Screen out effective users from 10,000 clues, Facebook data processing case

Many teams doWhen Facebook attracts traffic, it is not difficult to grow leads on the front end. What is really difficult is that it becomes increasingly difficult to follow up on the back end. The number of forms, private messages, and advertising interactions are all increasing, but the number of people who can truly engage in effective communication has not increased at the same time.

This situation will become more obvious when the amount of data reaches thousands or tens of thousands. becauseFacebook clues themselves will be mixed with a lot of low-quality data. If it is not handled in advance, customer service, private chat and private domain operations will become increasingly difficult.

In this caseOf the 10,000 Facebook leads, the number of users who were actually suitable to continue operating in the end was not as high as initially imagined. But after data processing, the overall efficiency has changed significantly.

In the original clue stage, where are the problems mainly concentrated?

This batch of data comes from multiple entrances:

lFacebook ad form

lMessenger private chat

lEvent registration data

lSocial media interactive traffic diversion

It seemed like there was a lot of clues at first, but after the system was actually introduced, several problems quickly emerged.

The proportion of empty and unavailable numbers is relatively high

Some contact information is no longer accessible.

More and more duplicate data

The same user repeatedly enters the system, causing statistical distortion.

A high proportion of long-term silent users

Although many users submitted information, there was little subsequent interaction.

Abnormal accounts are obviously mixed in

The behavior of some accounts is unreal and their subsequent operational value is very low.

When these problems are added up, customer service will spend a lot of time dealing with invalid leads.

WhyOut of 10,000 clues, less than half are effective users.

Many people mistakenly think that:

As long as you get the contact information, it will be considered a valid clue.

But in fact, users with real operational value usually need to meet several conditions:

lCan be reached normally

lStay active for a long time

lHave real usage behavior

lThere is the possibility of continued communication in the future

andFacebook's traffic itself will naturally bring a large amount of general traffic.

Especially in low-threshold advertising and event scenarios, many users only interact for a short period of time and are not suitable for long-term operations.

What actions were taken during the data processing stage?

This time the processing is not so complicated that it relies entirely on manual labor. Instead, the data process is first dismantled.

Step one: Unify the data structure

Complete first:

lUniform format

lDuplicate data cleaning

lTag classification

Avoid further confusion later.

Step 2: Basic usability testing

confirm:

lIs the contact information authentic?

lCan it still be reached normally?

lWhether there is an abnormal state

This layer will directly affect subsequent sending efficiency.

Step Three: Active Screening

Separate long-term active users from low-active users.

Because they are both accessible, the subsequent value difference will be very large.

Step 4: Re-layer

according to:

lactivity level

luser behavior

lSource channel

Reprioritize operations.

After processing, although the data is reduced, the proportion of effective users is significantly increased.

After data processing, what is the biggest change in the backend?

The most obvious change is not the number of clues, but the back-end rhythm.

For example:

lCustomer service no longer faces a large number of invalid users

lPrivate chat advances faster

lResponse rate significantly improved

lHigh-value users are easier to identify

Many teams will find that when the data becomes clean, the difficulty of subsequent operations will be significantly reduced.

WhyFacebook operations increasingly rely on front-end data processing

In the past, many teams focused more on advertising, but now they pay more and more attention to data quality.

Because even if advertising continues to expand, if the back-end data gets worse and worse:

lCustomer service will become more and more tired

lConversion rates will get lower and lower

lThe private domain structure will become increasingly chaotic

So now many teams have started:

Filter first

Re-operate

Continuously update data at the end

Instead of importing all clues into the system uniformly.

Before official operation, it is more stable to complete the number detection first

If the data directly enters the group sending and private chat processes, repeated problems will continue to occur later.

In actual operation, you can first use Digital Planet to perform screen number detection to filter out unavailable numbers and abnormal data in advance. Digital Planet supports free trial screening test.

This allowsFacebook clues must first complete basic cleaning before entering the private domain.

Why effective user ratio is more important than lead volume

Many teams will continue to expand their advertising budgets in the hope of generating more leads.

But if:

lThe proportion of empty numbers is getting higher and higher

lThere are more and more low active users

lAbnormal accounts continue to increase

No matter how many clues there are, it will be difficult to form a stable conversion.

What really determines the operational effectiveness is the proportion of effective users.

Facebook's data processing is becoming more like a long-term operational project

In the past, many people understood data cleaning as a one-time action, but now it is becoming more and more like a long-term process.

because:

lUser status will change

lData will continue to age

lActivity will continue to fluctuate

If it is not continuously updated, the originally valid data will gradually become invalid.

What really matters is not how much is filtered out, but who is left behind

The core of data processing is not to simply reduce the amount of data, but to retain the people who are truly worthy of continuing operations.

same10,000 clues:

Some teams end up with a lot of invalid traffic.

Some teams are able to screen out stable long-term users.

The difference is often not in the traffic entrance, but in the front-end data processing logic.

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