When growing Japanese users, which numbers should be excluded from the beginning?

Anyone who has actually worked in the Japanese market knows that even though some numbers are technically"Usable", but the actual value is very low. After growing for a long time, you will find a pattern: instead of constantly expanding the data, it is better to exclude obviously low-value numbers from the beginning. The purpose of screening is not to pursue quantity, but to improve follow-up efficiency.

In the scenario of Japanese user growth, the following types of numbers should actually be focused on in the first round of screening, rather than being left to be consumed later.

Numbers that have no signs of activity for a long time

Just because the number is online does not mean that someone is using it. Numbers that have no signs of activity for a long time tend to have a low reach rate. Even if the text message is sent successfully, no one may view it.

Judgment criteria may include:

l No activity detected for a long time

l Historical contact no response record

l Tried multiple times with no feedback

Such numbers, even if they are not completely invalid, are considered low-priority data. It is recommended to manage them in separate groups rather than directly investing in core delivery batches.

Obviously repeated collection of numbers

Duplicate numbers not only waste costs but may also create the risk of over-reaching. If a number comes from multiple collection channels and is combined without unified deduplication, the operating frequency will be superimposed when it is reached.

Common sources of duplicate data include historical data merging, cross-channel imports, and old data not being cleaned. It is recommended to unify the format before batch processing, and then deduplicate primary keys.

In large-scale situations, Digital Planet can be used for batch deduplication and status detection to improve screening efficiency and avoid wasting resources through repeated detection.

Abnormal number segment or non-standard number format

Japanese numbers usually have a clear structure. If there is a number segment or abnormal data that does not comply with the rules, it can basically be eliminated directly. Mesily formatted data often comes from low-quality acquisition sources.

When screening, format standardization can be done first, and then batch screening can be carried out. The earlier obvious abnormal data is handled, the easier subsequent operations will be.

High complaint or high unsubscribe record number

During the growth process, although some numbers were effective, the historical contact feedback was obviously negative, such as multiple unsubscriptions or complaints. If such numbers continue to be used, risks will gradually accumulate.

It is recommended to file this type of number separately and not participate in subsequent frequent contacts. The more data the better, the more important the structure is.

Historical numbers that have not been updated for a long time

Japan number status changes over time. If the data source is too old, the proportion of empty accounts and outages will increase significantly. Using old data without updating will lower the overall success rate.

It is recommended to establish a regular detection mechanism and review the status of historical data. Digital Planet can assist in batch network status identification and help filter out obviously invalid numbers.

don't put"Usable" should be regarded as "worth using"

In the growth of Japanese users, the criterion for judging the value of data is not only whether it can be reached, but whether it is worth the investment cost. Prioritize the use of active, stable, and low-risk numbers, and the effect will be more stable.

Screening isn’t about cutting down on size, it’s about improving quality. Instead of constantly making amends later, it is better to exclude obviously low-value numbers in the early stage.

When the data structure is clean enough, the growth action will be more rhythmic and the test results will be more realistic. Really stable growth is not based on massive data, but on controllable, high-quality data. Eliminate invalid and low-value numbers in advance, and every subsequent step will be easier.

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