Solving Data‑Driven Full‑Link Technical Challenges: A Case Study of the Kai Shu Storytelling App
This article analyzes the technical difficulties of building a data‑driven full‑link system and describes how the Kai Shu Storytelling app overcame them by adopting DataFinder for automated event tracking, metric management, and growth analysis, offering practical guidance for enterprises and developers.
In 2014, former CCTV host Kai Shu founded Beijing Kaisheng Cultural Media Co., Ltd., launching the Kai Shu Storytelling app in 2016; it now exceeds 60 million users and 14.5 billion plays, creating a complex data network that needed clarification.
The article first examines the technical challenges of data‑driven full‑link systems, including information silos, low‑quality data, and cultural resistance, and then details specific pain points in data collection (event tracking design, validation, consistency) and metric system construction (definition, production, consumption).
After completing data collection and metric setup, suitable analysis and attribution models are required to discover growth opportunities and enable precise operations.
To address these issues, the Kai Shu app underwent a major refactor in April 2022, rewriting its core code and adopting Volcano Engine’s DataFinder platform. DataFinder automated the entire event‑tracking lifecycle, improved data quality, reduced verification time from days to a few hours, and provided multiple analysis models for rapid problem identification.
The adoption of DataFinder significantly boosted development efficiency, alleviated data‑warehouse pressure, and opened possibilities for further collaboration with Volcano Engine’s cloud data products.
Developers and chief data officers interested in data‑driven full‑link solutions are invited to watch the Volcano Engine Data Intelligence Summit on September 2, where the full suite of cloud data products will be showcased.
DataFunTalk
Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
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