Article summary
Interpret Telegram active days, offline time, UserID, username, VIP and frozen-state fields with cohort-based analysis instead of arbitrary activity labels.
A Telegram activity export can look precise while still being easy to misuse. “Active days” and “user offline time” are observations returned by a selected task; they are not a universal engagement score, a prediction of response, or evidence of consent. The correct interpretation depends on the comparison group and the business event being studied.
This article treats the Excel output as an analytical dataset. The emphasis is on field definitions, distributions, cohort design and exception handling rather than on the mechanics of uploading a file.
A field is not yet a metric
The AIPUSH TG activity task can return phone number, TG UserID, Telegram username, user offline time, active days, first name, last name, TG VIP status and frozen status. These are source fields. A metric is created only after the analyst defines a rule, period and population—for example, the median active-days value among opted-in customers acquired in the same month.
Keep source fields unchanged. Store labels such as “recent,” “middle” and “dormant” in new columns with a versioned rule name. Otherwise, a future analyst cannot separate the original result from the interpretation layered on top of it.
Interpret the activity schema field by field
| Returned value | Analytical use | Common overclaim |
|---|---|---|
| Active days | Rank relative recency within a comparable cohort | “This person will reply” |
| User offline time | Review timing patterns and anomalies | “This is the exact next-online time” |
| TG UserID / username | Reconcile returned records and detect changes | “The displayed identity is verified” |
| First name / last name | Assist controlled record review | “The name is legally accurate” |
| TG VIP status | Preserve the returned account-state field | “The person has higher commercial value” |
| Frozen status | Separate a returned account-state condition | “The user violated a specific rule” |
Why one global activity cutoff usually fails
A list assembled from last week’s customers will naturally differ from a three-year partner directory. Countries, acquisition channels and product cycles can also create different distributions. If every record is judged against one cutoff, the rule often becomes a disguised source classifier rather than a true recency signal.
Begin by partitioning records into comparable cohorts: customer versus prospect, source channel, acquisition month, region and most recent internal event. Calculate the median and several percentiles inside each group. Only then decide whether a returned activity range distinguishes outcomes that matter.
A small cohort experiment
Suppose 1,000 opted-in records are divided into recent customers and legacy leads. Within each source group, create three activity bands using its own distribution. Randomly hold back part of every band. Run the same appropriate communication treatment and compare reply, conversion, opt-out, block and complaint rates.
The holdout matters because a high-performing band may simply contain better customers. If outcomes improve only when activity and customer recency are combined, the business rule should reflect that interaction instead of giving Telegram activity all the credit.
Account for Telegram visibility and approximation
Telegram’s own FAQ says users can restrict last-seen visibility and may expose approximate ranges instead of exact timestamps. That privacy context should temper any claim made from an activity-related dataset. Platform visibility rules can change, and a missing or coarse result should not be described as inactivity without evidence.
Consult the official Telegram FAQ for the current explanation of last-seen and online status. The analysis should preserve an “unknown/unavailable” bucket rather than forcing every record into active or inactive.
Use an exception taxonomy
| Exception class | Example | Handling |
|---|---|---|
| Input problem | Country code cannot be determined | Repair from the source or exclude without guessing |
| Return gap | No activity value is present | Keep as unavailable; do not convert to zero |
| Join conflict | One normalized number maps to several CRM rows | Resolve the source relationship before segmentation |
| State anomaly | Frozen state conflicts with a known internal event | Flag for review and retain both observations |
| Policy exclusion | Opt-out exists in the CRM | Suppress regardless of the activity band |
Measure data quality before campaign performance
Calculate completion rate for each field, unique-key rate for phone number and UserID, source-join success and the share of records in the unavailable bucket. Compare these measures by country and acquisition source. If one cohort has much lower completion, a campaign result may reflect data quality rather than actual audience behavior.
Version the normalization code and the segmentation thresholds. When a later batch shifts dramatically, the team can determine whether the population changed, the processing rule changed or the returned schema changed.
Choose activity checking only when activity informs a decision
If the team needs a simple registered/not-registered answer, the TG registration service is the clearer task. If it needs age, gender or avatar-related output, the corresponding profile task has a different schema and stronger minimization questions. Do not order activity merely because it sounds like a richer signal.
The full workflow still begins with a TXT file and ends with Excel, but those mechanics should not dictate the analysis. The desired decision determines the task; the task determines the fields.
A defensible segmentation rule has five parts
- Population: the authorized source cohort to which the rule applies.
- Observation: the specific returned field and its definition.
- Threshold: a cohort-based range with a version date.
- Exclusions: opt-outs, unknown sources, join failures and unavailable values.
- Evaluation: business outcomes and negative signals measured against a holdout.
When those parts are documented, “TG active” stops being a vague label and becomes a testable rule. That is the difference between a spreadsheet that looks informative and an analysis that can guide a real decision.
