Article summary
TG checking cannot return a high-conversion label; separate platform observation, contact eligibility, need behavior and outcomes, then validate explainable CRM features offline.
TG number checking cannot directly identify “high-converting users.” It returns Telegram registration, activity or limited profile observations. Conversion is an inquiry, purchase, renewal or other business event defined by your organization. The two can be studied together in CRM, but a platform field cannot be renamed as an outcome.
Define conversion and its window first
High conversion needs a specific event, such as a qualified inquiry within 30 days or contribution margin above a threshold within 90 days. Freeze the definition before modeling. Do not change date, amount or event after seeing who purchased.
The TG task-field boundary
| Task | Available observation | It is not |
|---|---|---|
| Registration | Phone, registration result | Conversion probability |
| Activity | UserID, username, offline time, active days and more | Purchase intent |
| Gender and Age | Avatar URL, age, gender and more | Ability to pay or credit |
CRM needs four feature layers
The platform-observation layer stores TG fields and checked_at. Eligibility stores source, permission and opt-out. Need stores voluntary inquiry, product view or support ticket. Outcome stores order, margin and retention. Training uses only fields available before prediction time, preventing leakage.
Features closer to business causality
| Feature | Reason | Risk |
|---|---|---|
| Voluntary inquiry topic | Direct expression of need | Requires correct classification |
| Resolved support case | Real relationship | Do not punish help seekers |
| Customer stage | Position in sales process | Manual entry bias |
| TG activity observation | May assist timing | Not brand intent |
Test incremental value before modeling
Train a baseline using first-party need and customer stage, then add TG observations. Compare precision-recall, calibration and business cost on an out-of-time validation set. If no stable lift appears, omit the field; payment for data is not a reason to retain it.
Unknown is not a low score
Encode unknown, input error, system error and no-registration-observed separately. Missingness may concentrate in a country or source. Filling every missing value with zero trains the model to treat coverage bias as customer quality.
High-risk limits for profile fields
Do not use age, gender or avatar for credit, employment, price or denial of service, and do not make them core “high-value” features. An approved aggregate content study uses broad groups, minimum cells and aggregated reporting with short retention for individual observations.
Scoring runs after permission
eligible=false or opt_out=true is a veto; a high score cannot bypass it. Scoring orders only an appropriately processable queue and displays its main reasons, data age and uncertainty.
Offline performance does not prove online value
Use a small randomized test between score-ordered and current workflows, holding content, team and offer constant. Measure incremental qualified conversation, margin, opt-out and complaint. A backtest among historic buyers exaggerates survivor bias.
Monitor drift and feedback loops
Country mix, acquisition channel and Telegram product changes alter feature distribution. Monitor calibration, unknowns, stratum coverage and human overrides. If sales contacts only high-score leads, do not train the resulting outcomes as circular proof that high scores work.
Accurate product language
Say that TG output is a timestamped platform observation evaluated with CRM behavior. Do not promise high-converting, high-net-worth or purchase-ready users. Model value comes from controlled validation, not a more attractive name for a column.
