Instagram number data cleaning case: How to increase the proportion of effective users with 100,000 pieces of data

A cross-border e-commerce team is preparing toWhen Instagram users were conducting a new round of marketing, they found that the 100,000 number data accumulated in the background did not bring the expected results. The advertising reach rate is low, the private message reply rate has dropped, and customer conversion has never been significantly improved.

After a complete data cleaning, although the number of operable numbers has been reduced, the proportion of effective users has increased significantly, and subsequent marketing efficiency has become more stable. This case illustrates that data quality often deserves more attention than data size.

one servingSeveral problems exposed by 100,000 number data

Before the project started, the team first conducted statistics on the original numbers and found that there were multiple problems with the data.

Mainly include:

lThere are many duplicate numbers

lThe proportion of accounts that have not been used for a long time is relatively high

lSome numbers do not correspondInstagram account

lData sources are complex and the format is not uniform

lMultiple records exist for the same user

If it is used directly for marketing, it will not only waste the budget, but also affect the data analysis results.

Where does data cleaning start?

In order to ensure subsequent operational effects, the entire cleaning work was not completed at one time, but was carried out in stages.

The first step is to unify the number format.

Unify and standardize different country area codes and input formats to facilitate subsequent batch processing.

The second step is to clean up duplicate data.

For duplicate numbers, only the latest or higher quality record is retained.

The third step is to check the account status.

Determine whether the number has been registeredInstagram, and whether the account remains in normal use.

The fourth step is to create user tags.

Based on the test results, users are divided into different levels to facilitate subsequent precision marketing.

What changes happened after cleaning?

After completing the data sorting, the database structure has improved significantly.

Major changes include:

lThe proportion of invalid numbers dropped significantly

lDuplicate data is handled uniformly

lIncreased proportion of active accounts

lUser labels are clearer

lSubsequent marketing data is more accurate

Although the final number of operational numbers is less than the original data, the overall data quality has been improved.

Why do many companies’ data accumulate and become more chaotic?

Number data is increasing every day, but many companies lack an ongoing maintenance mechanism.

Common reasons include:

lRepeatedly import data from multiple platforms

lHistorical customers have not been updated for a long time

lAdvertising customer acquisition data continues to accumulate

lDifferent departments manage data separately

The longer the time, the more invalid information in the database.

Therefore, data cleaning is not a one-time job but should become part of daily operations.

What else can you do after data cleaning is completed?

After the basic cleaning of numbers is completed, further data analysis can be performed.

include:

lUser region classification

lActivity level stratification

lSocial account status recognition

lHigh value customer screening

lAdvertising audience creation

After reorganization, the number data can support more precise marketing strategies.

How Digital Planet HelpsInstagram number data cleaning

During this data compilation process, the team used Digital Planet as the core data processing platform and conducted a systematic analysis of the numbers.

The platform first completes number standardization processing, automatically unifies international area codes, corrects format abnormalities, and removes duplicate data.

then enterIn the Instagram number detection stage, multi-dimensional analysis of the data is performed:

lDetermine whether the number has been registeredInstagram

lCheck whether the account remains in normal use

lMark long-term silent accounts

lIdentify sources of abnormal data

lAutomatically create user tags

In addition, Digital Planet also supports cross-analysis of data on multiple platforms, includingWhatsApp, Facebook, Telegram and TikTok account status detection allows companies to have a more comprehensive understanding of customer profiles.

After processing, the system automatically outputs multiple data categories such as highly active users, ordinary users, users to be activated, and invalid numbers. Enterprises can directly use it for advertising, private domain operations, and customer management without manual sorting again.

Data cleaning is not about deleting data, but about increasing the value of data

Many teams think that data cleaning means constantly deleting numbers. In fact, the real purpose is to make every piece of retained data have higher operational value.

When the database is cleaner and user labels are more accurate, whether it is advertising delivery, customer contact or subsequent data analysis, it can be based on more reliable data. This is why more and more companies areThe important reason why Instagram number data cleaning should be put before marketing.

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