existIn Twitter marketing scenarios, many teams will encounter a problem: they have already screened Twitter accounts and used the Twitter batch screening tool, but the private message response rate is still unstable and the event participation rate fluctuates significantly. The question is usually not "whether there is filtering", but "whether the filtering logic is precise enough".

In 2026, as social platform algorithms change and the pace of user activity accelerates, just doing basic registration testing is no longer enough. What can really widen the conversion gap is systematic screening of active Twitter users.

1. Real test cases: Why active screening can improve30%

A cross-border e-commerce team is doingWhen screening Twitter marketing customers, two rounds of controlled tests were conducted.

In the first round, the original list is used, and only basic status filtering is done.

In the second round, additionalTwitter active user screening dimensions include recent tweet frequency, interaction behavior and online activity cycle.

The test period is two weeks, and the results are compared as follows:

lPrivate message reply rate increased by approx.30%

lThe click-through rate of promotional links increased significantly

lIncreased number of effective customer service communications

lThe number of event participants is more concentrated

The key change is that the proportion of low-active accounts in the second-round list has dropped significantly.

This shows thatIf the active dimension is added to Twitter user data screening, the conversion efficiency will be structurally improved, not just a small fluctuation.

2. Core judgment criteria for active users

Filtering Twitter's active users is not simply about whether a tweet has been posted recently, but a comprehensive judgment of multiple indicators.

First, tweet frequency.

Accounts that continuously output content are usually more likely to see private messages or marketing messages.

Second, interaction density.

Users who frequently like, comment or retweet are more likely to respond.

Third, the recent active cycle.

For exampleWhether there is active behavior within 7 days has far more marketing value than being active for 30 days.

Fourth, account stability.

Accounts with long-term stable operation have lower risks and are more suitable for long-term private domain accumulation.

After superimposing these dimensions, the filteredTwitter’s high-quality account screening results are more commercially meaningful.

3. Why simply registering for testing is no longer enough

Many teams are doing itWhen checking Twitter's online account, it stays at the level of "whether private messages can be sent normally." However, the registration status can only prove that the account exists, but does not prove that the user will respond.

When the marketing list contains a large number of low-active accounts, promotion costs will be spread out. Customer service sends a lot of private messages but gets no effective interaction.The core of Twitter's accurate fan acquisition is not to expand the size of the list, but to increase the density of list activity.

4. How to establish a replicable screening process

in order to letTwitter active user screening is sustainable and the following process can be established:

The first step is to filter basic account status.

Eliminate obviously abnormal or invalid accounts.

The second step is identification of active cycles.

combineTwitter online account detection or recent interaction behavior screening.

The third step is interactive hierarchical management.

Distinguish highly active accounts from ordinary accounts and develop different marketing strategies.

When the data scale is large, manual operations are difficult to maintain efficiency. At this time, you can use a global network such as Digital Planet to244 national social AI number screening platforms use algorithms to identify account activity status and behavior trajectories to achieve batch screening.

Digital Planet supports multi-country data screening and is suitable for cross-borderTwitter marketing scenarios and provide free trials, you can test with small batches of data first.

5. Filter logical pairsThe long-term impact of ROI

In long-term operation,The value of Twitter’s active user screening will gradually increase.

When marketing targets are more precise:

lResponse rate is more stable

lCustomer service is more efficient

lIncreased activity participation

lReduce promotion costs

Such changes are not accidental, but the inevitable result of data structure optimization. if yourTwitter marketing customer screening is still at the basic detection level. You can consider upgrading the screening logic and adding active dimensions to test the effect.

digital planet is a world-leading number screening platform that combines Global mobile phone number segment selection, number generation, deduplication, comparison and other functions . It supports customers worldwideBatch numbers for 236 countriesScreening and testing services , currently supports40+ social and apps like:

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The platform has several features including Open filtering, active filtering, interactive filtering, gender filtering, avatar filtering, age filtering, online filtering, precise filtering, duration filtering, power-on filtering, empty number filtering, mobile phone device filtering wait.

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Its advantage lies in integrating major social networking and applications around the world, providing one-stop, real-time and efficient number screening services to help you achieve global digital development.

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