Guide to splitting large batch number screening tasks: an efficient operation plan for million-level number verification

In the overseas customer acquisition business, number screening is the core link to verify whether the target customers are reachable. When you need to process hundreds of thousands, millions or even more numbers, submitting all the data directly into one task often results in timeouts, data loss, or balance loss. This article will explain in detail how to split the task of screening large batches of numbers to help you improve efficiency, reduce costs, and ensure data accuracy.

What is bulk number screening? Why do you need to split tasks?

Large-volume number screening refers to a number verification task with a single processing scale of 100,000 to one million numbers, which usually involves multi-platform detection such as Telegram, WhatsApp, Line, and Zalo. Different from daily small batch screening, large batch operations have higher technical threshold and management complexity. Directly submitting a full task may lead to three major risks.

Typical scale and business scenarios of large batch number screening

• Before e-commerce promotion: Filter out Telegram active users from 5 million registration numbers for mass notification.
• Social Media Operation: Verify WhatsApp activation status and gender of 2 million potential customer data at the same time.
• Independent station promotion: Screen out effective male Line users from the 100,000 new numbers added every day for targeted push.

In these scenarios, submitting millions of numbers to a single detection task at a time can easily exceed the platform’s processing limit or cause the server to respond to a timeout.

Three major risks of not splitting tasks

1. Timeout Interruption: The platform has a maximum data limit for a single task (such as about 1 million). When exceeded, the task may be truncated or fail directly.
2. Double Deduction: If you need to retry if it fails midway, the detected part may be charged repeatedly, resulting in a waste of funds.
3. Confusing results: Mixing tasks of multiple detection types (such as detecting Telegram activity and WhatsApp gender at the same time), exporting files with mixed fields, and high post-parsing costs.

Task splitting is to decompose a large and comprehensive task into multiple small and independent subtasks. Each subtask only does one thing, thereby improving the success rate and controllability.

Core principles for splitting large batch number screening tasks

Splitting is not a random division, but is based on the following three principles, each of which directly affects the final effect.

Principle 1: Split by platform

The detection logic and speed vary greatly between platforms (Telegram, WhatsApp, Line, Zalo, etc.). Mixing multiple platforms into one mission can result in slow platforms slowing down overall progress. Therefore tasks should be created separately for each platform. For example:

• Task A: Telegram activation detection (1 million messages)
• Task B: WhatsApp activity detection (1 million messages)

Principle 2: Split by detection type

Within the same platform, different detection types (activated/active/gender/other fields) often have independent resource consumption. Avoid putting “activation detection” and “active detection” in the same task, because active detection takes longer and the result fields are different, making it easy to make errors when merging and exporting. Suggestions:

• Submit the activation detection task first to obtain all activation numbers.
• Submit active detection tasks to the subset of subscribed numbers.

Principle 3: Split by data source

If you use Global Number Generation and Private Number Import at the same time, it is recommended to submit them separately. The generated numbers are highly random and have a low detection hit rate; the imported numbers are mostly existing user data and have a high hit rate. Separate processing helps analyze the quality of different sources and avoids a large number of invalid data in generated numbers from affecting the detection progress of imported numbers.

How to efficiently split large batches of numbers?

Taking the KK-DATA platform as an example, the actual operation steps include data preprocessing, batch submission and result merging.

Data preprocessing: deduplication and sharding

1. Deduplication: Use the platform’s built-in Data Deduplication Warehouse to upload all numbers to be detected. The system automatically compares historical tasks, marks the detected numbers and skips them to avoid repeated deductions. This step reduces the effective amount to be detected by 10%–30%.
2. Sharding: Split the deduplicated numbers into multiple CSV files in groups of 50,000 to 100,000. The shard size depends on the platform’s single task processing capability (KK-DATA has a maximum of about 1 million items at a time, but it is recommended to be within 100,000 for faster speed). It is recommended that the file name be numbered, for example batch_01.csv.

Submit in batches: Use the console to create tasks in batches

Log in to the Application Console, select the corresponding platform (such as Telegram), the detection type (such as “Activate Detection”), and upload the fragmented file. KK-DATA supports submitting multiple tasks at the same time, and the system will queue them for processing. You can arrange the submission order reasonably according to the number of tasks:

• Submit all “Activation Detection” tasks first, and then export the activation number as input for the “Active Detection” task after all are completed.
• Utilize the platform’s task notification function to receive completion reminders through Telegram without repeatedly refreshing the page.

Result merging: multi-task export and data cleaning

After each task is completed, export a CSV file. Use Excel or a script to combine all results:

1. Unify column fields (for example, keep “number, platform, activation status, active status, gender”).
2. Deduplicate according to the “Number” column (if there is duplicate detection across tasks, the deduplication warehouse has been processed, but you can deduplicate again for insurance).
3. Filter out the numbers in the target status (such as “Active-Yes” and “Gender-Male”) and generate the final sending list.

Common pitfalls and countermeasures in large-volume number screening

Even if the tasks are divided, there will still be some pitfalls in actual combat. Knowing and preparing countermeasures in advance can make the process smoother.

How to avoid wasting balance due to repeated detection? Make good use of data deduplication warehouse

Before submitting a new task each time, upload the number to the deduplication warehouse for comparison. KK-DATA’s deduplication warehouse will record the historical detection records of each number (stored independently by platform and detection type). If the number has been detected and “Telegram is activated and valid”, it will be automatically skipped when submitting the same type of detection again, and no fee will be deducted. This can save 10%–50% on the cost of invalid detection.

How do differences in detection speed between different platforms affect split batch sizes?

• Telegram active detection: usually the fastest, 100,000–200,000 messages can be set in a single batch.
• WhatsApp Gender Detection: Second, 50,000–100,000 messages are recommended in a single batch.
• Line Gender Detection: Relatively slow, 20,000–50,000 items are recommended for a single batch.
• Zalo Test: For the Vietnam region, network delay affects the speed, and the batch size should not exceed 30,000.

Adjusting the shard size according to the platform can maximize the parallelism of all tasks and reduce the total waiting time.

Task completion notification and exception handling

Check “Notify on completion” when submitting a task in the console, and the system will send a result link through the Telegram bot. If a task is unresponsive for a long time (more than 3 times the estimated duration), it is recommended to cancel and resubmit. KK-DATA provides a task status query page, where you can view progress and error details at any time.

Comparison before and after a successful large-volume number screening practice

Assume that a Vietnamese e-commerce team needs to screen 500,000 Zalo numbers to attract new customers for weekend promotions. Goal: Find “open and active” male users among them.

Inefficient process of not splitting tasks:

• Directly submitted 500,000 Zalo mixed detections (activated + active + gender), and the task timed out and was interrupted after running for 8 hours.
• After resubmitting, the system detected that some numbers had been detected, but due to the mixed task design, 100,000 numbers were repeatedly detected, resulting in an over-deduction.
• It ended up taking 3 days, spending 150% of the budget, and the result data fields were messy, and manual cleaning took another 1 day.

Efficient process after splitting according to the strategy of this article:

1. Deduplication: Upload 500,000 items to the deduplication warehouse, exclude the 50,000 items that have been detected, and leave 450,000 items.
2. Split: Divide it into 15 tasks, each with 30,000 items, and submit them all to “Zalo activation detection”.
3. Completion Notification: All tasks will be completed after 7 hours, and 300,000 activated numbers will be obtained.
4. Second split: Divide 300,000 items into 10 tasks, each with 30,000 items, and submit “Zalo activity detection + gender detection”.
5. Merge: 80,000 active male numbers will be obtained after 10 hours, which will be used directly for promotion.

• Total time spent: 17 hours (including waiting and merging), the cost was controlled at 95% of the budget, and the data was accurate and clean.

Key changes: Time is shortened to 1/4 of the original, cost savings are 35%, and manpower input is reduced by 80%.

5 key points to pay attention to when choosing a large-volume number screening platform

When you need to process large batches of number screening for a long time, choosing the right platform can get twice the result with half the effort. The following is the core assessment:

Key Points	Description
Multi-platform support	Should cover mainstream customer acquisition channels such as Telegram, WhatsApp, Line, Zalo, iMessage, RCS, Viber, etc.
Maximum task size	What is the maximum number of items supported at a time? Does it support parallel tasks? Can I flexibly adjust the batch size? KK-DATA supports about 1 million items at a time and can submit multiple tasks at the same time.
Billing Transparency	Is billing based on items? Can I see the estimated cost before submitting? Avoid hidden charges. For details, please see [official website billing page] (https://kkdata.cc/billing/) and [console real-time price] (https://app.kkdata.cc/).
Deduplication capability	Is there a cross-task data deduplication warehouse? Avoid wasting balance through repeated testing.
Data export format	Supports CSV, TXT and other standard formats, with clear fields (such as number, platform, activation status, active status, gender, age, etc.).

KK-DATA has complete support in the above five aspects. You can go directly to https://kkdata.cc/ for details.

FAQ

**Q: Is it necessary to split the task when screening large batches of numbers? **

Answer: Not necessarily, but based on detection success rate and cost control, it is recommended that a single task be controlled within 100,000 items. If it exceeds 500,000 items, it is strongly recommended to split it. The basis for splitting is the platform’s limit on the amount of task data and your need for fine-grained management of results.

**Q: How to avoid repeated detection of numbers in split tasks? **

Answer: Use a platform that supports cross-task deduplication, such as KK-DATA’s data deduplication warehouse function. Before submitting a new task, the platform will automatically compare existing detection records, skip the detection numbers, and will not deduct fees repeatedly.

**Q: How long does it usually take to screen large batches of numbers? **

Answer: The time taken depends on the total number of numbers, the type of detection platform and the network environment. Telegram activity detection is generally faster; Line gender detection is relatively slow. Parallel submission of multiple tasks after splitting can significantly reduce the total time consumption.

**Q: How to control the cost-effectiveness of large-volume number screening? **

Answer: First test the detection hit rate and unit price of different platforms through small samples, and calculate the estimated value per 10,000 items; then use the deduplication function and batch strategy to avoid waste; finally select in-depth detection fields such as activity and gender as needed instead of enabling detection in full.

**Q: Splitting tasks will cause the result data to be scattered. How to integrate it? **

Answer: Most platforms support CSV export. You can merge files exported by multiple tasks and then use Excel or scripts to remove duplicates. Some platforms (such as KK-DATA) support viewing summary statistics of all historical tasks on the console to simplify integration work.

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👉 Log in to the console to start filtering numbers Immediately experience the splitting of large batch number screening tasks; if you need help, you can contact customer service https://t.me/kkdata_robot for consultation. The official website https://kkdata.cc/ and usage documentation https://docs.kkdata.cc/ provide more guidance.