Omnichannel Data Integration
A well-known clothing brand wants to integrate offline retail stores and online private domain data collection. They aim to connect their mini-program, analyze user behavior, and establish a process to attract offline event participants to become online fans and members, enabling marketing tracking.
The challenges faced by the brand include:
1. Data collection: There is a lot of data to collect, and it is difficult to access quickly, accurately, comprehensively, and in detail.
2. Data connectivity: It is difficult to connect cross-platform and cross-scenario data, and users are difficult to identify.
3. Member data: Exporting historical member data is cumbersome and hard to be classified and integrated.
4. Data analysis: After data integration, there are no professional data analysts to conduct post-event analysis.
AIOT: Intelligent AI recognition system and smart display system integration
SDK: Professional SDK for multiple platforms, customizable full-coverage embedding with daily collection of over 2 million triggering events
Analytics: Design of 29 behavioral events, 130+ event properties, 48 membership labels, and 65+ user properties for mini-programs, and four major analysis models
Table: Integration of data visualization, data import, cleaning, analysis, and application
By using these methods, the brand is able to collect and integrate data across different platforms and scenarios, enriching the event properties, membership attributes, and labels, as well as visualizing the data, providing a convenient tool for data analysis.