Whether the Amazon number is real or fake, it is best to check it before starting to use the customer list

DoWhen Amazon-related customers are developing, it is not uncommon to have thousands, tens of thousands or even more numbers in their hands. The real trouble is whether these numbers can continue to be used. Some come from historical orders, some come from after-sales records, some inquire through the official website, and some are old customers who stayed many years ago.

Once data is mixed together, it's easy for sales to waste time on duplicate numbers, formatting errors, and historical records that have lost business value. What the Amazon number authenticity test should really solve is not to prove that there must be an Amazon buyer behind a certain number, but to first check whether the existing customer numbers themselves are complete, repeated, and have clear sources, and whether they can enter the normal customer follow-up process.

The first item is to check whether the number format is correct.

a batchAfter getting the Amazon customer number, the first thing you should check is not whether the customer is worth developing, but whether there are any obvious problems with the number format.

Numbers are written differently in different countries.

Some save the complete international dial code, some only have local numbers, some have spaces, brackets or dashes, and some country fields and number country codes do not match.

For example, if the customer information is written in the United Kingdom, but the mobile phone number corresponds to another country, this type of data needs to be rechecked first.

If you haven’t even sorted out the number format, you’ll do it laterIt’s easy to create more confusion when filtering through WhatsApp, Telegram or other social platforms.

Therefore, you can do basic checks in the first round:

Whether the country code is complete;

Is the length of the mobile phone number reasonable?

Are there any extra characters in the number?

Does the country field and number match?

After this layer is completed, subsequent detection will be much simpler.

The second item is to check if there are duplicate numbers.

Amazon business data is easily duplicated.

The same customer may first place an order, then contact after-sales, and then inquire through the official website a few months later.

If the order system, customer service system and sales form keep a record respectively, the same mobile phone number may appear three times.

If all this data is handed over directly to sales, there will be a problem of customers being contacted repeatedly.

Therefore, before checking the authenticity of the number, it is best to remove duplicates first.

butAmazon customer data deduplication cannot be deleted directly just by looking at duplicate phone numbers.

For example, one record contains the order time, another record contains after-sales issues, and the third record contains products that customers have recently inquired about.

What should really be done is a merge.

Finally, a mobile phone number is retained, and orders, consultations, after-sales and recent interaction information are gathered into one customer profile.

In this way, the data is reduced, but the customer information is more complete.

The third item depends on where the number comes from.

Whether the Amazon number can continue to be used, the source is very important.

It’s also a US mobile phone number, probably from my ownAmazon order after-sales service may also come from the company's official website for price inquiry, or it may be a product registration form actively filled out by the user.

These sources make it easier to explain why customers appear in the database.

However, if the source of a batch of numbers cannot be clearly stated and only a list of mobile phone numbers is left, then even if the number format is detected to be normal later, it will be difficult to directly judge whether it is suitable for continued marketing.

soWhen detecting Amazon numbers, it is best to retain at least one source field.

for example:

Amazon order history

Amazon after-sales customers

Independent station inquiry

Product warranty registration

Event registration

The company's own old customer base

After the source of the number is clear, the salesperson will know what reason should be used to continue contacting the customer.

Just because the number is real, it doesn’t mean it’s accurate.Amazon customers

This isIt’s easy to get confused about Amazon number detection.

A mobile phone number has a normal format and can be processed normally. This only means that there are no obvious technical problems with this contact method.

It does not directly prove that this person is still around recentlyAmazon shopping.

It does not mean that he must be a high-spending user.

It is even more impossible to judge the specific goods purchased and the amount spent simply based on the mobile phone number.

Whether a customer is worth continuing to develop depends on the business record that the company has.

For example, you have recently placed an order, proactively inquired about products, submitted after-sales questions, and re-inquired. These are all more valuable than simple number status.

soAmazon number authenticity detection addresses data quality, while customer value judgment addresses sales priorities.

The two things are best kept separate.

Digital planet can be placed in the number inspection link

When the company already has a group of people with clear sourcesAmazon related customer numbers, you can first treat the numbers as a batch of data that need to be inspected.

First round of checking the format.

In the second round, the numbers will be removed.

In the third round, Digital Planet will be used to organize existing numbers and screen target social platforms.

For example, sales follow-up is mainly throughWhatsApp maintenance customers can further filter which numbers have been subscribed to WhatsApp.

If some customers are more suitableTelegram, you can also continue to filter Telegram related status.

This is the originalThe Amazon customer list can be further broken down into:

USAAmazon historical customer + WhatsApp has been activated

U.K.Amazon after-sales customer + WhatsApp has been activated

FranceAmazon inquiry customers + Telegram related users

German historical order customers + target communication channel to be confirmed

Digital Planet is responsible for the layer of existing numbers and communication platforms here, and is not responsible for determining whether a person is in the near future.Amazon shopping.

It will be clearer to use this way, and it will not mix the number detection andAmazon consumer behavior is mixed.

historyAmazon number needs to be checked separately

A batch of numbers left five years ago will definitely have different values ​​than the order customers placed just last week.

So in addition to format and platform status, you can also add a time field.

for example:

close30 days for orders or inquiries;

closeHave business records for 3 months;

There have been orders in the past year;

No interaction for a long time.

In this way, after the salesperson sees the list, he will know which ones should be processed first.

If a number format is normal,WhatsApp has also been activated, but it has not had any business records for three years, so it can be placed in the historical customer group.

Although the other number has just been added to the database, the customer just completed the inquiry yesterday, so it should be contacted first.

Number detection can only determine the level of contact information. Time and business behavior determine who to contact first.

WhichAmazon numbers are suitable for priority retention

If you want to sort out a large amount of data quickly, you can prioritize keeping a few categories.

The first category is customers with recent order records.

The second category is customers who have recently taken the initiative to inquire about prices, products, logistics or after-sales.

The third category includes customers who have purchased products in the past and have recently made new business moves.

The fourth category is old customers with clear sources and normal number status, but have no new needs for the time being.

As for data with unclear sources, incomplete information, and serious duplication, it can be placed in the area to be checked first.

This way the sales list won't be too complicated.

Do not use the after-sales number directly as a marketing list

Part of the Amazon customer number comes from orders and after-sales.

This type of number was originally reserved for completing transactions, logistics or after-sales services.

If you want to continue marketing later, it is best to combine the original business relationship and user selection to deal with it, rather than directly stuffing all after-sales numbers into the new promotion list.

For example, if a customer just reports a product problem, the most important thing is to solve the after-sales problem first.

If the problem is solved and the product itself has a reasonable repurchase cycle, follow-up maintenance will be arranged based on business conditions.

This is much more natural than launching new products as soon as complaints are resolved.

Just because the number can be contacted does not mean that it is suitable for marketing at any time.

tens of thousandsAmazon numbers can be processed in three batches

If the amount of data is large, there is no need to manually read each item.

It can be divided into three categories in batches first.

The first category is processing that can continue.

The number format is complete, the source is clear, there are no duplicate records, and there are certain business relationships recently.

The second category is to be confirmed.

The format is basically normal, but the source is missing, the time is longer, or the customer information is incomplete.

The third category is not entering sales for the time being.

Obvious duplication, formatting errors, the customer has explicitly declined further contact, or data from essential business sources is missing.

This way the salesperson will not get an original form that has not been sorted out at all.

What really requires manual judgment is the middle batch of data to be confirmed.

After the detection is completed, it is best to retain this information in the customer table

After sorting out the Amazon numbers, it is best not to just keep the mobile phone number and test results.

More practical customer information can include:

nation

Complete mobile number

Customer source

Last order or consultation time

Follow products

Target communication platform

Current sales status

For example, a piece of data can be organized into:

American customers

source:Amazon order history

recentRe-inquiry within 30 days

WhatsApp has been activated

focus onA product

Awaiting sales follow-up

Another one:

UK customers

Source: Historical After-Sales

No interaction for a year

Telegram related status has been organized

low priority

When sales get such data, they basically don’t need to go through the original records again.

Amazon number authenticity detection, the focus is not on proving identity

When many people see the authenticity test, they will subconsciously interpret it as judging whether there is a real person behind the number.Amazon buyer.

In fact, during normal data sorting, what should be more concerned about is whether there are obvious problems with the number itself, whether the source is clear, whether there are duplicates, and whether there is a basis for subsequent communication.

If you need to determine whether the customer is a recentAmazon's active users should still look at the company's own order, inquiry, after-sales and customer records.

If you need to determine whether the number is suitableCommunicate on WhatsApp or Telegram, and then enter the number screening process such as Digital Planet.

After breaking down each problem, the entire process becomes simpler.

Before you start using your Amazon number, do a product inspection: check the format, check the duplicates, check the source, and screen the communication channels again. Find out first whether the number can continue to be used, so that salespeople don’t have to hold tens of thousands of pieces of raw data and try them one by one.

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