Leveraging Self-Serve Analytics to Drive Growth: A Case Study on Kahoot!
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Education
Applicable Functions
- Quality Assurance
Use Cases
- Time Sensitive Networking
- Usage-Based Insurance
Services
- Data Science Services
- Training
About The Customer
Kahoot! is a global platform where users can create, share, and play learning games or trivia quizzes. Founded in 2012 as a research project at the Norwegian University of Science and Technology, the company has grown to serve more than 550,000 paying users. In 2020, more than 250 million games were played on Kahoot!, with over 1.5 billion participating players in 200 countries. The company is headquartered in Oslo, with offices in the U.S. and throughout Europe. Kahoot! is used by half of all teachers and students in the United States and 97% of Fortune 500 companies for various purposes including training, onboarding, presentations, and events.
The Challenge
Kahoot!, a platform for creating, sharing, and playing learning games or trivia quizzes, has experienced significant growth since its inception in 2012. With over 550,000 paying users and more than 1.5 billion participating players in 200 countries, the company faced the challenge of effectively managing and utilizing its vast product usage data. Despite having adopted Amplitude, a product intelligence platform, Kahoot! was only using a few functionalities and tracking minimal events. The company was suffering from a classic bottleneck where all data requests had to go through the data analysts. This situation was not sustainable given the company's growth and the increasing need for data-driven decision making across different departments.
The Solution
To address this challenge, Kahoot! focused on improving its data governance and promoting self-service analytics. The company set clear naming conventions, made unnecessary information invisible for the end user, and considered which properties had different values and needed to be merged. This groundwork ensured that the product analytics data could be trusted and that the end user didn’t need a background in data science to understand it. Kahoot! also invested time in creating self-service analytics that allowed people across the company to use product data for analysis by themselves. With Amplitude, engineers, PMs, and marketers could understand how users used the product or how the features were performing without having to rely on the data team for everything. The company also incorporated Amplitude into training for new hires to ensure everyone could effectively use the platform.
Operational Impact
Quantitative Benefit
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