How to Perform Quality Spot-Checking After Number Generation? 3 Methods & Sample Size Recommendations

Number generation is only the first step; the quality after generation is what determines the cost of customer acquisition. If you directly use numbers that haven’t been spot-checked for large-scale screening or marketing, you are likely to face a high percentage of invalid numbers, empty numbers, or even campaign failure due to platform rejection. Generation quality spot-checking is a low-cost, high-efficiency verification method that helps you assess the validity rate of your number pool in advance and optimize your next steps. This article details 3 practical spot-checking methods and provides statistically based sample size recommendations to help you achieve precise quality control.

Why Must Quality Spot-Checking Be Done After Number Generation?

Many teams think, “Just screen the numbers directly after generation; spot-checking is an extra step that wastes time and effort.” This mindset often leads to wasted investment.

Number Generation ≠ Number Validity

Number generation tools (including random generation or prefix-based generation) follow number rules or prefix data, but they cannot guarantee that every number has been registered or is currently in use. Factors such as carrier number recycling, user deactivation, and platform registration restrictions can cause many generated numbers to never reach real users. Without spot-checking, submitting numbers directly for batch screening tasks may waste significant balances on invalid numbers.

ROI of Spot-Checking: Avoid Large Screening Waste with Small Testing Costs

A single spot-check might only test 500–1000 numbers, costing relatively little. But it provides crucial insights: an estimate of the validity rate of the number pool, which prefixes are higher quality, and whether you need to adjust your generation strategy. This is like using “small money” for a “preliminary survey,” avoiding large screening expenses on invalid numbers. For budget-limited teams, this is a highly cost-effective initial step.

3 Common Spot-Checking Methods

Based on your number pool size and business goals, you can choose one or combine multiple methods.

1. Random Sampling — Simplest Proportion Estimation

Applicable scenarios: The number pool comes from a single country or prefix range, with relatively uniform distribution.

Steps:

1. From all generated numbers, use random numbers or tools (e.g., Python’s random.sample, Excel’s RAND() function) to extract a fixed number of samples.
2. Submit the samples to a screening platform for “activation check” (i.e., registration check) to confirm whether the numbers are valid.
3. Calculate the validity rate: valid numbers ÷ total samples × 100%.

Pros: Simple operation, intuitive results. Cons: If the number pool has significant quality differences between prefixes (e.g., high-quality vs. low-quality prefixes mixed together), random sampling may overestimate or underestimate the overall validity rate.

2. Stratified Sampling — Spot-Check by Country or Prefix Segment

Applicable scenarios: The number pool covers multiple countries/regions or comes from different prefixes.

Steps:

1. Group numbers by country, region, or prefix. For example, separate US numbers, UK numbers, and Singapore numbers.
2. Extract samples from each group. For groups with few numbers, increase the sample proportion; for groups with many numbers, decrease it appropriately.
3. Check the validity rate for each group separately, then weight the results to calculate the overall validity rate. Formula: Σ (group validity rate × group number proportion).

Pros: More accurate results; can identify high-quality and low-quality segments. Cons: More complex operation; requires sorting numbers first.

3. Cross-Platform Verification — Check Telegram & WhatsApp Activity Simultaneously

Applicable scenarios: You need to know the validity of numbers on multiple social platforms to choose the best outreach channel.

Steps:

1. From the spot-check sample, select the same set of numbers.
2. Submit them sequentially to Telegram and WhatsApp screening tasks. For Telegram, you can select “active detection” to confirm recent login behavior; for WhatsApp, use “valid number detection.”
3. Compare results from both platforms. For example, a number might be valid and active on Telegram but invalid on WhatsApp. This helps decide which platform is better for messaging marketing.

Pros: Provides a comprehensive number profile, avoiding the limitations of a single platform. Cons: Higher cost because the same number is tested multiple times.

Sample Size Recommendations: How to Estimate Overall Validity Rate with Minimum Numbers?

Sample size is not simply “bigger is better” or “smaller is better.” Based on statistical principles, at a 95% confidence level, recommended sample sizes for different number pool sizes are as follows:

Number Pool Size (Total)	Recommended Sample Size	Achievable Margin of Error (approx.)
Under 10,000	500–800	±5%
10,000–100,000	1,000–1,500	±3%
100,000–1,000,000	1,500–2,000	±2.5%
Over 1,000,000	2,000–3,000	±2%

Practical tip: If you have 500,000 generated numbers, extract at least 1,500 for multi-platform cross‑checking (e.g., Telegram activation check + WhatsApp valid number check). This gives you an overall validity rate estimate within a ±3% margin of error.

Complete Workflow: From Spot-Checking to Batch Screening

Spot-checking is not the end but the starting point for optimizing the entire workflow. Below is a full process using a real-world scenario.

Step 1: Generate Test Samples Using Global Number Generation

Go to the KK-DATA console, select the “Number Generation” module. Generate a batch of numbers (e.g., 100,000 US numbers) based on your target market. You can configure by prefix or country during generation.

Step 2: Adjust Screening Budget & Strategy Based on Spot-Check Data

After spot-checking the generated numbers, obtain the validity rate. For example, if checking 1,000 numbers yields a validity rate of 65%, that means out of your estimated 100,000 numbers, about 65,000 are valuable. Based on this:

• Adjust budget: If your budget is limited, only proceed with screening those 65,000 likely valid numbers.
• Optimize generation: If a certain prefix has a significantly lower validity rate than others, exclude that prefix during generation.

Step 3: Submit Batch Screening Tasks and Monitor Results

Based on the spot-check results, re-plan batch screening tasks. In KK-DATA, you can submit spot-checked prefixes or countries as separate tasks, selecting the required detection type (e.g., Telegram active detection or WhatsApp valid number detection). After the task completes, the system will notify you (via Telegram), and you can download the screened high-quality numbers.

Correct Interpretation of Spot-Check Results — Common Pitfalls & Solutions

Spot-check results provide valuable references, but treating them as absolute truth may lead to poor decisions.

Common Pitfall	Correct Interpretation
Assuming the spot-check validity rate equals the overall validity rate	Spot-check results have a ±2% to ±5% margin of error; consider sample size and confidence level.
Ignoring quality differences between prefixes	If numbers come from multiple countries or prefixes, calculate separately to avoid misleading averages.
Treating one spot-check result as permanently valid	Number status is dynamic. Carriers may recycle prefixes, users may deactivate. Re‑spot-check regularly, especially if there is a long gap between generation and screening.
Only performing one detection type	Choose detection type based on your acquisition goal. If you only need to confirm the number can receive messages, activation check is sufficient. If you need active users, use active detection.

Solution: For large number pools, conduct multiple rounds of spot-checking. For example, first round: 500 samples for quick assessment; second round: 1,500 samples for in‑depth validation. If the two results differ significantly, check for sampling bias.

How to Use the Data Deduplication Warehouse to Optimize Future Spot-Checking

In multiple rounds of spot-checking, the same number may be submitted repeatedly, wasting costs and skewing results. The data deduplication warehouse is a great tool to solve this.

• Avoid duplicate checks: Before submitting a spot-check task, import numbers into the deduplication warehouse. The system automatically removes numbers already tested, ensuring each number is tested only once.
• Accumulate quality data: Save results from multiple spot-checks in the deduplication warehouse to form a number quality database. For example, you can see that a certain mobile number was valid three months ago but invalid in the most recent test, helping you understand the timeliness of number status.
• Increase spot-check efficiency: For a new number pool, cross-reference it with the deduplication warehouse to quickly find numbers that have never been tested, avoiding wasted effort.

Checklist: Quick Reference for Number Generation Quality Spot-Checking

Before, during, and after each spot-check, follow this checklist to significantly reduce bias risk.

Before Spot-Checking

• Have you stratified the number pool by country/prefix (if applicable)?
• Have you determined the sample size (based on pool size and acceptable error margin)?
• Have you ensured sufficient balance (spot‑check costs are low, but confirm)?
• Have you set up Telegram task notifications (for timely results)?

During Spot-Checking

• Did you select the correct detection type (activation check vs. active check vs. cross-validation)?
• Did you clean the sample using the deduplication warehouse to avoid duplicate checks?
• Did you record the source of the spot‑check sample (generation batch, country, prefix) for future review?

After Spot-Checking

• Have you calculated the validity rate based on results, considering the margin of error?
• Have you adjusted your generation strategy or screening budget based on results?
• Have you imported the spot‑check results into the data deduplication warehouse for future comparison?

Frequently Asked Questions

Q: If the spot‑check sample size is too small, can the results represent the whole pool?

A: In theory, the larger the random sample, the better the representation. For a pool of 100,000+ numbers, it is recommended to extract at least 1,000–2,000 samples for multi‑platform verification to achieve a ±3% margin of error. If the numbers are uniformly distributed or you know the prefixes are similar in quality, you can be more lenient.

Q: Which detection type should I choose for spot‑checking? “Activation check” or “active detection”?

A: It depends on your acquisition goal. If you only need to confirm the number can receive messages, use activation check (registration check). If you need the user to have recent login activity (so that messages are not blocked), use active detection. If budget allows, do both for cross‑comparison.

Q: Isn’t it faster to directly batch screen numbers after generation? Why bother with spot‑checking first?

A: Direct batch screening deducts fees all at once. If the overall validity rate is very low (e.g., less than 10%), you will waste a large balance on invalid numbers. Spot‑checking first lets you estimate the validity rate, assess prefix quality, and even pause the use of low‑quality prefixes, optimizing your generation logic before proceeding in bulk. The small cost of spot‑checking is far less than large‑scale indiscriminate screening.

Q: Are spot‑check results affected by the number generation algorithm? Is the quality of numbers from different countries the same?

A: Yes. Number generation is based on prefix rules, and different countries/regions have different prefix availability. Generally, registration rates are higher in Europe, the US, and some Southeast Asian countries; smaller language countries or regions with strict carrier controls may have higher rates of invalid numbers. It is recommended to spot‑check stratified by country/region rather than a general check across all numbers.

Q: Do spot‑check numbers need to be deduplicated? What if there are duplicates within the same generation batch?

A: Yes. If deduplication was not performed during number generation, the same number might be sampled more than once, leading to bias. It is recommended to use the data deduplication warehouse to clean the sample before submitting for spot‑checking, ensuring each number is tested only once.

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To start generation quality spot‑checking and experience the complete generation‑screening workflow, log in to the KK-DATA console to create an account and try the number generation feature. For detailed operation steps, refer to the documentation. If you have any questions, contact customer service via Telegram @kkdata_robot.