There are too many mobile phone numbers on LinkedIn. How can I clean out the ones that are still usable?

Anyone who does LinkedIn customer development must have had this feeling: When I first started accumulating customer information, I felt that the more numbers I had, the better.

Hundreds of mobile phone numbers seem rich, and thousands of numbers feel like more resources. But when we actually started contacting each other, we discovered that the situation was not as ideal as imagined.

Some numbers have been replaced, some numbers have not been answered for a long time, and some numbers have been repeated several times. On the surface, it looks like a piece of thousands of pieces of data, but only a part of it may actually be useful.

There was one who worked overseas beforeThe B2B business team has compiled a batch of LinkedIn customer information, including more than 20,000 mobile phone numbers. When they first received the data, the team felt that this batch of resources was of high value and was ready to assign it directly to sales staff for follow-up.

But within a week of actual implementation, problems arose.

Salesmen call a large number of numbers every day, and many prompts that they cannot be connected, and some of the people corresponding to the numbers are not target customers. Salespeople spend a lot of time screening, only to find that the proportion of truly effective contact methods is not as high as expected.

Later, they adjusted the method and first cleaned the mobile phone number data before entering the subsequent development process.

The results are obvious: Sales handles fewer invalid numbers every day, and the follow-up rhythm becomes more stable.

Why LinkedIn mobile numbers tend to get confusing

Sources of customer data on LinkedIn are complex.

Some are contacts accumulated by the team over a long period of time, some are from marketing activities, and some are information compiled through different channels.

After putting these data together, several questions can easily arise.

For example, the same person's contact information may be saved multiple times by different people.

One sales copy is saved, another sales copy is saved, and finally there are multiple duplicate records in the database.

Another situation is that the data time is too long.

The mobile phone number saved a few years ago may have been changed or no longer belongs to the original contact.

If these data are kept without any sorting, the quantity will appear to be increasing, but the actual usable value will decrease.

Cleaning mobile phone numbers is not simply deleting them, but reorganizing customer resources.

Many people heard"Cleaning numbers", the first reaction is to delete invalid data.

But the actual operation is not so simple.

Real data cleaning is more like reclassifying a bunch of confusing information.

for example:

Merge duplicate numbers;

Organizing data in inconsistent formats;

Abnormal information screening;

Long-term invalid data filtering.

After doing this, the data left behind will be clearer.

What salespeople get is no longer a messy list that needs to be re-judged, but data that is easier to use.

What does a real data collation process look like?

Let’s take the team above as an example.

They initially obtained more than 20,000 LinkedIn mobile phone numbers. They did not distribute them directly to sales, but sorted them out first.

The first step was to bring together the numbers collected from different channels.

Previously, this data was scattered across multipleSome of the Excel files are even stored in personal computers. After concentrating, you can find that there is a lot of duplicate content.

The second step is to organize the number information in a unified manner.

Different people record it in different ways. Some people include the country area code, and some people directly save the local number. The format is very confusing.

After unification, the efficiency of subsequent processing has been significantly improved.

The third step is to filter the data according to actual needs.

They focus on which numbers are more suitable for subsequent customer communication, rather than purely pursuing data quantity.

After sorting it out, the sales team found that although the final amount of data was reduced, ineffective communication every day was significantly reduced.

In the past, a salesperson might have spent a lot of time confirming whether the number was valid during the day, but now more time can be spent on actual customer communication.

The larger the amount of data, the more likely it is that manual cleaning will cause problems.

If there are only a few hundred numbers, manual inspection is still acceptable.

But when the data reaches tens of thousands or more, manual methods will become more and more difficult.

The reason is simple.

People can judge dozens or hundreds of pieces of information, but it is difficult to process a large amount of repetitive work with high accuracy for a long time.

Especially cleaning mobile phone numbers seems simple, but in fact it takes a lot of patience.

It's okay to check hundreds of numbers a day, but if you do it for weeks or even months, it's easy to miss something.

Therefore, many teams will later choose tool assistance and hand over the repetitive data sorting work to the system.

How Digital Planet helps LinkedIn organize mobile phone number data

When processing a large amount of overseas customer data, Digital Planet can help users organize, filter and classify number data, making the originally scattered information easier to manage.

For example, in the LinkedIn mobile phone number data scenario, users can use Digital Planet to assist in data sorting and unified management of numbers from different sources to reduce interference caused by repeated information.

Previously the sales team might have spent a lot of time checking"Is this number useful?" After sorting it out, you can focus more on truly valuable customer communications.

In addition to LinkedIn-related data, Digital Planet can also be applied to data management scenarios on different platforms, such as social account data sorting, user information classification, and multi-source data integration.

To put it simply, it not only solves the number problem, but also helps users establish a more efficient way of processing data.

After cleaning, what changes will happen to the customer development effect?

Many people are worried about a problem:

If the number is reduced, will it affect the development effect?

In fact, oftentimes it's just the opposite.

There used to be a list of 10,000 numbers, but it might contain a lot of duplicate and invalid information.

After sorting, although the quantity decreased, the proportion of effective information increased.

For sales, the most important thing is not how many numbers you call every day, but how many truly valuable customers you can contact every day.

One sale per day100 valid numbers may be more effective than calling 500 confusing numbers.

What situations are suitable for LinkedIn mobile phone number cleaning?

If the following situations occur, it means that the data may need to be sorted:

The customer list takes a long time to accumulate;

Multiple teams work together to maintain data;

The source of mobile phone numbers is more complicated;

There is a large amount of sales feedback that cannot be contacted.

These situations are not isolated phenomena and will be encountered by many teams doing overseas market development.

The more data accumulates, the more it needs regular maintenance.

Good data is not about having more data, but about being more accurate.

Mobile phone number data management is actually very similar to organizing a warehouse.

The more stuff there is in the warehouse, it doesn’t mean it’s more valuable. If there are a lot of broken products and duplicate inventory, it will affect the search for what you really need.

The same goes for LinkedIn mobile number cleaning.

What is truly valuable is data that is organized and easy to use.

When data quality improves, customer development efficiency, sales time utilization, and overall operational rhythm will change.

Therefore, cleaning mobile phone numbers does not reduce resources, but helps resources play a greater role.

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