Zalo activates data and LLM: How precise screening can improve AI models and corporate customer acquisition efficiency

#zalo activates data and LLM: How precise screening can improve AI models and corporate customer acquisition efficiency

Among overseas customer acquisitions in Vietnam and Southeast Asian markets, Zalo is a dominant social tool, covering more than 75 million monthly active users. However, the first problem that many operation teams face is that although they have a large number of mobile phone numbers on hand, it is impossible to determine which numbers are actually registered with Zalo, let alone which users are active and their gender. This is where zalo activation data comes into play - through technical detection, it is confirmed whether the number has been activated for Zalo, and it is accompanied by structured tags such as activity, gender, age (partially). This article will provide an in-depth analysis of how zalo activation data can become a high-quality data source for LLM (large language model) training, and how to achieve precise reach in corporate customer acquisition.

What is zalo activation data? Why has it become a necessity for LLM and overseas operations?

Zalo activation data, simply put, is a structured data set formed by batch testing whether the number is registered with Zalo and whether it has been active recently, and extracting the public fields in the user profile (such as gender, age group, avatar, etc.). Its core value is: Convert uncontrollable ordinary mobile phone numbers into verified Zalo user portraits.

The essential difference between zalo activation data and ordinary mobile phone number data

Dimensions	Ordinary mobile phone number data	Zalo activation data
Whether it is possible to determine social platform registration	Unable to determine	Verified as a registered Zalo user
Activity information	None	The latest active time window can be provided (such as active within 7 days/30 days)
Gender/age tag	None	Derived from user profile, can be used for group analysis
Data noise	High (a large number of empty numbers and silent numbers)	Low (only real users are retained)
Scope of application	General communications	Accurate access to social scenes in Vietnam/Southeast Asia

If the overseas operation team misuses ordinary mobile phone numbers for marketing, it will not only waste costs, but may also cause the platform to ban the account due to sending invalid messages. And zalo activates data, allowing you to face real users every time you reach out.

Why does LLM need real social platform data as training material?

The generalization ability of large language models (LLM) is highly dependent on the quality and diversity of training data. If Southeast Asian language and user behavior characteristics are missing from the training data, the model will be significantly weaker in Vietnamese dialogue and local context understanding. The zalo activation data provides:

• Real user portraits: Gender and age distribution, helping the model understand the structure of Zalo’s user group.
• Regional distribution characteristics: Through the country code of the number (Vietnam +84, etc.), the model can learn regional language usage habits.
• Active Behavior Pattern: Active “time window” data can be used to train user behavior sequence models (such as predicting user response probability).

After desensitization, these data can be used as auxiliary features for LLM fine-tuning to improve the performance of the model in Vietnam/Southeast Asia scenarios.

What core fields does zalo activation data contain? How to export?

The sieve number platform represented by KK-DATA can output the following typical fields in the Zalo sieve number task (subject to the actual export by the console):

Field name	Meaning	Purpose
phone number	original number	primary key identifier
Whether to activate	true/false	Distinguish Zalo registered users
Activity	Last 7 days/30 days/generally active	Filter out silent accounts
Gender	Male/Female/Unknown	Gender Targeted Marketing
Age (part)	Age group filled in by user	Auxiliary age group filtering
uid	Zalo internal user ID	Cross-task deduplication and association of public data
Detection time	Task completion time	Data freshness mark

The export format supports CSV or TXT and can be downloaded directly to the local computer. For data preprocessing for LLM training, the CSV format is the easiest to parse and can be read directly with Python’s pandas.

Accuracy instructions for gender and age fields

It should be emphasized that the gender and age data come from the user’s voluntary filling in the Zalo profile and are not ID cards or official certification information. Some users may leave it blank or fill it in incorrectly. Therefore, the age field is only suitable as a group trend reference (such as “people around 30 years old”) and cannot be used as a precise personal identity identifier. In LLM training, it is recommended to divide the age into intervals (such as 20-29, 30-39) to avoid the model overly relying on imprecise values.

uid exports the value of LLM data association

uid is the unique identifier of the user within the Zalo platform. If you also obtain data from other compliance channels (such as user public posts, interaction records), you can use uid to perform cross-source correlation (you need to ensure data compliance yourself). This can make LLM’s training features richer—for example, associating the user’s speaking style with his gender and active period, helping the model learn more fine-grained conversation patterns.

How to obtain high-quality zalo activation data? (Comparison of screening process and tools)

There are usually two ways to obtain zalo activation data:

1. Manual verification: Manually search or send messages one by one to test. The disadvantages are obvious - extremely low efficiency (1,000 entries may take several days), easy to trigger platform risk control, and cannot obtain fields such as activity level/gender.
2. Platform batch number screening: For example, KK-DATA supports a single submission of up to about 1 million numbers, automatically completes the detection, and notifies the export after completion. Billed per item, no subscription packages.

The standard pipeline is as follows:

1. Number preparation: You can prepare the number to be tested through the platform’s built-in “Global Number Generation” module (random generation in 240+ countries, import of number segments, and custom CSV import).
2. Submit detection task: Select the Zalo screening type (activation detection, active detection, gender and age detection) in the console, upload the number file, and the system will display the estimated cost.
3. Task Notification: After the task is completed, check the results through Telegram or the platform. If you use KK-DATA’s [Two-way Contact Customer Service] (https://t.me/kkdata_robot), you can also proactively receive task completion reminders.
4. Export results: Download CSV/TXT, and then perform subsequent analysis or LLM preprocessing.

Specific application scenarios of zalo activation data in LLM training

Although the zalo activation data does not contain conversation content, it can be used as structured context features in LLM training to improve the model’s performance in specific scenarios.

Used for user profiling and recommendation systems

Suppose you are training a recommendation model for Vietnamese users (such as cross-border e-commerce product recommendation). Using the gender, activity, and age group of zalo activation data as user-side feature inputs, the recommendation system can make a preliminary judgment on user preferences during the cold start stage. For example: highly active men aged 30-40 may be more interested in electronic products. Compared with purely random recommendations, CTR can be increased by 15%-30% (industry reference).

Assisted cross-border customer service robot training

Cross-border customer service robots need to understand the communication habits of local users. Using the active period distribution in zalo activation data, you can simulate the most common user inquiry time; combined with gender groups, you can also design different reply tones. For example, when targeting female users in Vietnam, bot responses can be softer and use more polite honorifics. These optimizations rely on the statistical distribution of real data rather than guesswork.

Regional market language model enhancement

Vietnamese has unique linguistic features (such as six tones, a large number of loanwords). If you use an LLM trained with general Chinese or English corpora to do Vietnamese dialogue, the effect is often not good. The zalo activation data provides the real social behavior context of Vietnamese users: multi-dimensional information such as active period, account age, etc., which can help the model perceive “whether this user is active or lurking”, and then adjust the response strategy. For example, the robot can push real-time updates to users who have been very active in the past seven days; and send promotional emails to low-frequency users.

Compliance and cost issues that must be paid attention to when using zalo activation data to do LLM

Any use of data must comply with laws, regulations and platform terms. When using zalo to activate data, please pay attention to the following points:

• Data source legality: Check whether the number is registered with Zalo through the number screening platform, which is a third-party API level verification and does not involve crawling users’ private data. However, please do not use the data for illegal purposes such as harassment and fraud.
• Personal Privacy Protection: Zalo activation data only includes publicly available user information and technical test results (such as whether it is activated), and does not include chat content and friend lists. In LLM training, it is recommended to perform desensitization (such as replacing uid with a random ID) to avoid direct association with personal identity.
• Pay-as-you-go billing strategy: KK-DATA adopts the balance recharge + per-item deduction model, without subscription packages. The minimum recharge is about 50 USDT (TRC20), and the unit price is different for different detection types (see official website billing page for details). New tasks cannot be submitted when the balance is insufficient and you need to recharge first.
• Cost control skills: Combined with the platform’s data deduplication warehouse, automatically identify detected numbers across tasks to avoid repeated deductions. At the same time, choose the detection type reasonably: If you only need to verify the activation status, do not check activity + gender detection, which can save about half of the cost.

Common misunderstandings and best practices of zalo activation data filtering

Many teams that are new to screen number operations are prone to making the following mistakes:

• Only screen for activation, not active: As a result, a large number of “zombie accounts” are reached, and the actual message opening rate is extremely low.
• Ignore gender/age filtering: Sending messages in groups regardless of group is a waste of budget and can easily cause resentment among users.
• Do not use task notifications: Large tasks (hundreds of thousands of items) may take several hours, and not setting notifications will delay the subsequent process.
• No deduplication: Duplicate detection wastes balance, and duplicate samples in the training data will affect the convergence of the LLM model.

Why is it recommended to combine multi-platform collaborative screening such as TG/WhatsApp?

A single platform has a limited user base. For example, Vietnamese users not only use Zalo, but many also use Telegram and WhatsApp. By screening multiple platforms at the same time through KK-DATA (supporting Telegram, WhatsApp, Line, Zalo, Viber, etc.), you can get a copy of the number’s activation status on different social platforms**. These cross-platform data are particularly valuable for LLM training - they can build a “user multi-platform behavior matrix” to help the model understand which users are “cross-platform actives” and which are “Zalo exclusives”. In this way, in recommendation systems or customer service robots, messages from different channels can be pushed according to the user’s platform preferences.

How to avoid duplicate detection in data deduplication warehouse?

Before using the screening platform, upload historical detected numbers to the data deduplication warehouse, and the system will automatically mark and filter out duplicates. In this way, every time a new task is submitted, only undetected numbers will be deducted. For the LLM training data set, deduplication can also ensure the uniqueness of each sample and avoid overfitting of the model due to repeated data. KK-DATA’s records show that rational use of deduplication warehouses can save an average of 20%-40% of costs.

FAQ

**Q: What is the difference between zalo activation data and ordinary Zalo account data? ** Answer: The activation data only includes whether the number has been registered with Zalo, its active status, and the user’s public information (gender, age, etc.), and does not include private content such as chat history and friend list. Regular Zalo accounts have a wider range of data, but the risk of obtaining compliance is higher.

**Q: Can zalo activation data be directly used to train LLM dialogue robots? ** Answer: Yes, but the fields in the activation data (such as gender, age, area code) must be used as part of feature engineering to help the model understand the distribution of user groups. Direct training of dialogue models still needs to be based on real dialogue samples, and this data can be used as auxiliary context.

**Q: Does filtering Zalo activation data violate Zalo platform rules? ** Answer: Using a number screening platform (such as KK-DATA) to detect whether a number is registered with Zalo through technical means is a third-party API level verification behavior and is generally not considered illegal crawling. It is recommended to abide by the target platform’s terms of service and only use it for legitimate business communications.

**Q: How many pieces of zalo activation data can be filtered at most at one time? ** Answer: Depending on the platform capabilities, approximately 1 million numbers can be submitted for testing in a single task. The actual configuration of the console shall prevail.

**Q: Is the age field in the zalo activation data accurate to the specific year? ** Answer: Not precise. The age field comes from personal information filled in by users. Some users have not filled it in or filled it out incorrectly. Therefore, it is only suitable for trend analysis (such as “about 25-35 years old”) and cannot be used as ID-level data.

**Q: How much recharge is required to filter zalo activation data? ** Answer: The platform adopts a balance system, with a minimum recharge of approximately 50 USDT (TRC20), and fees are deducted on a per-item basis. Different detection types (activated only vs activated + active + gender) have different unit prices. Please check the real-time price on the console for details.

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Mastering zalo activation data is a key step for LLM optimization and overseas customer acquisition. Experience the efficient screening assembly line now:

👉Log in to the console to start screening numbers 🤖 Two-way contact customer service: https://t.me/kkdata_robot 📚 Usage documentation: https://docs.kkdata.cc/ 🌐 Official website: https://kkdata.cc/