Telegram Screening Quality Spot Check: 3-Step Verification Plan & Efficiency Calculation Guide

In the process of overseas customer acquisition, Telegram number screening is a core step for batch-filtering potential customer numbers. Regardless of which screening platform you use, the quality of the results directly determines subsequent acquisition costs and conversion efficiency. If the screening results are inaccurate—for example, marking invalid numbers as valid, or missing real active users—your promotional budget will be wasted.

This article provides a practical Telegram screening quality spot check method, covering sample size determination, three-stage cross-verification, efficiency calculation, and common pitfalls. It helps overseas teams establish a quality inspection mechanism to ensure every screening investment is worthwhile.

Why Do You Need a Quality Spot Check on Telegram Screening Results?

Even with professional screening platforms, number statuses can deviate due to the following reasons:

• Real-time status changes: A number valid today may become invalid tomorrow due to account deactivation, banning, or unbinding.
• Different detection strategies: Different platforms have different definitions of “valid” (e.g., whether TGID has been verified, whether the user was recently online). The same number may yield different results on different platforms.
• Data contamination risk: The original number list may contain duplicates, empty numbers, or incorrectly formatted records, which the screening platform may not fully filter out.

Regular spot checks allow you to quantify deviations and exclude abnormal batches, thereby keeping customer acquisition costs within a reasonable range.

What Is a Telegram Screening Quality Spot Check?

A quality spot check involves selecting a certain number of samples from the completed screening results based on statistical rules, rechecking the actual status of the numbers manually or through cross-verification, and then comparing the efficiency reported originally to calculate the deviation. A spot check is not a full re-screening; it uses minimal cost to evaluate overall accuracy.

Differences Between Spot Check and Full Re-screening

Dimension	Spot Check	Full Re-screening
Sample size	Small (tens to hundreds)	All data
Cost	Very low (only consumes a small amount of balance or manual time)	High (full consumption of balance)
Time	Minutes to hours	Usually several hours
Purpose	Quality verification, deviation warning	Complete correction, precise results
Applicable scenarios	Regular inspection, platform comparison, new tests	High-value lists, when extremely high accuracy is required

Core Metrics to Monitor in a Spot Check

• Efficiency: Number of numbers marked as “valid” in the original report ÷ total screened numbers. This is the most basic metric.
• Misjudgment rate: Number of numbers whose original report status does not match actual status after spot check verification ÷ total spot-checked numbers. For example, the original report shows “tg valid” but the number cannot be found on TG search.
• Open rate deviation: If you filtered for “tg active (30 days)”, but the actual spot check finds that the active rate is lower than the reported value. This deviation directly reflects the likelihood of reaching customers.

How to Set a Reasonable Sample Size and Spot Check Strategy?

Too small a sample size leads to unreliable results; too large wastes cost. The following principles can help you determine the scale.

Basic Principles for Sample Size Selection

Total Screened Numbers	Recommended Spot Check Sample Size	Notes
Below 5,000	100–200	At least 100 to ensure statistical significance
10,000–50,000	200–500	2%–5% of total batch
Above 100,000	500–1,000	Ratio can drop to 0.5%–1%, but absolute value is sufficiently large

The above is based on a 95% confidence level. For high-value customer acquisition scenarios (e.g., finance, medical aesthetics), use the upper limit.

Implementing a Layered Spot Check Strategy

Do not sample only one batch of data. It is recommended to sample by the following dimensions:

1. By detection type: tg registered, tg valid, tg active (7 days/15 days/30 days) separately, because the accuracy of different detections may vary.
2. By task submission time: Sample tasks at different times or days separately to exclude temporary platform fluctuations.
3. By number source: If numbers are batch-generated, sample each generated file independently; if imported from CSV, sample by source file.

Three Steps to Complete a Telegram Screening Quality Spot Check

Below is a three-step process that can be directly implemented.

Step 1: Extract Samples from Screening Results and Prepare a Verification List

1. Export the screening results from the screening platform (e.g., KK-DATA Console), choose CSV or TXT format.
2. Randomly extract numbers proportionally according to the sample size standard from the previous step. If using Excel, you can sort using =RAND() and take the first N entries; or use an online random extraction tool.
3. Remove duplicate numbers from the sampling list (if duplicates exist in the original data, prioritize deduplication before sampling to avoid verifying the same number multiple times).
4. Organize the sampled numbers into a verification list, preferably containing three columns: number, original report status (valid/invalid/active), and notes column.

Step 2: Cross-Verify Sample Numbers Manually or with Tools

There are two verification methods; choose based on your resources and time:

Method A: Manual Verification (suitable for small samples)

• Open Telegram, enter the number in the search box (remember to add the correct country code).
• If you can find the user’s avatar, nickname, and can send a “hi” message without error, it is judged as “tg valid”.
• If the search has no results or prompts “number not registered”, it is judged as “tg invalid”.
• For “tg active” judgment: Check the user’s last online time (in PC version, click avatar to see “last seen”). If it falls within your window (e.g., 30 days), mark as active; otherwise, not active.

Method B: Tool-Assisted Verification (suitable for medium to large-scale spot checks)

• Use another screening platform (not the original one) for a secondary detection, but choose a different detection strategy (e.g., if the original platform detected “tg registered”, choose “tg valid” for secondary detection to avoid mutual influence).
• Alternatively, use Telegram API for low-frequency queries (be aware of API anti-scraping limits).

Regardless of the method, record the actual status (valid/invalid/active/inactive) for each number and note the verification time.

Step 3: Calculate Efficiency and Evaluate Deviation

Suppose you extract 200 samples. The original report indicates that 150 of them are “tg valid”. After verification, the actual number of valid ones is 130.

• Spot check efficiency = (130 / 200) × 100% = 65%
• Original report efficiency = (150 / 200) × 100% = 75%
• Deviation rate = (75% - 65%) = 10%

The deviation rate indicates that the original report overestimates by 10 percentage points. If the deviation is within your acceptable range (e.g., you set it to < 5%), the task quality is acceptable; otherwise, the cause needs to be investigated.

Metric	Formula
Spot check efficiency	(Verified valid count / Total spot-checked count) × 100%
Original report efficiency	Original report valid count / Total spot-checked count × 100%
Deviation rate	Original report efficiency - Spot check efficiency
Misjudgment rate	(Original report valid but actually invalid + Original report invalid but actually valid) / Total spot-checked count × 100%

Common Factors Affecting Spot Check Accuracy and Countermeasures

1. Timeliness of number status: Number status may change within hours after screening (e.g., user deactivates account). → Countermeasure: Complete the spot check within 24 hours after screening; if immediate verification is not possible, note the verification time in the report.
2. Differences in detection strategies: Different platforms define “tg valid” differently (some only check registration, some check whether messages can be received). → Countermeasure: Confirm the specific detection type of the screening platform; use the same definition as the original report during spot checks (e.g., both use “whether it can be found via TG search”).
3. Regional network issues: Some numbers may not be verifiable due to regional blocks. → Countermeasure: Use a network environment that is the same or similar to that used by the screening platform.

Common Spot Check Misconceptions and Pitfall Avoidance Guide

• Misconception 1: Only spot-check valid numbers, not invalid ones
  Correct practice: Both valid and invalid numbers should be sampled proportionally. Only verifying valid numbers cannot assess the situation of marking invalid numbers as valid (which is the biggest pain point).
• Misconception 2: Ignoring activity window differences
  If the original report filtered for “tg active (30 days)”, but you only check whether the user is registered during spot check, you will underestimate the risk of misjudgment. You must verify using the same window.
• Misconception 3: Sample size too small (e.g., only 30 samples)
  With too small a sample size, the confidence interval for efficiency estimation becomes very wide, and conclusions are unreliable. At least 100 samples or more.
• Misconception 4: Only spot-check once and think it’s done
  Screening platform algorithms may update, and number statuses change continuously. It is recommended to perform a spot check after every 5–10 tasks or when changing detection packages.

How to Use Spot Check Results to Optimize Subsequent Screening Strategies?

1. Adjust platform or detection type: If spot checks reveal that a platform’s “tg valid” deviation consistently exceeds 5%, consider switching to other detection types (e.g., using “tg registered” + manual layering), or directly contact platform customer service (e.g., KK-DATA two-way customer service) to report the issue.
2. Establish a regular spot check mechanism: It is recommended to conduct a centralized spot check on the previous month’s screening tasks at the beginning of each month, forming a regular quality monitoring routine.
3. Feed back into the deduplication warehouse: If spot checks find duplicate or abnormal numbers, promptly add them to the data deduplication warehouse to avoid wasting balance on repeated detection in the future.
4. Generate deviation reports: Record the deviation rate for each spot check, and compare data across different time periods to assess platform stability or changes in your data source quality.

Frequently Asked Questions

Q: How many samples should be taken for a Telegram screening quality spot check?
A: There is no absolute standard. It is recommended to sample 2%–5% of the total batch, with a minimum of 100. For high-value customer acquisition scenarios, at least 200 samples are recommended to ensure statistical reliability.

Q: If the spot check finds that the efficiency is more than 10% lower than the original report, what should I do?
A: First, check whether the spot check was completed within 24 hours. Second, confirm whether the spot check samples cover different activity windows. If the deviation remains too large, it is recommended to contact the screening platform’s customer service (e.g., KK-DATA’s two-way customer service channel) to report the original report and consider re-screening.

Q: Should the spot check verify only “valid” numbers, or should it also verify “invalid” numbers?
A: Both must be verified. Only verifying valid numbers will overestimate accuracy; invalid numbers may contain real active numbers misjudged as invalid, causing customer loss. It is recommended to sample 50% each for quantitative comparison.

Q: Can the spot check results represent the quality of the entire screening task?
A: If random sampling and sufficient sample size are used, the spot check efficiency can represent the whole with 95% confidence. However, if the screening task consists of multiple sub-batches (different time periods, different detection types), layered sampling and separate evaluation are needed.

Q: Is it necessary to repeat spot checks regularly after one check?
A: Yes. Number status changes in real time, and screening platform detection algorithms may evolve. It is recommended to perform a quality spot check after every 5–10 screening tasks or when changing detection packages, forming a routine mechanism.

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