Best Practices for Million-Level Number Screening: Complete Guide to Splitting, Submitting, and Handling Results

If you’re involved in overseas marketing, community management, or cross-border e-commerce, you’ve likely faced this scenario: You have millions of potential customer numbers and need to batch verify which are active users on Telegram or WhatsApp, and which are no longer valid. Submitting 1 million numbers at once sounds efficient, but without a sound strategy, it can easily lead to task failure, wasted balance, or unmanageable data. Based on the actual capabilities of the KK-DATA platform, this article outlines a complete workflow from data preparation to result export, helping you avoid common pitfalls when screening millions of numbers.

Why You Need to Pay Attention to Execution Strategy for Million-Level Screening

Large-scale screening (1 million numbers) is completely different from simple verification of a few hundred or thousand numbers. Factors such as inconsistent number quality, network fluctuations, and platform response delays can turn a “super-sized task” into a nightmare. Properly planning task size, detection types, and sequence not only improves success rates but also significantly reduces costs.

System Limitations and Real-World Scenarios for a Single 1 Million Task

KK-DATA supports up to approximately 1 million numbers in a single task. This is the platform’s upper limit. However, in practice, it’s recommended not to always submit at this limit. Reasons include:

• Number quality affects speed: A large number of invalid numbers (empty, incorrectly formatted) consume detection resources, prolonging task processing time.
• Network stability: Telegram, WhatsApp, and other platforms’ API responses can vary in speed. Submitting too many numbers at once increases the likelihood of timeouts or interruptions.
• Result file size: Exporting results for 1 million numbers can produce CSV files of tens or even hundreds of MB, making local opening, filtering, and analysis cumbersome.

Therefore, understanding the difference between “single task max ~1 million numbers” and “recommended batch size of 50,000–100,000” is the first step to using the platform efficiently.

Three Major Risks of Blindly Submitting Large Tasks

1. Duplicate numbers lead to wasted detections: If your number list contains many duplicates, each duplicate will be charged again (even if some platforms have deduplication mechanisms, failing to clean beforehand still wastes quota).
2. No partial rollback on task interruption: Once submitted, the task begins processing. If interrupted due to network or system issues, completed parts are still charged, and incomplete parts must be resubmitted.
3. Export files too large to handle: Exporting 1 million rows at once cannot be opened directly in Excel (Excel supports ~1.04 million rows, but with many columns and complex fields, performance degrades). Specialized tools or programming languages are required, increasing the barrier to operation.

Step 1: Data Preparation – From Number Generation to Deduplication

High-quality data is the foundation of successful screening. KK-DATA provides two ways to prepare numbers:

• Global Number Generation: Covers 240+ countries/regions. You can randomly generate numbers by country code or number range. Generation is free; screening charges per number. Suitable for building customer bases from scratch.
• Custom CSV Import: If you already have your own number list, simply upload it.

Regardless of the method, deduplication is mandatory before submission. The data deduplication repository is the platform’s built-in cross-task deduplication feature: you can upload numbers from multiple tasks, and the system automatically identifies and removes duplicates. This ensures the same number won’t be detected again in subsequent tasks, avoiding wasted balance.

Step 2: Task Splitting Strategy – How to Scientifically Partition 1 Million Numbers

Splitting 1 million numbers into multiple smaller tasks reduces risk and gives you flexible control over progress. Here are two recommended partitioning methods.

Splitting Based on Number Attributes

• By Country Code: User activity times and detection response times may vary by country. For example, splitting US numbers (+1) and Indian numbers (+91) into separate tasks makes it easier to analyze results by country later.
• By Number Range: If you have sequential number ranges, group them by range intervals. Each group becomes a task, making it easy to trace the source of numbers.
• By Carrier Prefix: Certain number ranges belong to the same carrier, and detection results may show consistency. Partitioning allows targeted optimization.

Splitting Based on Detection Type

This is a core technique for controlling costs. Different detection types have different unit prices, and order matters:

1. Start with basic registration/validity check: For example, “Telegram registered” or “WhatsApp valid” – these are the cheapest. After filtering out invalid numbers, the remaining valid pool will be much smaller.
2. Then perform advanced checks on valid numbers: Such as “Telegram activity (7/15/30 days)” or “Gender detection.” This way, only valid numbers undergo the more expensive checks, keeping costs under control.

For example, if you have 1 million numbers, perhaps only 600,000 are registered on Telegram, and of those, 400,000 are active. If you submit all 1 million for “activity check” directly, you’ll waste detection fees on 400,000 never-registered numbers. The correct approach: first run the registration check on 1 million numbers, get 600,000 registered numbers; then run the activity check on those 600,000, not on all 1 million.

Step 3: Task Submission and Monitoring – Ensuring 100% Completion

Submitting a task in the KK-DATA console is straightforward:

1. Select the detection platform (Telegram / WhatsApp / iMessage / RCS, etc.).
2. Upload the number file or select a pre-generated number list.
3. Choose the detection type (registration, validity, activity, gender, etc.). The system will display an estimated fee.
4. Enable Telegram notifications: You’ll receive a message on your Telegram when the task completes, allowing prompt access to results.
5. Click Submit.

After submission, you can monitor real-time progress in the console’s task list. If your balance is insufficient, the task will enter a “waiting for top-up” state, not fail automatically. You can recharge using USDT (TRC20), with a minimum of approximately 50 USDT; once credited, the task resumes automatically.

Step 4: Result Processing and Export – Efficiently Filtering Useful Data

After the task completes, follow these recommendations when exporting results:

• Choose the right format: CSV works well for Excel or database analysis; TXT is suitable for importing into other tools. CSV is recommended for its clear fields.
• Export in segments as needed: Avoid exporting the entire result at once. The console supports filtering by detection result (e.g., export only “active” or “female” users), reducing file size.
• Leverage activity fields: For example, export users who were “active on Telegram in the last 7 days” as the first target for private messaging campaigns.

Recommended workflow: Generate → Clean and deduplicate → Split tasks → Basic detection → Advanced detection → Segment export.

Cost Optimization – How to Control Million-Level Screening Costs

KK-DATA charges per number, with no subscription plans. This means you only pay for each number that is successfully detected. Invalid numbers (empty, unregistered) are usually not charged, but check the platform’s specific rules. The following optimization tips can save you money:

• Deduplicate first: As mentioned, deduplication can reduce repeated detections by 10%–30%.
• Low-level before high-level: Basic checks (registration/validity) have lower unit prices; advanced checks (activity/gender) have higher prices. First, use low-cost checks to filter a valid pool, then run high-cost checks on that pool.
• Set activity windows wisely: If you need users active in the last 7 days, don’t select “30 days activity.” Although the latter yields richer results, it costs more and covers a broader set, increasing expenses.
• Avoid submitting too many tasks simultaneously: Some users submit 10 million-level tasks at once to speed things up, but this increases system load and slows processing. It’s better to stagger submissions.

Common Pitfalls to Avoid

• Ignoring number formatting: Spaces, parentheses, or inconsistent + signs can cause detection errors. Standardize to E.164 format (country code + number, e.g., 8613800138000).
• Submitting tasks with insufficient balance: Tasks will enter a waiting state, not fail automatically, but won’t recharge automatically either. Remember to top up with USDT in advance.
• Forgetting to enable notifications: Million-level tasks may take several hours. Enabling Telegram notifications lets you receive results immediately without repeatedly logging in to check.
• Not verifying detection type definitions: For example, “Telegram valid” and “Telegram active” have different meanings; selecting the wrong one can produce inaccurate data. Read the documentation first.

Frequently Asked Questions

Q: Will a task with 1 million numbers fail?

A: KK-DATA supports up to approximately 1 million numbers per task, but it is recommended to split based on number quality and network conditions (e.g., batches of 50,000–100,000) to reduce overall failure risk. If interrupted mid-way, completed portions are still charged.

Q: How can I tell if numbers have been submitted repeatedly?

A: Use KK-DATA’s data deduplication repository. Upload your numbers, and the system automatically deduplicates across tasks. It’s recommended to deduplicate before each generation or import to avoid wasting balance.

Q: How long does million-level screening take?

A: Time depends on number quantity, platform query speed, and current system load. Typically, detection of 1 million Telegram registrations may take several hours; activity detection takes longer. Splitting tasks allows phased execution.

Q: Is the balance deducted based on the estimated fee at submission or actual detection results?

A: An estimated fee is shown before submission, but actual deductions are based on the number of successfully detected numbers. Invalid numbers (e.g., empty numbers) are generally not charged; see console prompts for specific rules.

Q: How to handle exports that are very large?

A: Export by country or filter by detection result (e.g., only active numbers), or use CSV split features. If a single export exceeds 500,000 rows, consider using the API or splitting into multiple smaller files.

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Start your million-level screening task now! Log in to the KK-DATA Console to experience the integrated workflow: generation, deduplication, splitting, detection, and export. For more detailed instructions, check the documentation. If you have any questions or need a customized splitting plan, contact Telegram support @kkdata_robot directly.