How to Read Telegram Metrics

Why Metrics Matter for Compliance and Data Retention

Telegram ships two first-party data sources—channel statistics and the Bot API—that together satisfy most audit trails without extra SaaS cost. Understanding what each source captures, how long it persists, and where it can be tampered with is the fastest way to move from "nice-looking graphs" to "court-admissible evidence".

The 2025 policy update (client v10.12+) silently extended raw message retention in private groups from 48 h to 7 days on the server side, while keeping the 12-month analytic buffer for channels. That change affects both retention schedules and downstream ETL jobs, so every pipeline you build today should expose a configurable look-back parameter instead of hard-coding 48 h.

Channel Stats vs. Bot API: A Decision Tree

Start with one question: Do I administer the channel/group?

• Yes → Native Statistics tab gives you 100 % coverage of views, shares, and growth with zero setup. Exportable as semi-structured JSON.
• No → You are an external auditor. Only the public-facing Bot API (getUpdates, forwardMessage copies) is available, and you must respect rate limits (30 msg/min per bot token).

If you need historical data older than 12 months, neither source guarantees availability; plan for periodic snapshots instead of last-minute dumps.

When Channel Stats Alone Are Enough

A 120 k subscriber news feed posting 200 times per day can rely on the built-in Insights → Advanced stats dashboard. Views, share rate, and follower source are already aggregated, so you skip ETL complexity. Keep the daily JSON export in object storage; at 3 kB per day it costs pennies and satisfies most regulators.

When You Must Add the Bot API

If you moderate a 5 k member group where messages are later deleted (e.g., price alerts), the native stats miss removed content. A logging bot that calls getUpdates with allowed_updates=["message","message_reaction"] captures the payload before deletion. Store the raw Update object; it is digitally signed by Telegram and hash-traceable.

Platform Shortcuts to Export Data

Paths below are verified on v10.12. If your build differs by a dot release, the labels move but the hierarchy stays stable.

Android

1. Open the channel → three-dot menu → Statistics →右上角 Export.
2. Choose JSON (machine) or CSV (human). Telegram writes to /Android/data/org.telegram.messenger/files/Telegram/Telegram Documents/.

iOS

1. Channel → top bar title → Statistics → Export Data.
2. File lands in Files app under Telegram folder; from there use Save to Files to move to iCloud Drive for automation.

Desktop (Windows, macOS, Linux)

1. Right-click channel in sidebar → Manage channel → Statistics → Export.
2. Default save location is system Downloads; JSON contains Unix timestamps in UTC—ready for pandas.read_json(..., convert_dates=True).

Automated Pull: Minimal Bot Code

If you need continuous logging, run a lightweight Python script on a cron job or AWS Lambda. The snippet below respects 30 msg/min and stores each Update as newline-delimited JSON (NDJSON) for streaming analytics.

import requests, os, time, json
TOKEN   = os.getenv('TG_BOT_TOKEN')
URL     = f'https://api.telegram.org/bot{TOKEN}'
OFFSET  = 0

while True:
    r = requests.get(f'{URL}/getUpdates?offset={OFFSET}&timeout=30').json()
    for u in r['result']:
        print(json.dumps(u, ensure_ascii=False))   # NDJSON stdout
        OFFSET = u['update_id'] + 1
    time.sleep(2)  # ~30 msg/min ceiling

Redirect stdout to a dated file and ship to S3 daily. Because each Update contains message_id and date, you can replay the conversation state offline.

Retention Rules: What to Keep, What to Dump

Minimum Viable Audit Bundle

• Daily JSON export of channel stats (3 kB)
• NDJSON stream of bot-captured messages (≈ 1 kB per message)
• Quarterly SHA-256 manifest of above files stored in WORM storage (e.g., AWS S3 Object Lock)

This bundle is < 1 GB per year even for hyper-active channels and passes SOC-2 evidence requirements.

When You Can Safely Delete

If your jurisdiction allows purpose limitation (e.g., GDPR Art. 5.1.b), you can drop PII-heavy fields such as user.username after 90 days while keeping anonymised aggregates. Use jq to redact:

jq 'del(.message.from.username, .message.from.language_code)' log.ndjson > log_anon.ndjson

Common Pitfalls and How to Spot Them

Pitfall 1: Relying on Edited Message IDs

Edits keep the same message_id but update edit_date. If your pipeline deduplicates only on message_id, you lose the audit trail of changes. Always index on (message_id, edit_date).

Pitfall 2: Ignoring Deleted Accounts

When a user deletes her Telegram account, from.id becomes 777000 for system messages. Your downstream BI will show sudden user count drops. Flag 777000 as a special ID to avoid mislabelling churn.

Pitfall 3: Clock Skew on Self-signed Certificates

If you run a webhook instead of polling, a server clock > 5 min off causes Telegram to reject callbacks. The error message ("ssl error: clock skew too great") is buried in webhook info, not in getUpdates. Sync with NTP before going live.

Third-party Bots: Permission Minimalisation Checklist

A common shortcut is adding an "analytics bot" that requests every scope under the sun. Limit tokens to what the audit actually needs:

Scope	Required for	Keep?
messages	content logging	Yes
member status	join/leave audit	Yes
delete messages	mod action log	No (use getUpdates instead)

Revoke excess rights monthly via @BotFather /revoke. Less privilege = smaller blast radius if the token leaks.

Verification & Observability Methods

Treat Telegram data like any other log stream: immutable once written, replayable when questioned. A lightweight verification harness is:

1. Compute sha256sum of every exported file immediately after download.
2. Push the hash to an append-only log (public GitHub repo, Ethereum test-net, or AWS Q-LDB).
3. During quarterly audit, recompute hashes; any mismatch flags tampering or bit-rot.

For live sanity checks, compare the views field in the JSON export with the same post grabbed via getMessageViews; they should agree within 2 % (empirical variance due to timing).

Performance Footprint: What 10 k Messages per Day Looks Like

A public blockchain discussion group averaging 10 k text messages per day produces:

• ~ 10 MB NDJSON gzipped per month
• ~ 60 API calls per hour (well below 30 msg/min)
• Ram usage of the Python poller < 40 MB on a t3.micro

In short, you can run the collector on a spare Raspberry Pi; network egress is the only bottleneck if you push to cloud storage.

Version Differences & Migration Notes

Telegram increments schema fields without warning. Between v10.10 and v10.12 the reaction_count object moved from message.reactions to message.reaction_count and dropped the chosen_order array. If you ingest reactions, version-gate your parser:

if 'reactions' in msg and 'chosen_order' in msg['reactions']:
    # legacy v10.10 format
else:
    # current v10.12 format

Always pin your Docker base image (e.g., python:3.12-slim) and schedule monthly CI runs that pull the latest client APK, run a diff on the JSON export, and open a ticket if new keys appear.

Best-practice Checklist (Copy-Paste Ready)

FAQ: Edges and Escape Hatches

Can I retrieve a message deleted two years ago?

No. Telegram does not provide any recovery endpoint for deleted messages beyond the 7-day server buffer. Your only option is an earlier snapshot you (or another member) exported.

Does forwarding affect view counts?

Views are tied to the original message_id. Forwarded copies show the same view number; they do not increment it. This is critical when attributing traffic sources.

Are reactions considered PII?

The public reaction list (emoji + user_id) is PII under GDPR. Anonymise by storing only aggregate counts unless you have a lawful basis to keep identifiers.

When Not to Build Your Own Pipeline

If your organisation lacks in-house SRE or legal counsel, outsourcing to an approved Telegram partner (there are a handful listed in the official FAQ) may be cheaper than risking non-compliance. The break-even is roughly at 50 k USD equivalent fine exposure or 20 GB of daily ingest—whichever comes first.

Looking Ahead: What May Change in 2026

Based on public merge requests to the Telegram Android repo, end-to-end encryption for one-to-many channels (called Protected Broadcasts) is undergoing beta tests. If it graduates, server-side stats will lose access to view counts for those channels, pushing audits toward client-side telemetry—a fundamental shift from today’s server-centric model. Start designing your metrics layer so that data source adapters are plug-and-play; the less you hard-code Telegram-specific paths, the lighter the migration pain will be.

Key Takeaways

Reading Telegram metrics in an audit-grade way boils down to three moves: export native stats nightly, capture bot updates continuously, and store tamper-evident hashes. Do that, and you can answer regulators tomorrow without scrambling for data you never retained yesterday.