TG Effective Data Scoring Guide: How to Self-Assess the Data Quality and Effectiveness of Your Telegram List

In outbound marketing, community operations, and batch outreach scenarios, the quality of your Telegram list directly determines your customer acquisition cost and conversion results. If you’re still measuring the value of a list by “quantity,” and each time you send messages you only get a few replies or even face mass account bans, then your list urgently needs a TG Effective Data Score.

This guide will take you through systematically evaluating the activation rate, activity level, gender accuracy, and deduplication rate of your Telegram list from a data operations perspective, and provide actionable scoring calculation methods and practical steps. No matter which screening tool you use, the scoring framework here will help you quickly determine whether a list is worth investing in.

Why Do Telegram Lists Need Data Quality Scoring?

Mass-purchased or collected Telegram lists often contain a large number of invalid numbers, zombie accounts, duplicate entries, and even fabricated data. If you start outreach without quality assessment, the consequences include:

• A large number of message send failures, wasting time and API resources
• Frequently triggering platform risk controls, leading to account restrictions or bans
• Difficulty in attributing conversion effects, leading to misjudgment of marketing strategies

Therefore, data quality scoring essentially shifts from “going by gut feeling” to “looking at data,” using quantifiable indicators to help you make decisions.

The Shift from Quantity-Oriented to Quality-Oriented

Many teams are used to focusing on “how many tens of thousands of numbers we have” but rarely ask “how many of them can actually be reached and generate interactions.” When the total list exceeds 100,000, every 10% drop in effectiveness means thousands or even tens of thousands of dollars in wasted costs. Shifting to quality orientation means incorporating the following indicators into daily operations:

• Effectiveness Rate: The actual proportion that has registered Telegram
• Activity Rate: The proportion that has logged in or interacted within a specified time window (e.g., 7 days, 30 days)
• Accuracy Rate: The trustworthiness of auxiliary dimensions such as gender identification and platform attribution
• Uniqueness Rate: The proportion of unique entries without duplicates

Which Scenarios Benefit Most from Data Quality Scoring?

Scenario	Direct Value of Scoring
Purchasing from data vendors	Verify with small sample before payment to avoid buying low-quality data
Bulk sending private messages	Filter highly active users to reduce ban risk and increase reply rate
Community invitations (TG follower growth)	Only invite activated and active numbers to reduce invitation failures and complaints
Targeted ad placement	Combine gender and activity level for audience segmentation to improve ROI
Internal data cleaning	Periodically “check up” on existing lists to eliminate outdated numbers

The Four Core Dimensions of TG Data Quality

To build a TG Effective Data Score system, you need to cover at least the following four dimensions. They reflect the reachability and potential value of your list from different angles.

Dimension	Description	Impact on Outreach Effectiveness
Activation Rate	Whether the number has registered Telegram	Threshold. Lists below 60% are basically unusable
Activity Rate	Recent login, online presence, or interaction	Core indicator. Highly active users have significantly higher reply rates
Gender Identification Accuracy	Whether the gender tag inferred from avatar or nickname matches reality	Improves targeting precision (e.g., beauty, gaming and other niche industries)
Deduplication Rate	Percentage of duplicate entries in the list	Duplicate data wastes screening credits and may cause the same user to be contacted multiple times

The four dimensions are not equal in weight. In a typical scoring model, activation rate and activity rate each account for 40%, while gender identification and deduplication rate each account for 10%. You can adjust the weights based on business needs, but it is recommended to always prioritize “activation + activity.”

How to Quantify Each Dimension’s Score?

Scoring criteria can be customized, but the following methods have been validated by many overseas teams and are easy to understand and implement.

Activation Rate Score (Basic Threshold)

• Scoring Rule: Number of detected activated numbers divided by total sample (excluding empty numbers and unregistered numbers)
  • 90% and above: 10 points (full score)
  • 80% – 89%: 8 points
  • 70% – 79%: 6 points
  • 60% – 69%: 4 points
  • Below 60%: 0 points (not recommended for use)

Lists with an activation rate below 60% should be discarded directly or returned to the data source.

Activity Rate Score (Core Value)

Activity rate requires specifying a time window first. Common windows are 7 days, 15 days, and 30 days. The shorter the window, the stricter the indicator. For time-sensitive campaigns requiring immediate outreach, use a 7-day activity window; for long-term operations (e.g., weekly push), a 30-day window is more reasonable.

• Scoring Rule: Number of active numbers / Number of activated numbers
  • 7-day activity rate ≥ 40% or 30-day activity rate ≥ 60%: 10 points
  • 7-day activity rate 30% – 39% or 30-day activity rate 50% – 59%: 8 points
  • … (and so on, can be prorated proportionally)

In practice, it is recommended to use both windows simultaneously and indicate the chosen window in the scoring report.

Gender Identification and Deduplication Score (Auxiliary Optimization)

• Gender Identification Score: If the list already has gender tags, use a small sample to compare accuracy.

  • Accuracy ≥ 85%: 10 points
  • Deduct 2 points for every 10% drop
  • Below 50%: 0 points (gender tags are meaningless)

• Deduplication Score: Unique count after deduplication / Original total

  • Uniqueness rate ≥ 95%: 10 points
  • 80% – 94%: 6 points
  • Below 80%: 0 points (must be cleaned first)

Deduplication is a dimension easy to overlook. Duplicate data causes the same number to be checked multiple times, wasting screening credits and potentially annoying users when contacted.

Practical Steps: Using a Screening Tool to Obtain Scoring Data in Batches

Using KK-DATA as an example, here is how to use the “Generate → Filter → Export” pipeline for scoring. Other platforms with similar functions can follow analogous steps.

Prerequisites: Registered and logged in to app.kkdata.cc, account balance sufficient (USDT payment, minimum ~50 USDT, charged per record).

1. Prepare a sample: Randomly extract 1000 – 5000 records from the target list. If the total list is huge (over 100,000), stratified sampling is recommended to ensure representation from different sources.

2. Submit a multi-dimensional screening task

   • Create a new task in the console, import the sample CSV/TXT.
   • Select detection types: simultaneously check “Telegram Activation Detection”, “Telegram Activity Detection (choose 7-day window)”, and “Telegram Gender Identification”. If you also want to export tgid for further analysis, check “tgid Export”.
   • Preview the estimated cost, then submit. After the task is completed, you will receive a Telegram notification (you need to bind @kkdata_cc in advance).

3. Export raw data

   • After the task is completed, the results can be exported as CSV or TXT. Each record includes the number, activation status (yes/no), active days (e.g., 0 – 7 days), gender tag (male/female/unknown), tgid, and other fields.

4. Build the scoring model

   • Import the CSV into Excel or Google Sheets.
   • Add new columns: “Activation Score”, “Activity Score”, “Gender Score”, “Deduplication Score”.
   • Use functions like COUNTIF, AVERAGE to calculate percentages for each dimension, then assign scores according to the rules above.
   • Composite Score = Activation Score × 0.4 + Activity Score × 0.4 + Gender Score × 0.1 + Deduplication Score × 0.1.

5. Interpret the results

   • Composite Score ≥ 8.0: High-quality list, ready for full-scale outreach.
   • 6.0 – 7.9: Average, recommend cleaning (remove inactive numbers) before use.
   • Below 6.0: Suggest seeking a better data source.

The entire process takes about 30 minutes the first time; after that, it can be automated with functions for one-click refresh.

What Should a Complete List’s Scoring Report Include?

A scoring report is not just numbers; it should be a decision-making tool. A professional report should include:

• Summary: List name, sample size, detection window, composite score, evaluation conclusion (keep / clean / discard)
• Radar chart or bar chart for each dimension: Visually display the four-dimensional scores for easy comparison between different lists
• Segmented comparison: If the list comes from different channels (e.g., scraping, purchase, user registration), give separate scores
• Action recommendations: Concrete actions based on scores, e.g., “List A has high activity on weekends, suitable for sending weekend event invitations”
• Raw data attachment: Export the result CSV for team secondary analysis

Three Best Practices to Improve TG List Quality

1. Periodic retesting: Telegram accounts can change due to long-term inactivity, deletion, or bans. It is recommended to re-test activity (not necessarily full-scale, sampling is enough) on existing lists every quarter to remove invalid numbers.

2. Combine with global number generation: If you need fresh numbers, first generate a batch of numbers via Global Number Generation (random or number-range generation across 240+ countries/regions), then immediately screen for activation and activity. Lists obtained this way often have higher initial quality than purchased data.

3. Leverage the data deduplication repository: In the KK-DATA console, all screening task results automatically enter the deduplication repository. When submitting a new task, you can choose “Skip already checked numbers” to avoid duplicate charges. For long-term operations, this feature can save 20% – 30% of screening budget annually.

Common Misconceptions and Cautions

• Focusing only on activation rate, ignoring activity rate: Even if 90% of numbers have Telegram, if 70% are zombie accounts, the actual reachable users are less than 30%. Always score both activation and activity.
• Submitting the entire list for multi-dimensional detection at once: It’s recommended to test with a small sample first to ensure list quality, then execute on the full list. Full-scale detection can incur high costs and is non-refundable.
• Ignoring list refresh cycles: Activity data from three months ago may have significantly degraded. Scoring results only represent the state during the detection window; timeliness is important.
• Trusting fake customer service: Carefully verify the platform’s official contact channels.

Frequently Asked Questions

Q: Do I need to run full-scale detection every time for TG effective data scoring?

A: No. It is recommended to first use random sampling (sample size ≥ 1000) for a quick assessment. If the composite score is below 60 points, consider changing the data source; if above 80 points, then perform targeted detection on the full list to save credits. Estimated charges can be seen in the console before batch detection.

Q: Does the activity window (7 days, 15 days, 30 days) significantly affect the score?

A: Yes, it is significant. 7-day activity is the strictest indicator, suitable for scenarios requiring immediate outreach (e.g., time-limited campaigns); 30-day activity is more suitable for long-term operations. It is recommended to choose the window according to your marketing rhythm and indicate it in the report.

Q: Can gender identification accuracy be a standalone scoring dimension?

A: Yes, it can. For communities that need to target female users or focus on content interaction (e.g., beauty, maternity), adding a gender dimension can improve targeting accuracy. However, if the list’s overall effectiveness is low, prioritizing activation and activity rates is more critical.

Q: Does a high score guarantee high conversion rates?

A: Not necessarily. Data quality scores reflect number validity, activity, and consistency, not user interest or purchase intent. High scores are a prerequisite for effective outreach, but conversion also depends on content quality, outreach frequency, and user profile matching. It is recommended to use scoring as the first screening step and then do further segmentation based on business data.

Q: Does KK-DATA support exporting scoring data for analysis in external tools?

A: Yes. Screening results can be exported as CSV, TXT, etc., including fields like tgid, activation status, active days, and gender tags. You can build your own scoring model in Excel, Google Sheets, or BI tools to batch process radar charts or composite scores as described in the article.

---

Start evaluating your first list: Log in to KK-DATA Console, create a small sample screening task, use the method in this guide to calculate the composite score, and see if the TG data you have is worth investing in. If you have any questions, refer to the official documentation or contact customer service directly via @kkdata_cc.