Four Steps to Build Reliable Data Middle‑Platform Tags

Step 1: Clarify Business Data

Tags must grow from business needs, not be invented arbitrarily. The first step is to inventory all data assets, identify their sources, and map them to concrete business actions. The article stresses that focusing solely on database tables leads to inconsistent definitions across systems, causing duplicate or conflicting tags later.

Business domain mapping: list data retained by sales, marketing, customer service, supply chain, finance, production, etc.

Channel mapping: enumerate sources such as website, mini‑programs, e‑commerce platforms, stores, distributors, WeChat Work, call centers.

Data type mapping: distinguish transaction, behavior, attribute, log, content, and external data.

Object mapping: define core entities like users, customers, products, stores, devices, orders, channel partners.

The author warns that ignoring business problems and starting from technical convenience results in fragmented data that cannot support downstream tagging.

Step 2: Consolidate Behavioral Elements

After data collection, raw records are heterogeneous and poorly named. This step transforms them into reusable "behavioral elements"—the standard building blocks for tags.

Business line: e.g., lifestyle paper, cultural paper, industrial paper, special‑purpose paper operations.

Entity objects: users, customers, products, orders, stores, devices.

Entity attributes: age, gender, region, membership level, product category, price range, lifecycle stage.

Actions: browse, click, favorite, inquire, place order, purchase, repurchase, complain.

The article explains that without a unified mapping, the same behavior (e.g., a page view) may be recorded differently across systems, breaking tag consistency. It also notes that this stage is the most intensive part of data governance, involving field standardization, master‑data identification, code mapping, time‑grain alignment, and event granularity definition. An example tool, FineDataLink, is cited as suitable for integrating ERP, CRM, e‑commerce, mini‑program, and tracking‑platform data.

Crucially, the number of behavioral elements should be manageable; start with core objects, attributes, and actions, then expand gradually.

Step 3: Generate Dynamic Profiles

With behavioral elements in place, the platform can automatically compute tags for each object (user, customer, product, store, device, channel) and keep them up‑to‑date. Tags are rule‑based calculation results rather than static manual notes.

Fact tags: gender, region, registration date, product category, channel type.

Statistical tags: visits in the last 7 days, order amount in the last 30 days, annual repurchase count.

Model tags: high churn risk, strong purchase intent, high price sensitivity.

The article stresses that mature tag systems combine frequency, recency, cumulative contribution, and business priority, producing a layered profile that supports identification, segmentation, prediction, and operation.

Step 4: Deploy Tags to Business

Building a tag system is only half the work; the tags must be callable by downstream applications. The final step is to expose the tag service to business scenarios.

Precise marketing: identify high‑intent, dormant, or at‑risk users and tailor outreach.

Personalized recommendation: use preference, purchase history, and behavior features to suggest relevant items.

Channel optimization: evaluate user quality, conversion efficiency, and long‑term value per channel.

Product innovation: derive demand signals from behavior tags to guide iteration and new product development.

The author notes that the real test is whether frontline teams can directly invoke these tags—for example, marketers pulling a segment of users active in the past 15 days with high repurchase propensity, or sales reps seeing a customer's recent inquiries and purchase likelihood. Data latency, inconsistent definitions, or unstable interfaces can break this flow, so robust data synchronization and governance are essential. FineDataLink is again mentioned as a tool that can reliably deliver tag results to CRM, user‑operation platforms, and reporting systems.

In summary, the four‑step method not only constructs a tag taxonomy but also tests an enterprise’s underlying data foundation—standardized definitions, clear object relationships, stable pipelines, and effective governance.