Article summary
Turn TG age, gender, avatar and activity fields into cautious aggregate analysis with completion checks, unknown-value handling and documented use limits.
A Telegram age-and-gender result should not go straight from Excel into a campaign segment. First establish what fields were returned, how complete they are and whether the business question can be answered at aggregate level. Usernames, names, avatars, age and gender values are not the same as verified identity attributes.
The most useful output is often a coverage and distribution report—not a row-by-row profile list.
Start with the TG field inventory
| Field group | AIPUSH task fields | Review question |
|---|---|---|
| Keys | Phone number, TG UserID, username | Can records be reconciled without overwriting identifiers? |
| Timing | User offline time, active days | How does visibility and missingness affect the value? |
| Names/state | First name, last name, TG VIP, frozen state | Are user-entered and account-state fields labelled correctly? |
| Profile analysis | Avatar URL, age, gender | Is the purpose proportionate and are unknown values retained? |
Write an analysis question that can be disproved
“Understand our Telegram audience” is too broad. A better question is: “Among customers who opted in during the last quarter, does the returned age-band distribution differ between two acquisition sources after excluding unknown values?” This defines population, comparison and missing-value treatment.
Avoid questions that make high-impact decisions about individuals or assume the returned field is verified fact.
Prepare and process only the approved population
Keep customer IDs, source and permission evidence locally. Export the normalized numbers as one-per-line TXT and choose the TG gender-and-age task. The Excel result can include the listed profile and activity fields; the broader full-format task adds more avatar-related attributes and should not be selected automatically.
Store the untouched result, then join in a restricted staging area. Keep submitted phone, returned UserID and username as separate keys.
Publish a coverage table before a demographic table
| Quality metric | Calculation | Reason |
|---|---|---|
| Join coverage | Matched output rows ÷ returned rows | Shows whether source context survived |
| Age completion | Non-unknown age values ÷ approved rows | Prevents percentages from hiding missingness |
| Gender completion | Non-unknown gender values ÷ approved rows | Reveals cohort-specific coverage differences |
| Avatar completion | Usable returned avatar fields ÷ approved rows | Shows whether image-derived analysis has sufficient base |
Turn values into bands without erasing uncertainty
Create derived age bands only after checking the observed range and field definition. Keep the original value beside the band and create explicit unknown and unavailable categories. Do not infer a missing age from name, avatar or activity.
For gender, publish the classification labels exactly as defined by the task and avoid implying self-identification. If the intended analysis cannot tolerate that uncertainty, use a more appropriate data source or do not make the comparison.
Control avatar-related handling
Avatar URLs and derived image fields should receive restricted access and short retention. A profile image may not depict the account holder, may contain several people or may change. Do not use it as identity proof.
If an aggregate analysis uses avatar-related attributes, report the number of usable images, ambiguous cases and excluded records. Delete local copies according to the approved retention plan.
Interpret activity alongside Telegram privacy
Telegram allows users to control last-seen visibility and may show approximate ranges. Activity-related fields should therefore be treated as bounded observations. Do not rank a missing value below an old value or claim an exact future availability time.
Review current platform explanations in the Telegram FAQ and document the date of the interpretation rule.
A miniature analysis example
Suppose two opted-in customer cohorts each contain 2,000 joined records. Cohort A has 80% age completion; cohort B has 45%. Reporting age percentages only among known values may make the groups appear comparable while hiding very different coverage. The correct report shows both the distribution and the denominator, then investigates why B has more unknowns.
The example numbers are illustrative. Use actual measured counts from the workbook and label them as such.
Release an aggregate report, not an unrestricted workbook
- State the approved question and population.
- Show join and field-completion rates.
- Separate direct, derived and CRM-supplied values.
- Keep unknown categories visible.
- Document source, run date and task.
- Limit access to row-level profile data.
- Set a deletion date for transient files.
Good TG profile analysis makes uncertainty measurable. If a chart cannot explain where its fields came from, how much data is missing and what the values do not prove, it is not ready for a business decision.
