How to Build a Universal Data Indicator System for Business Success

Data Empowerment and Indicator System Overview

Data is the core asset of internet companies; it drives product recommendations on e‑commerce platforms, route optimization for ride‑hailing services, and travel suggestions for booking sites. Empowering business with data involves four stages: data planning, data collection, data analysis, and data‑driven decision making, each requiring collaboration across multiple departments.

The complete indicator‑system construction process can be summarized in seven steps:

Requirement gathering: product or operations teams provide prototypes or plans, and data analysts extract and evaluate data needs.

Requirement consolidation and scheduling: analysts document requirements and prioritize them.

Designing the indicator‑system scheme: analysts use OSM, AARRR/UJM, and MECE models to draft the framework.

Data‑tracking plan: analysts define tracking points, field naming conventions, and collection methods, then hand them to front‑end and back‑end developers.

Data collection: data engineers ingest tracked data into the data warehouse and perform cleaning.

Building the indicator system: analysts verify the ingested data and implement the scheme.

Effect evaluation: the deployed system monitors business status, guides decisions, and is continuously refined.

To create a universal indicator system, the article adopts a product‑lifecycle perspective and applies the same three‑step, four‑model methodology.

Step 1 – OSM: Define Business Goals and Data Dimensions

The primary business goal for any internet product is revenue generation; thus, increasing the product’s paid conversion rate is the focus. Consistent data dimensions (e.g., “people”) are essential for comparable metrics across stages.

Step 2 – AARRR/UJM: Decompose the User Path

After setting the goal of boosting paid conversion, the AARRR model breaks the user journey into acquisition, activation, retention, referral, and revenue, allowing each sub‑path to be optimized for higher conversion.

Step 3 – MECE: Hierarchical Governance of Indicators

Using the MECE principle, the decomposed sub‑goals are turned into independent, exhaustive data indicators. First‑level indicators represent core KPIs for each user‑path step; they are further broken down into second‑ and third‑level indicators to pinpoint causes of performance changes.

By mastering this methodology, analysts can adapt the framework to complex business scenarios, systematically extracting core KPIs, and ensuring that all lower‑level metrics are mutually exclusive and collectively exhaustive.