created, doing most of the computational heavy lifting for them.
Operational Impact
Quantitative Benefit
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