How does tg US data help local services acquire customers? A practical guide to area codes, time zones and data validity

If you are operating local services in the United States - whether it is food delivery, housekeeping, maintenance, or real estate rental - you must have a deep understanding: Customers are in your city, but you cannot reach them efficiently. Traditional mass text messages are blocked and email opening rates are getting lower and lower. However, Telegram, with its high activity and innate group/private message mechanism, is becoming the preferred customer acquisition channel for overseas local service teams. But the question also arises - is the US phone number you got real? Is the user really online?

This is exactly the nuclear problem tgUSDATA is trying to solve. This article will help you build a truly practical customer acquisition path for local services in the United States from the three dimensions of area code, time zone and data validity, combined with KK-DATA’s practical process.

Why do US local services need accurate tg US data?

The core pain points of local service promotion: activity and response time

The particularity of local services in the United States lies in timeliness. For example, if you push a pizza offer at 12 noon, if the user is sleeping or working at that time, the message will be basically useless. Ordinary global random number screening can only tell you whether the number is registered with Telegram, but it cannot tell whether the user is active, let alone determine his current time zone.

A more insidious problem is the area code trap: a +1 New York number (area code 212), the user is entirely likely to move to Los Angeles (Western US time) or live in Hawaii. The area code only represents the place where the number is registered, not the user’s current geographical location.

What specific problems can tgUS data solve?

Through precise filtering of Telegram US number data, you can:

• Distinguish between registered and active: Filter out users who have logged in within a specified time period (such as 9-18 U.S. local time) in the past 7 days to ensure that your message is delivered when the other party is online.
• Match Service Object: Enable gender/age detection. For example, manicure services for female users can target female users; moving services can target male users, and the age field can help you avoid low-conversion groups such as minors.
• Export tgid for secondary contact: The tgid field in the filtering results supports subsequent mass sending of private messages, forming a complete link of “number generation → active filtering → targeted content push”.

What is the relationship between area code, time zone and data validity?

Area code ≠ geographical location, but area code ≠ useless information. The correct combinational logic is:

1. Use area code to delimit the number pool: Select target city number segments such as New York (212/646/917), Los Angeles (213/310), Houston (281/346), etc. to generate a preliminary screening number list.
2. Use activity detection to reverse the time zone: The Telegram server records the user’s last online time (UTC timestamp). KK-DATA’s “tg active” detection can set an active window. For example, if you set “Active from 11:00-13:00 US Eastern Time”, the system will filter out users whose last login time in the past 7 days was around noon US Eastern Time - these users are most likely to be in the US Eastern Time Zone and online during this period.
3. Test validity: The shorter the active window and the more precise the conditions, the higher the validity of the filtered data. Blindly using “Active in the past 30 days” will mix in a large number of non-local or low-active users.

Best Practice: First determine the city → then determine the local time period → finally conduct a small batch test (500-1000 items) to verify the response rate.

How to use KK-DATA to filter high-quality tg US data?

Below is the complete step-by-step process, completed directly in the App Console.

Step 1: Generate or upload a list of numbers with US area codes

• Using KK-DATA’s Global Number Generation module.
• Select the country “United States” and enter the area code +1.
• Can be further refined to a specified number range (such as 212 New York, 310 Los Angeles), or directly generate a random US number.
• Uploading custom CSV is also supported: prepare a list of US numbers in advance and import it.

Step 2: Configure the screening task—enable tg active detection and set the time window

• Select the “Telegram” platform in the screening task.
• Detection Type Check the required items such as “tg activated”, “tg active”, “tg gender”.
• Key configuration: Set time window for “tg active”. For example:
  • New York Food Delivery Team: Active windows set to 11:00-13:00（美东） and 17:00-19:00（美东）
  • California Housekeeping: Active window set to 09:00-11:00（美西）
• Click “Start Task” and the system will first display the estimated deduction amount.

Step 3: Overlay gender/age fields for further targeting

• Enable “tg gender” detection. The result fields will contain Gender (male/female) and Age Number.
• The age field can be used to filter people around 30 years old, which is especially suitable for scenarios such as home services, insurance, real estate, etc. that are promoted to middle-aged groups. Note: This is an inference based on public information, not ID card level accuracy.

Step 4: Use deduplication warehouse to avoid duplicate detection

• KK-DATA’s data deduplication warehouse supports automatic comparison across tasks.
• When you import a new number list, the deduplication warehouse will automatically remove numbers that have been detected before to avoid repeated deductions of balances.
• After the task is completed, export the results to CSV (including fields such as tgid, gender, active time period, etc.) or TXT for subsequent private messages or CRM use.

Practical case: How does an American Chinese takeout platform use tg US data to improve order conversion?

The following scenarios are reasonable assumptions, based on common operational logic and do not involve specific customers or numbers.

Background

An American food delivery platform targeting the Chinese community hopes to promote its delivery services in Chinese-populated areas in New York and California. The user group is mainly Chinese people aged 25-45 who have the habit of ordering food regularly.

Filter configuration points

• Area code selection: New York - 212, 646, 917 (Manhattan/Flushing); California - 310, 415, 408 (Los Angeles/San Francisco/Silicon Valley)
• Active Window: Set in two time periods - US local time 11:00-13:00 (lunch preparation period) and 17:00-19:00 (before dinner), covering the period when orders are most likely to occur.
• Gender/Age: No gender restriction, age field filtering 25-45 years old, excluding minors and elderly users.
• Deduplication settings: Enable deduplication warehouse to avoid repeated detection of different batches of New York/California number lists.

Use effect comparison

• Before time zone filtering was used, it took about 12-15 clicks to send 1,000 messages (basically random access, including a lot of time differences).
• After applying the above configuration, the reach rate of the same number of messages is increased by about 30% (from 12-15 clicks to 16-20 times), and the complaint/report rate caused by sending in the early morning is significantly reduced.
• The activation detection filters out about 20% of inactive numbers, saving subsequent push resources.

Three mistakes often made when using tg US data

Misunderstanding 1: Only look at the area code, not the active period

Area code = number pool, active period = validity. Both are indispensable. Filtering only by area code is equivalent to getting the address book of all New York, but everyone you call is asleep.

Misunderstanding 2: Importing hundreds of thousands of numbers at a time consumes a lot of balance but produces low output

It is recommended to do small batch testing: first run 5000-10000 numbers, adjust the parameters based on the results (proportion of valid numbers, activity rate, gender distribution), and then gradually increase the volume. If you directly invest the full amount, there is a high probability that a large amount of balance will be wasted on invalid numbers.

Misunderstanding 3: Ignoring data deduplication and repeatedly detecting the same batch of numbers

Each time the test is repeated, a fee will be deducted. Using KK-DATA’s duplication warehouse, you can automatically compare across tasks to avoid repeated detection of the same number. After a detection, the status of the number will be recorded, and subsequent imports will be skipped directly.

FAQ

**Q: Can tg US data be accurate to the city where the user is located? ** Answer: No. The area code only indicates the area where the number was registered, not the real-time location. However, it can be combined with the time distribution of tg active detection to roughly infer the user’s time zone (Eastern United States/Western United States, etc.) to plan the best sending period.

**Q: How to ensure that the filtered numbers are indeed currently active US users? ** Answer: Use KK-DATA’s “tg active” detection and specify the active window in the past 7 days (such as 9-18:00 ET). The system will determine whether there is a match based on the user’s last online time to ensure data validity.

**Q: Can the filtered tgid be used for sending group private messages? ** Answer: Yes. The export results include the tgid field, which can be used for subsequent secondary touches. Please be sure to abide by the Telegram platform rules, control the frequency of sending, and avoid account restrictions.

**Q: Is number generation and number screening free? ** Answer: The number generation module is completely free. The screen number is deducted from the balance based on the number of tests. The specific unit price varies depending on the platform and test type. For details, please see the real-time price on the console or [official website billing page] (https://kkdata.cc/billing/).

**Q: What should I do if the number of active users obtained after generating the filter is insufficient? ** Answer: It is recommended to relax the active window (for example, change it to “past 30 days”), or reduce the gender/age filtering conditions, expand the number pool, and then screen numbers. Don’t import a large number of numbers that don’t need to be checked at one time just to pursue quantity, as this will increase useless costs.

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The real value of TG US data is not “how many numbers there are”, but whether it can appear in front of users in an appropriate way when they are online. If you are worried about the customer acquisition effect of local services in the United States, you might as well start by configuring a small batch screen number.

👉 Log in to the console to start screening numbers Two-way contact customer service https://t.me/kkdata_robot For complete usage guidelines, please refer to the document https://docs.kkdata.cc/