Actual test results of Telegram marketing number detection: A set of screening methods can directly double the conversion rate

Many teams are doing itWhen marketing on Telegram, you will go through a very typical stage: more and more accounts are added, the number of groups is getting larger and larger, but the actual conversion does not increase simultaneously.On the surface, it seems to be a content issue, but after multiple rounds of actual testing, it was found that more core issues often appear in"Before the number enters the system".

A real batch import test

In a cross-border community operation project, the team imported about100,000 Telegram-related number data, used for group messaging, private messaging, and community recruitment.

There is no screening in the initial stage and the marketing process is entered directly. The results are as follows:

lMessage delivery rate is low

lGroup invitation pass rate is unstable

lUser response rates are obviously stratified

lA large number of accounts do not have any interactive feedback

Overall, the data scale is large, but the proportion of effective responding users is low.

The problem is notTelegram, while in data structures

After further analysis, it was found that this batch of data was not from a unified source, but had a mixed structure:

lAdvertising investment retention data

lHistorical registered user data

lCollect data through third-party channels

lCross-import data from multiple platforms

This structure leads to a problem: real users are mixed with low-quality accounts.

What happens to unfiltered data?

Without filtering,Several typical phenomena will occur in Telegram marketing:

1. Reach rate decreases

Some accounts do not exist at all or have expired.

2. Interaction is extremely scattered

A small number of real users gave centralized feedback, while a large number of accounts were unresponsive.

3. System learning distortion

Subsequent automated contact model judgments were incorrect.

4. The quality of the community has declined.

Within the groupThe proportion of "silent users" is too high.

These issues directly impact the overall conversion path.

Add a second round of testing after screening

After the first round of data was run, the team introduced number screening logic and processed the same batch of data again.

Filter dimensions include:

lIs Telegram really registered?

lIs there any active behavior on the account?

lWhether it has cross-platform behavioral characteristics

lWhether it is a batch registration source

lWhether it has been inactive for a long time

After filtering was completed, the data was re-stratified.

Changes after data stratification

After processing,100,000 pieces of data are divided into three categories:

High quality users

Can be directly used for private chat conversion and community operations.

medium quality users

It needs to be nurtured through content and pacing.

Invalid or low-quality users

Eliminate them directly without entering the marketing link.

The result is a reduced amount of data but a clearer structure.

Why conversion changes become obvious

In the second round of testing, there were significant changes in marketing metrics:

lIncreased effective response rate

lThe proportion of group interaction increases

lPrivate chat conversion path is more stable

lIncreased success rate of automated contact

The key to change is not"What should be sent more", but "only to whom".

The real problem with Telegram marketing is “user mixing”

Many teams easily misjudge a phenomenon:

I think the inactivity of the group is a content issue.

But in fact, many groups have a large number of unreachable or low-quality accounts mixed in from the beginning.

These accounts will not reply, interact, or convert, but will continue to lower overall data performance.

Digital Planet’s role in the selection process

In this actual measurement, the data screening process is enteringTelegram marketing was done before.

The entire processing process roughly includes:

First perform basic number cleaning to remove duplicate and invalid data.

then enterTelegram special detection:

lDetermine whether the account actually exists

lCheck whether there is active behavior

lIdentify long-term silent accounts

lFlag exception bulk registration source

Then conduct cross-platform correlation analysis:

lWhatsApp status recognition

lFacebook registration status analysis

lTikTok behavioral characteristic matching

Finally, the structured result is output:

lCan directly market users

lNeed to cultivate users

lInvalid user pool

These results directly determine subsequent reach strategies.

Marketing logic is changing

Telegram marketing is shifting from "bulk-driven" to "layered operations."

Past concerns include:

lNumber of people in the group

lSend quantity

lCoverage

What I am more concerned about now is:

lActual active ratio

lResponse speed

lUser quality structure

lConversion path completeness

When the user structure is reconstructed, the marketing methods will also change accordingly.

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